// SPDX-License-Identifier: GPL-3.0-or-later #include "query.h" #include "web/api/formatters/rrd2json.h" #include "rrdr.h" #include "average/average.h" #include "countif/countif.h" #include "incremental_sum/incremental_sum.h" #include "max/max.h" #include "median/median.h" #include "min/min.h" #include "sum/sum.h" #include "stddev/stddev.h" #include "ses/ses.h" #include "des/des.h" #include "percentile/percentile.h" #include "trimmed_mean/trimmed_mean.h" #define QUERY_PLAN_MIN_POINTS 10 #define POINTS_TO_EXPAND_QUERY 5 // ---------------------------------------------------------------------------- static struct { const char *name; uint32_t hash; RRDR_TIME_GROUPING value; RRDR_TIME_GROUPING add_flush; // One time initialization for the module. // This is called once, when netdata starts. void (*init)(void); // Allocate all required structures for a query. // This is called once for each netdata query. void (*create)(struct rrdresult *r, const char *options); // Cleanup collected values, but don't destroy the structures. // This is called when the query engine switches dimensions, // as part of the same query (so same chart, switching metric). void (*reset)(struct rrdresult *r); // Free all resources allocated for the query. void (*free)(struct rrdresult *r); // Add a single value into the calculation. // The module may decide to cache it, or use it in the fly. void (*add)(struct rrdresult *r, NETDATA_DOUBLE value); // Generate a single result for the values added so far. // More values and points may be requested later. // It is up to the module to reset its internal structures // when flushing it (so for a few modules it may be better to // continue after a flush as if nothing changed, for others a // cleanup of the internal structures may be required). NETDATA_DOUBLE (*flush)(struct rrdresult *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr); TIER_QUERY_FETCH tier_query_fetch; } api_v1_data_groups[] = { {.name = "average", .hash = 0, .value = RRDR_GROUPING_AVERAGE, .add_flush = RRDR_GROUPING_AVERAGE, .init = NULL, .create= tg_average_create, .reset = tg_average_reset, .free = tg_average_free, .add = tg_average_add, .flush = tg_average_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "avg", // alias on 'average' .hash = 0, .value = RRDR_GROUPING_AVERAGE, .add_flush = RRDR_GROUPING_AVERAGE, .init = NULL, .create= tg_average_create, .reset = tg_average_reset, .free = tg_average_free, .add = tg_average_add, .flush = tg_average_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "mean", // alias on 'average' .hash = 0, .value = RRDR_GROUPING_AVERAGE, .add_flush = RRDR_GROUPING_AVERAGE, .init = NULL, .create= tg_average_create, .reset = tg_average_reset, .free = tg_average_free, .add = tg_average_add, .flush = tg_average_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean1", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN1, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_1, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean2", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN2, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_2, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean3", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN3, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_3, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean5", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_5, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean10", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN10, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_10, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean15", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN15, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_15, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean20", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN20, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_20, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean25", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN25, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_25, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-mean", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEAN, .add_flush = RRDR_GROUPING_TRIMMED_MEAN, .init = NULL, .create= tg_trimmed_mean_create_5, .reset = tg_trimmed_mean_reset, .free = tg_trimmed_mean_free, .add = tg_trimmed_mean_add, .flush = tg_trimmed_mean_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "incremental_sum", .hash = 0, .value = RRDR_GROUPING_INCREMENTAL_SUM, .add_flush = RRDR_GROUPING_INCREMENTAL_SUM, .init = NULL, .create= tg_incremental_sum_create, .reset = tg_incremental_sum_reset, .free = tg_incremental_sum_free, .add = tg_incremental_sum_add, .flush = tg_incremental_sum_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "incremental-sum", .hash = 0, .value = RRDR_GROUPING_INCREMENTAL_SUM, .add_flush = RRDR_GROUPING_INCREMENTAL_SUM, .init = NULL, .create= tg_incremental_sum_create, .reset = tg_incremental_sum_reset, .free = tg_incremental_sum_free, .add = tg_incremental_sum_add, .flush = tg_incremental_sum_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "median", .hash = 0, .value = RRDR_GROUPING_MEDIAN, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median1", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN1, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_1, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median2", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN2, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_2, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median3", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN3, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_3, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median5", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN5, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_5, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median10", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN10, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_10, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median15", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN15, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_15, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median20", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN20, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_20, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median25", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN25, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_25, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "trimmed-median", .hash = 0, .value = RRDR_GROUPING_TRIMMED_MEDIAN5, .add_flush = RRDR_GROUPING_MEDIAN, .init = NULL, .create= tg_median_create_trimmed_5, .reset = tg_median_reset, .free = tg_median_free, .add = tg_median_add, .flush = tg_median_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile25", .hash = 0, .value = RRDR_GROUPING_PERCENTILE25, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_25, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile50", .hash = 0, .value = RRDR_GROUPING_PERCENTILE50, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_50, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile75", .hash = 0, .value = RRDR_GROUPING_PERCENTILE75, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_75, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile80", .hash = 0, .value = RRDR_GROUPING_PERCENTILE80, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_80, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile90", .hash = 0, .value = RRDR_GROUPING_PERCENTILE90, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_90, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile95", .hash = 0, .value = RRDR_GROUPING_PERCENTILE, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_95, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile97", .hash = 0, .value = RRDR_GROUPING_PERCENTILE97, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_97, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile98", .hash = 0, .value = RRDR_GROUPING_PERCENTILE98, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_98, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile99", .hash = 0, .value = RRDR_GROUPING_PERCENTILE99, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_99, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "percentile", .hash = 0, .value = RRDR_GROUPING_PERCENTILE, .add_flush = RRDR_GROUPING_PERCENTILE, .init = NULL, .create= tg_percentile_create_95, .reset = tg_percentile_reset, .free = tg_percentile_free, .add = tg_percentile_add, .flush = tg_percentile_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "min", .hash = 0, .value = RRDR_GROUPING_MIN, .add_flush = RRDR_GROUPING_MIN, .init = NULL, .create= tg_min_create, .reset = tg_min_reset, .free = tg_min_free, .add = tg_min_add, .flush = tg_min_flush, .tier_query_fetch = TIER_QUERY_FETCH_MIN }, {.name = "max", .hash = 0, .value = RRDR_GROUPING_MAX, .add_flush = RRDR_GROUPING_MAX, .init = NULL, .create= tg_max_create, .reset = tg_max_reset, .free = tg_max_free, .add = tg_max_add, .flush = tg_max_flush, .tier_query_fetch = TIER_QUERY_FETCH_MAX }, {.name = "sum", .hash = 0, .value = RRDR_GROUPING_SUM, .add_flush = RRDR_GROUPING_SUM, .init = NULL, .create= tg_sum_create, .reset = tg_sum_reset, .free = tg_sum_free, .add = tg_sum_add, .flush = tg_sum_flush, .tier_query_fetch = TIER_QUERY_FETCH_SUM }, // standard deviation {.name = "stddev", .hash = 0, .value = RRDR_GROUPING_STDDEV, .add_flush = RRDR_GROUPING_STDDEV, .init = NULL, .create= tg_stddev_create, .reset = tg_stddev_reset, .free = tg_stddev_free, .add = tg_stddev_add, .flush = tg_stddev_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "cv", // coefficient variation is calculated by stddev .hash = 0, .value = RRDR_GROUPING_CV, .add_flush = RRDR_GROUPING_CV, .init = NULL, .create= tg_stddev_create, // not an error, stddev calculates this too .reset = tg_stddev_reset, // not an error, stddev calculates this too .free = tg_stddev_free, // not an error, stddev calculates this too .add = tg_stddev_add, // not an error, stddev calculates this too .flush = tg_stddev_coefficient_of_variation_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "rsd", // alias of 'cv' .hash = 0, .value = RRDR_GROUPING_CV, .add_flush = RRDR_GROUPING_CV, .init = NULL, .create= tg_stddev_create, // not an error, stddev calculates this too .reset = tg_stddev_reset, // not an error, stddev calculates this too .free = tg_stddev_free, // not an error, stddev calculates this too .add = tg_stddev_add, // not an error, stddev calculates this too .flush = tg_stddev_coefficient_of_variation_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, // single exponential smoothing {.name = "ses", .hash = 0, .value = RRDR_GROUPING_SES, .add_flush = RRDR_GROUPING_SES, .init = tg_ses_init, .create= tg_ses_create, .reset = tg_ses_reset, .free = tg_ses_free, .add = tg_ses_add, .flush = tg_ses_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "ema", // alias for 'ses' .hash = 0, .value = RRDR_GROUPING_SES, .add_flush = RRDR_GROUPING_SES, .init = NULL, .create= tg_ses_create, .reset = tg_ses_reset, .free = tg_ses_free, .add = tg_ses_add, .flush = tg_ses_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "ewma", // alias for ses .hash = 0, .value = RRDR_GROUPING_SES, .add_flush = RRDR_GROUPING_SES, .init = NULL, .create= tg_ses_create, .reset = tg_ses_reset, .free = tg_ses_free, .add = tg_ses_add, .flush = tg_ses_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, // double exponential smoothing {.name = "des", .hash = 0, .value = RRDR_GROUPING_DES, .add_flush = RRDR_GROUPING_DES, .init = tg_des_init, .create= tg_des_create, .reset = tg_des_reset, .free = tg_des_free, .add = tg_des_add, .flush = tg_des_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, {.name = "countif", .hash = 0, .value = RRDR_GROUPING_COUNTIF, .add_flush = RRDR_GROUPING_COUNTIF, .init = NULL, .create= tg_countif_create, .reset = tg_countif_reset, .free = tg_countif_free, .add = tg_countif_add, .flush = tg_countif_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE }, // terminator {.name = NULL, .hash = 0, .value = RRDR_GROUPING_UNDEFINED, .add_flush = RRDR_GROUPING_AVERAGE, .init = NULL, .create= tg_average_create, .reset = tg_average_reset, .free = tg_average_free, .add = tg_average_add, .flush = tg_average_flush, .tier_query_fetch = TIER_QUERY_FETCH_AVERAGE } }; void time_grouping_init(void) { int i; for(i = 0; api_v1_data_groups[i].name ; i++) { api_v1_data_groups[i].hash = simple_hash(api_v1_data_groups[i].name); if(api_v1_data_groups[i].init) api_v1_data_groups[i].init(); } } const char *time_grouping_method2string(RRDR_TIME_GROUPING group) { int i; for(i = 0; api_v1_data_groups[i].name ; i++) { if(api_v1_data_groups[i].value == group) { return api_v1_data_groups[i].name; } } return "unknown-group-method"; } RRDR_TIME_GROUPING time_grouping_parse(const char *name, RRDR_TIME_GROUPING def) { int i; uint32_t hash = simple_hash(name); for(i = 0; api_v1_data_groups[i].name ; i++) if(unlikely(hash == api_v1_data_groups[i].hash && !strcmp(name, api_v1_data_groups[i].name))) return api_v1_data_groups[i].value; return def; } const char *time_grouping_tostring(RRDR_TIME_GROUPING group) { int i; for(i = 0; api_v1_data_groups[i].name ; i++) if(unlikely(group == api_v1_data_groups[i].value)) return api_v1_data_groups[i].name; return "unknown"; } static void rrdr_set_grouping_function(RRDR *r, RRDR_TIME_GROUPING group_method) { int i, found = 0; for(i = 0; !found && api_v1_data_groups[i].name ;i++) { if(api_v1_data_groups[i].value == group_method) { r->time_grouping.create = api_v1_data_groups[i].create; r->time_grouping.reset = api_v1_data_groups[i].reset; r->time_grouping.free = api_v1_data_groups[i].free; r->time_grouping.add = api_v1_data_groups[i].add; r->time_grouping.flush = api_v1_data_groups[i].flush; r->time_grouping.tier_query_fetch = api_v1_data_groups[i].tier_query_fetch; r->time_grouping.add_flush = api_v1_data_groups[i].add_flush; found = 1; } } if(!found) { errno = 0; internal_error(true, "QUERY: grouping method %u not found. Using 'average'", (unsigned int)group_method); r->time_grouping.create = tg_average_create; r->time_grouping.reset = tg_average_reset; r->time_grouping.free = tg_average_free; r->time_grouping.add = tg_average_add; r->time_grouping.flush = tg_average_flush; r->time_grouping.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE; r->time_grouping.add_flush = RRDR_GROUPING_AVERAGE; } } static inline void time_grouping_add(RRDR *r, NETDATA_DOUBLE value, const RRDR_TIME_GROUPING add_flush) { switch(add_flush) { case RRDR_GROUPING_AVERAGE: tg_average_add(r, value); break; case RRDR_GROUPING_MAX: tg_max_add(r, value); break; case RRDR_GROUPING_MIN: tg_min_add(r, value); break; case RRDR_GROUPING_MEDIAN: tg_median_add(r, value); break; case RRDR_GROUPING_STDDEV: case RRDR_GROUPING_CV: tg_stddev_add(r, value); break; case RRDR_GROUPING_SUM: tg_sum_add(r, value); break; case RRDR_GROUPING_COUNTIF: tg_countif_add(r, value); break; case RRDR_GROUPING_TRIMMED_MEAN: tg_trimmed_mean_add(r, value); break; case RRDR_GROUPING_PERCENTILE: tg_percentile_add(r, value); break; case RRDR_GROUPING_SES: tg_ses_add(r, value); break; case RRDR_GROUPING_DES: tg_des_add(r, value); break; case RRDR_GROUPING_INCREMENTAL_SUM: tg_incremental_sum_add(r, value); break; default: r->time_grouping.add(r, value); break; } } static inline NETDATA_DOUBLE time_grouping_flush(RRDR *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr, const RRDR_TIME_GROUPING add_flush) { switch(add_flush) { case RRDR_GROUPING_AVERAGE: return tg_average_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_MAX: return tg_max_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_MIN: return tg_min_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_MEDIAN: return tg_median_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_STDDEV: return tg_stddev_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_CV: return tg_stddev_coefficient_of_variation_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_SUM: return tg_sum_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_COUNTIF: return tg_countif_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_TRIMMED_MEAN: return tg_trimmed_mean_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_PERCENTILE: return tg_percentile_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_SES: return tg_ses_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_DES: return tg_des_flush(r, rrdr_value_options_ptr); case RRDR_GROUPING_INCREMENTAL_SUM: return tg_incremental_sum_flush(r, rrdr_value_options_ptr); default: return r->time_grouping.flush(r, rrdr_value_options_ptr); } } RRDR_GROUP_BY group_by_parse(char *s) { RRDR_GROUP_BY group_by = RRDR_GROUP_BY_NONE; while(s) { char *key = strsep_skip_consecutive_separators(&s, ",| "); if (!key || !*key) continue; if (strcmp(key, "selected") == 0) group_by |= RRDR_GROUP_BY_SELECTED; if (strcmp(key, "dimension") == 0) group_by |= RRDR_GROUP_BY_DIMENSION; if (strcmp(key, "instance") == 0) group_by |= RRDR_GROUP_BY_INSTANCE; if (strcmp(key, "percentage-of-instance") == 0) group_by |= RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE; if (strcmp(key, "label") == 0) group_by |= RRDR_GROUP_BY_LABEL; if (strcmp(key, "node") == 0) group_by |= RRDR_GROUP_BY_NODE; if (strcmp(key, "context") == 0) group_by |= RRDR_GROUP_BY_CONTEXT; if (strcmp(key, "units") == 0) group_by |= RRDR_GROUP_BY_UNITS; } if((group_by & RRDR_GROUP_BY_SELECTED) && (group_by & ~RRDR_GROUP_BY_SELECTED)) { internal_error(true, "group-by given by query has 'selected' together with more groupings"); group_by = RRDR_GROUP_BY_SELECTED; // remove all other groupings } if(group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE) group_by = RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE; // remove all other groupings return group_by; } void buffer_json_group_by_to_array(BUFFER *wb, RRDR_GROUP_BY group_by) { if(group_by == RRDR_GROUP_BY_NONE) buffer_json_add_array_item_string(wb, "none"); else { if (group_by & RRDR_GROUP_BY_DIMENSION) buffer_json_add_array_item_string(wb, "dimension"); if (group_by & RRDR_GROUP_BY_INSTANCE) buffer_json_add_array_item_string(wb, "instance"); if (group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE) buffer_json_add_array_item_string(wb, "percentage-of-instance"); if (group_by & RRDR_GROUP_BY_LABEL) buffer_json_add_array_item_string(wb, "label"); if (group_by & RRDR_GROUP_BY_NODE) buffer_json_add_array_item_string(wb, "node"); if (group_by & RRDR_GROUP_BY_CONTEXT) buffer_json_add_array_item_string(wb, "context"); if (group_by & RRDR_GROUP_BY_UNITS) buffer_json_add_array_item_string(wb, "units"); if (group_by & RRDR_GROUP_BY_SELECTED) buffer_json_add_array_item_string(wb, "selected"); } } RRDR_GROUP_BY_FUNCTION group_by_aggregate_function_parse(const char *s) { if(strcmp(s, "average") == 0) return RRDR_GROUP_BY_FUNCTION_AVERAGE; if(strcmp(s, "avg") == 0) return RRDR_GROUP_BY_FUNCTION_AVERAGE; if(strcmp(s, "min") == 0) return RRDR_GROUP_BY_FUNCTION_MIN; if(strcmp(s, "max") == 0) return RRDR_GROUP_BY_FUNCTION_MAX; if(strcmp(s, "sum") == 0) return RRDR_GROUP_BY_FUNCTION_SUM; if(strcmp(s, "percentage") == 0) return RRDR_GROUP_BY_FUNCTION_PERCENTAGE; return RRDR_GROUP_BY_FUNCTION_AVERAGE; } const char *group_by_aggregate_function_to_string(RRDR_GROUP_BY_FUNCTION group_by_function) { switch(group_by_function) { default: case RRDR_GROUP_BY_FUNCTION_AVERAGE: return "average"; case RRDR_GROUP_BY_FUNCTION_MIN: return "min"; case RRDR_GROUP_BY_FUNCTION_MAX: return "max"; case RRDR_GROUP_BY_FUNCTION_SUM: return "sum"; case RRDR_GROUP_BY_FUNCTION_PERCENTAGE: return "percentage"; } } // ---------------------------------------------------------------------------- // helpers to find our way in RRDR static inline RRDR_VALUE_FLAGS *UNUSED_FUNCTION(rrdr_line_options)(RRDR *r, long rrdr_line) { return &r->o[ rrdr_line * r->d ]; } static inline NETDATA_DOUBLE *UNUSED_FUNCTION(rrdr_line_values)(RRDR *r, long rrdr_line) { return &r->v[ rrdr_line * r->d ]; } static inline long rrdr_line_init(RRDR *r __maybe_unused, time_t t __maybe_unused, long rrdr_line) { rrdr_line++; internal_fatal(rrdr_line >= (long)r->n, "QUERY: requested to step above RRDR size for query '%s'", r->internal.qt->id); internal_fatal(r->t[rrdr_line] != t, "QUERY: wrong timestamp at RRDR line %ld, expected %ld, got %ld, of query '%s'", rrdr_line, r->t[rrdr_line], t, r->internal.qt->id); return rrdr_line; } // ---------------------------------------------------------------------------- // tier management static bool query_metric_is_valid_tier(QUERY_METRIC *qm, size_t tier) { if(!qm->tiers[tier].db_metric_handle || !qm->tiers[tier].db_first_time_s || !qm->tiers[tier].db_last_time_s || !qm->tiers[tier].db_update_every_s) return false; return true; } static size_t query_metric_first_working_tier(QUERY_METRIC *qm) { for(size_t tier = 0; tier < storage_tiers ; tier++) { // find the db time-range for this tier for all metrics STORAGE_METRIC_HANDLE *db_metric_handle = qm->tiers[tier].db_metric_handle; time_t first_time_s = qm->tiers[tier].db_first_time_s; time_t last_time_s = qm->tiers[tier].db_last_time_s; time_t update_every_s = qm->tiers[tier].db_update_every_s; if(!db_metric_handle || !first_time_s || !last_time_s || !update_every_s) continue; return tier; } return 0; } static long query_plan_points_coverage_weight(time_t db_first_time_s, time_t db_last_time_s, time_t db_update_every_s, time_t after_wanted, time_t before_wanted, size_t points_wanted, size_t tier __maybe_unused) { if(db_first_time_s == 0 || db_last_time_s == 0 || db_update_every_s == 0 || db_first_time_s > before_wanted || db_last_time_s < after_wanted) return -LONG_MAX; long long common_first_t = MAX(db_first_time_s, after_wanted); long long common_last_t = MIN(db_last_time_s, before_wanted); long long time_coverage = (common_last_t - common_first_t) * 1000000LL / (before_wanted - after_wanted); long long points_wanted_in_coverage = (long long)points_wanted * time_coverage / 1000000LL; long long points_available = (common_last_t - common_first_t) / db_update_every_s; long long points_delta = (long)(points_available - points_wanted_in_coverage); long long points_coverage = (points_delta < 0) ? (long)(points_available * time_coverage / points_wanted_in_coverage) : time_coverage; // a way to benefit higher tiers // points_coverage += (long)tier * 10000; if(points_available <= 0) return -LONG_MAX; return (long)(points_coverage + (25000LL * tier)); // 2.5% benefit for each higher tier } static size_t query_metric_best_tier_for_timeframe(QUERY_METRIC *qm, time_t after_wanted, time_t before_wanted, size_t points_wanted) { if(unlikely(storage_tiers < 2)) return 0; if(unlikely(after_wanted == before_wanted || points_wanted <= 0)) return query_metric_first_working_tier(qm); if(points_wanted < QUERY_PLAN_MIN_POINTS) // when selecting tiers, aim for a resolution of at least QUERY_PLAN_MIN_POINTS points points_wanted = (before_wanted - after_wanted) > QUERY_PLAN_MIN_POINTS ? QUERY_PLAN_MIN_POINTS : before_wanted - after_wanted; time_t min_first_time_s = 0; time_t max_last_time_s = 0; for(size_t tier = 0; tier < storage_tiers ; tier++) { time_t first_time_s = qm->tiers[tier].db_first_time_s; time_t last_time_s = qm->tiers[tier].db_last_time_s; if(!min_first_time_s || (first_time_s && first_time_s < min_first_time_s)) min_first_time_s = first_time_s; if(!max_last_time_s || (last_time_s && last_time_s > max_last_time_s)) max_last_time_s = last_time_s; } for(size_t tier = 0; tier < storage_tiers ; tier++) { // find the db time-range for this tier for all metrics STORAGE_METRIC_HANDLE *db_metric_handle = qm->tiers[tier].db_metric_handle; time_t first_time_s = qm->tiers[tier].db_first_time_s; time_t last_time_s = qm->tiers[tier].db_last_time_s; time_t update_every_s = qm->tiers[tier].db_update_every_s; if( !db_metric_handle || !first_time_s || !last_time_s || !update_every_s || first_time_s > before_wanted || last_time_s < after_wanted ) { qm->tiers[tier].weight = -LONG_MAX; continue; } internal_fatal(first_time_s > before_wanted || last_time_s < after_wanted, "QUERY: invalid db durations"); qm->tiers[tier].weight = query_plan_points_coverage_weight( min_first_time_s, max_last_time_s, update_every_s, after_wanted, before_wanted, points_wanted, tier); } size_t best_tier = 0; for(size_t tier = 1; tier < storage_tiers ; tier++) { if(qm->tiers[tier].weight >= qm->tiers[best_tier].weight) best_tier = tier; } return best_tier; } static size_t rrddim_find_best_tier_for_timeframe(QUERY_TARGET *qt, time_t after_wanted, time_t before_wanted, size_t points_wanted) { if(unlikely(storage_tiers < 2)) return 0; if(unlikely(after_wanted == before_wanted || points_wanted <= 0)) { internal_error(true, "QUERY: '%s' has invalid params to tier calculation", qt->id); return 0; } long weight[storage_tiers]; for(size_t tier = 0; tier < storage_tiers ; tier++) { time_t common_first_time_s = 0; time_t common_last_time_s = 0; time_t common_update_every_s = 0; // find the db time-range for this tier for all metrics for(size_t i = 0, used = qt->query.used; i < used ; i++) { QUERY_METRIC *qm = query_metric(qt, i); time_t first_time_s = qm->tiers[tier].db_first_time_s; time_t last_time_s = qm->tiers[tier].db_last_time_s; time_t update_every_s = qm->tiers[tier].db_update_every_s; if(!first_time_s || !last_time_s || !update_every_s) continue; if(!common_first_time_s) common_first_time_s = first_time_s; else common_first_time_s = MIN(first_time_s, common_first_time_s); if(!common_last_time_s) common_last_time_s = last_time_s; else common_last_time_s = MAX(last_time_s, common_last_time_s); if(!common_update_every_s) common_update_every_s = update_every_s; else common_update_every_s = MIN(update_every_s, common_update_every_s); } weight[tier] = query_plan_points_coverage_weight(common_first_time_s, common_last_time_s, common_update_every_s, after_wanted, before_wanted, points_wanted, tier); } size_t best_tier = 0; for(size_t tier = 1; tier < storage_tiers ; tier++) { if(weight[tier] >= weight[best_tier]) best_tier = tier; } if(weight[best_tier] == -LONG_MAX) best_tier = 0; return best_tier; } static time_t rrdset_find_natural_update_every_for_timeframe(QUERY_TARGET *qt, time_t after_wanted, time_t before_wanted, size_t points_wanted, RRDR_OPTIONS options, size_t tier) { size_t best_tier; if((options & RRDR_OPTION_SELECTED_TIER) && tier < storage_tiers) best_tier = tier; else best_tier = rrddim_find_best_tier_for_timeframe(qt, after_wanted, before_wanted, points_wanted); // find the db minimum update every for this tier for all metrics time_t common_update_every_s = default_rrd_update_every; for(size_t i = 0, used = qt->query.used; i < used ; i++) { QUERY_METRIC *qm = query_metric(qt, i); time_t update_every_s = qm->tiers[best_tier].db_update_every_s; if(!i) common_update_every_s = update_every_s; else common_update_every_s = MIN(update_every_s, common_update_every_s); } return common_update_every_s; } // ---------------------------------------------------------------------------- // query ops typedef struct query_point { STORAGE_POINT sp; NETDATA_DOUBLE value; bool added; #ifdef NETDATA_INTERNAL_CHECKS size_t id; #endif } QUERY_POINT; QUERY_POINT QUERY_POINT_EMPTY = { .sp = STORAGE_POINT_UNSET, .value = NAN, .added = false, #ifdef NETDATA_INTERNAL_CHECKS .id = 0, #endif }; #ifdef NETDATA_INTERNAL_CHECKS #define query_point_set_id(point, point_id) (point).id = point_id #else #define query_point_set_id(point, point_id) debug_dummy() #endif typedef struct query_engine_ops { // configuration RRDR *r; QUERY_METRIC *qm; time_t view_update_every; time_t query_granularity; TIER_QUERY_FETCH tier_query_fetch; // query planer size_t current_plan; time_t current_plan_expire_time; time_t plan_expanded_after; time_t plan_expanded_before; // storage queries size_t tier; struct query_metric_tier *tier_ptr; struct storage_engine_query_handle *handle; // aggregating points over time size_t group_points_non_zero; size_t group_points_added; STORAGE_POINT group_point; // aggregates min, max, sum, count, anomaly count for each group point STORAGE_POINT query_point; // aggregates min, max, sum, count, anomaly count across the whole query RRDR_VALUE_FLAGS group_value_flags; // statistics size_t db_total_points_read; size_t db_points_read_per_tier[RRD_STORAGE_TIERS]; struct { time_t expanded_after; time_t expanded_before; struct storage_engine_query_handle handle; bool initialized; bool finalized; } plans[QUERY_PLANS_MAX]; struct query_engine_ops *next; } QUERY_ENGINE_OPS; // ---------------------------------------------------------------------------- // query planer #define query_plan_should_switch_plan(ops, now) ((now) >= (ops)->current_plan_expire_time) static size_t query_planer_expand_duration_in_points(time_t this_update_every, time_t next_update_every) { time_t delta = this_update_every - next_update_every; if(delta < 0) delta = -delta; size_t points; if(delta < this_update_every * POINTS_TO_EXPAND_QUERY) points = POINTS_TO_EXPAND_QUERY; else points = (delta + this_update_every - 1) / this_update_every; return points; } static void query_planer_initialize_plans(QUERY_ENGINE_OPS *ops) { QUERY_METRIC *qm = ops->qm; for(size_t p = 0; p < qm->plan.used ; p++) { size_t tier = qm->plan.array[p].tier; time_t update_every = qm->tiers[tier].db_update_every_s; size_t points_to_add_to_after; if(p > 0) { // there is another plan before to this size_t tier0 = qm->plan.array[p - 1].tier; time_t update_every0 = qm->tiers[tier0].db_update_every_s; points_to_add_to_after = query_planer_expand_duration_in_points(update_every, update_every0); } else points_to_add_to_after = (tier == 0) ? 0 : POINTS_TO_EXPAND_QUERY; size_t points_to_add_to_before; if(p + 1 < qm->plan.used) { // there is another plan after to this size_t tier1 = qm->plan.array[p+1].tier; time_t update_every1 = qm->tiers[tier1].db_update_every_s; points_to_add_to_before = query_planer_expand_duration_in_points(update_every, update_every1); } else points_to_add_to_before = POINTS_TO_EXPAND_QUERY; time_t after = qm->plan.array[p].after - (time_t)(update_every * points_to_add_to_after); time_t before = qm->plan.array[p].before + (time_t)(update_every * points_to_add_to_before); ops->plans[p].expanded_after = after; ops->plans[p].expanded_before = before; ops->r->internal.qt->db.tiers[tier].queries++; struct query_metric_tier *tier_ptr = &qm->tiers[tier]; STORAGE_ENGINE *eng = query_metric_storage_engine(ops->r->internal.qt, qm, tier); storage_engine_query_init(eng->backend, tier_ptr->db_metric_handle, &ops->plans[p].handle, after, before, ops->r->internal.qt->request.priority); ops->plans[p].initialized = true; ops->plans[p].finalized = false; } } static void query_planer_finalize_plan(QUERY_ENGINE_OPS *ops, size_t plan_id) { // QUERY_METRIC *qm = ops->qm; if(ops->plans[plan_id].initialized && !ops->plans[plan_id].finalized) { storage_engine_query_finalize(&ops->plans[plan_id].handle); ops->plans[plan_id].initialized = false; ops->plans[plan_id].finalized = true; } } static void query_planer_finalize_remaining_plans(QUERY_ENGINE_OPS *ops) { QUERY_METRIC *qm = ops->qm; for(size_t p = 0; p < qm->plan.used ; p++) query_planer_finalize_plan(ops, p); } static void query_planer_activate_plan(QUERY_ENGINE_OPS *ops, size_t plan_id, time_t overwrite_after __maybe_unused) { QUERY_METRIC *qm = ops->qm; internal_fatal(plan_id >= qm->plan.used, "QUERY: invalid plan_id given"); internal_fatal(!ops->plans[plan_id].initialized, "QUERY: plan has not been initialized"); internal_fatal(ops->plans[plan_id].finalized, "QUERY: plan has been finalized"); internal_fatal(qm->plan.array[plan_id].after > qm->plan.array[plan_id].before, "QUERY: flipped after/before"); ops->tier = qm->plan.array[plan_id].tier; ops->tier_ptr = &qm->tiers[ops->tier]; ops->handle = &ops->plans[plan_id].handle; ops->current_plan = plan_id; if(plan_id + 1 < qm->plan.used && qm->plan.array[plan_id + 1].after < qm->plan.array[plan_id].before) ops->current_plan_expire_time = qm->plan.array[plan_id + 1].after; else ops->current_plan_expire_time = qm->plan.array[plan_id].before; ops->plan_expanded_after = ops->plans[plan_id].expanded_after; ops->plan_expanded_before = ops->plans[plan_id].expanded_before; } static bool query_planer_next_plan(QUERY_ENGINE_OPS *ops, time_t now, time_t last_point_end_time) { QUERY_METRIC *qm = ops->qm; size_t old_plan = ops->current_plan; time_t next_plan_before_time; do { ops->current_plan++; if (ops->current_plan >= qm->plan.used) { ops->current_plan = old_plan; ops->current_plan_expire_time = ops->r->internal.qt->window.before; // let the query run with current plan // we will not switch it return false; } next_plan_before_time = qm->plan.array[ops->current_plan].before; } while(now >= next_plan_before_time || last_point_end_time >= next_plan_before_time); if(!query_metric_is_valid_tier(qm, qm->plan.array[ops->current_plan].tier)) { ops->current_plan = old_plan; ops->current_plan_expire_time = ops->r->internal.qt->window.before; return false; } query_planer_finalize_plan(ops, old_plan); query_planer_activate_plan(ops, ops->current_plan, MIN(now, last_point_end_time)); return true; } static int compare_query_plan_entries_on_start_time(const void *a, const void *b) { QUERY_PLAN_ENTRY *p1 = (QUERY_PLAN_ENTRY *)a; QUERY_PLAN_ENTRY *p2 = (QUERY_PLAN_ENTRY *)b; return (p1->after < p2->after)?-1:1; } static bool query_plan(QUERY_ENGINE_OPS *ops, time_t after_wanted, time_t before_wanted, size_t points_wanted) { QUERY_METRIC *qm = ops->qm; // put our selected tier as the first plan size_t selected_tier; bool switch_tiers = true; if((ops->r->internal.qt->window.options & RRDR_OPTION_SELECTED_TIER) && ops->r->internal.qt->window.tier < storage_tiers && query_metric_is_valid_tier(qm, ops->r->internal.qt->window.tier)) { selected_tier = ops->r->internal.qt->window.tier; switch_tiers = false; } else { selected_tier = query_metric_best_tier_for_timeframe(qm, after_wanted, before_wanted, points_wanted); if(!query_metric_is_valid_tier(qm, selected_tier)) return false; } if(qm->tiers[selected_tier].db_first_time_s > before_wanted || qm->tiers[selected_tier].db_last_time_s < after_wanted) { // we don't have any data to satisfy this query return false; } qm->plan.used = 1; qm->plan.array[0].tier = selected_tier; qm->plan.array[0].after = (qm->tiers[selected_tier].db_first_time_s < after_wanted) ? after_wanted : qm->tiers[selected_tier].db_first_time_s; qm->plan.array[0].before = (qm->tiers[selected_tier].db_last_time_s > before_wanted) ? before_wanted : qm->tiers[selected_tier].db_last_time_s; if(switch_tiers) { // the selected tier time_t selected_tier_first_time_s = qm->plan.array[0].after; time_t selected_tier_last_time_s = qm->plan.array[0].before; // check if our selected tier can start the query if (selected_tier_first_time_s > after_wanted) { // we need some help from other tiers for (size_t tr = (int)selected_tier + 1; tr < storage_tiers && qm->plan.used < QUERY_PLANS_MAX ; tr++) { if(!query_metric_is_valid_tier(qm, tr)) continue; // find the first time of this tier time_t tier_first_time_s = qm->tiers[tr].db_first_time_s; // can it help? if (tier_first_time_s < selected_tier_first_time_s) { // it can help us add detail at the beginning of the query QUERY_PLAN_ENTRY t = { .tier = tr, .after = (tier_first_time_s < after_wanted) ? after_wanted : tier_first_time_s, .before = selected_tier_first_time_s, }; ops->plans[qm->plan.used].initialized = false; ops->plans[qm->plan.used].finalized = false; qm->plan.array[qm->plan.used++] = t; internal_fatal(!t.after || !t.before, "QUERY: invalid plan selected"); // prepare for the tier selected_tier_first_time_s = t.after; if (t.after <= after_wanted) break; } } } // check if our selected tier can finish the query if (selected_tier_last_time_s < before_wanted) { // we need some help from other tiers for (int tr = (int)selected_tier - 1; tr >= 0 && qm->plan.used < QUERY_PLANS_MAX ; tr--) { if(!query_metric_is_valid_tier(qm, tr)) continue; // find the last time of this tier time_t tier_last_time_s = qm->tiers[tr].db_last_time_s; //buffer_sprintf(wb, ": EVAL BEFORE tier %d, %ld", tier, last_time_s); // can it help? if (tier_last_time_s > selected_tier_last_time_s) { // it can help us add detail at the end of the query QUERY_PLAN_ENTRY t = { .tier = tr, .after = selected_tier_last_time_s, .before = (tier_last_time_s > before_wanted) ? before_wanted : tier_last_time_s, }; ops->plans[qm->plan.used].initialized = false; ops->plans[qm->plan.used].finalized = false; qm->plan.array[qm->plan.used++] = t; // prepare for the tier selected_tier_last_time_s = t.before; internal_fatal(!t.after || !t.before, "QUERY: invalid plan selected"); if (t.before >= before_wanted) break; } } } } // sort the query plan if(qm->plan.used > 1) qsort(&qm->plan.array, qm->plan.used, sizeof(QUERY_PLAN_ENTRY), compare_query_plan_entries_on_start_time); if(!query_metric_is_valid_tier(qm, qm->plan.array[0].tier)) return false; #ifdef NETDATA_INTERNAL_CHECKS for(size_t p = 0; p < qm->plan.used ;p++) { internal_fatal(qm->plan.array[p].after > qm->plan.array[p].before, "QUERY: flipped after/before"); internal_fatal(qm->plan.array[p].after < after_wanted, "QUERY: too small plan first time"); internal_fatal(qm->plan.array[p].before > before_wanted, "QUERY: too big plan last time"); } #endif query_planer_initialize_plans(ops); query_planer_activate_plan(ops, 0, 0); return true; } // ---------------------------------------------------------------------------- // dimension level query engine #define query_interpolate_point(this_point, last_point, now) do { \ if(likely( \ /* the point to interpolate is more than 1s wide */ \ (this_point).sp.end_time_s - (this_point).sp.start_time_s > 1 \ \ /* the two points are exactly next to each other */ \ && (last_point).sp.end_time_s == (this_point).sp.start_time_s \ \ /* both points are valid numbers */ \ && netdata_double_isnumber((this_point).value) \ && netdata_double_isnumber((last_point).value) \ \ )) { \ (this_point).value = (last_point).value + ((this_point).value - (last_point).value) * (1.0 - (NETDATA_DOUBLE)((this_point).sp.end_time_s - (now)) / (NETDATA_DOUBLE)((this_point).sp.end_time_s - (this_point).sp.start_time_s)); \ (this_point).sp.end_time_s = now; \ } \ } while(0) #define query_add_point_to_group(r, point, ops, add_flush) do { \ if(likely(netdata_double_isnumber((point).value))) { \ if(likely(fpclassify((point).value) != FP_ZERO)) \ (ops)->group_points_non_zero++; \ \ if(unlikely((point).sp.flags & SN_FLAG_RESET)) \ (ops)->group_value_flags |= RRDR_VALUE_RESET; \ \ time_grouping_add(r, (point).value, add_flush); \ \ storage_point_merge_to((ops)->group_point, (point).sp); \ if(!(point).added) \ storage_point_merge_to((ops)->query_point, (point).sp); \ } \ \ (ops)->group_points_added++; \ } while(0) static __thread QUERY_ENGINE_OPS *released_ops = NULL; static void rrd2rrdr_query_ops_freeall(RRDR *r __maybe_unused) { while(released_ops) { QUERY_ENGINE_OPS *ops = released_ops; released_ops = ops->next; onewayalloc_freez(r->internal.owa, ops); } } static void rrd2rrdr_query_ops_release(QUERY_ENGINE_OPS *ops) { if(!ops) return; ops->next = released_ops; released_ops = ops; } static QUERY_ENGINE_OPS *rrd2rrdr_query_ops_get(RRDR *r) { QUERY_ENGINE_OPS *ops; if(released_ops) { ops = released_ops; released_ops = ops->next; } else { ops = onewayalloc_mallocz(r->internal.owa, sizeof(QUERY_ENGINE_OPS)); } memset(ops, 0, sizeof(*ops)); return ops; } static QUERY_ENGINE_OPS *rrd2rrdr_query_ops_prep(RRDR *r, size_t query_metric_id) { QUERY_TARGET *qt = r->internal.qt; QUERY_ENGINE_OPS *ops = rrd2rrdr_query_ops_get(r); *ops = (QUERY_ENGINE_OPS) { .r = r, .qm = query_metric(qt, query_metric_id), .tier_query_fetch = r->time_grouping.tier_query_fetch, .view_update_every = r->view.update_every, .query_granularity = (time_t)(r->view.update_every / r->view.group), .group_value_flags = RRDR_VALUE_NOTHING, }; if(!query_plan(ops, qt->window.after, qt->window.before, qt->window.points)) { rrd2rrdr_query_ops_release(ops); return NULL; } return ops; } static void rrd2rrdr_query_execute(RRDR *r, size_t dim_id_in_rrdr, QUERY_ENGINE_OPS *ops) { QUERY_TARGET *qt = r->internal.qt; QUERY_METRIC *qm = ops->qm; const RRDR_TIME_GROUPING add_flush = r->time_grouping.add_flush; ops->group_point = STORAGE_POINT_UNSET; ops->query_point = STORAGE_POINT_UNSET; RRDR_OPTIONS options = qt->window.options; size_t points_wanted = qt->window.points; time_t after_wanted = qt->window.after; time_t before_wanted = qt->window.before; (void)before_wanted; // bool debug_this = false; // if(strcmp("user", string2str(rd->id)) == 0 && strcmp("system.cpu", string2str(rd->rrdset->id)) == 0) // debug_this = true; size_t points_added = 0; long rrdr_line = -1; bool use_anomaly_bit_as_value = (r->internal.qt->window.options & RRDR_OPTION_ANOMALY_BIT) ? true : false; NETDATA_DOUBLE min = r->view.min, max = r->view.max; QUERY_POINT last2_point = QUERY_POINT_EMPTY; QUERY_POINT last1_point = QUERY_POINT_EMPTY; QUERY_POINT new_point = QUERY_POINT_EMPTY; // ONE POINT READ-AHEAD // when we switch plans, we read-ahead a point from the next plan // to join them smoothly at the exact time the next plan begins STORAGE_POINT next1_point = STORAGE_POINT_UNSET; time_t now_start_time = after_wanted - ops->query_granularity; time_t now_end_time = after_wanted + ops->view_update_every - ops->query_granularity; size_t db_points_read_since_plan_switch = 0; (void)db_points_read_since_plan_switch; size_t query_is_finished_counter = 0; // The main loop, based on the query granularity we need for( ; points_added < points_wanted && query_is_finished_counter <= 10 ; now_start_time = now_end_time, now_end_time += ops->view_update_every) { if(unlikely(query_plan_should_switch_plan(ops, now_end_time))) { query_planer_next_plan(ops, now_end_time, new_point.sp.end_time_s); db_points_read_since_plan_switch = 0; } // read all the points of the db, prior to the time we need (now_end_time) size_t count_same_end_time = 0; while(count_same_end_time < 100) { if(likely(count_same_end_time == 0)) { last2_point = last1_point; last1_point = new_point; } if(unlikely(storage_engine_query_is_finished(ops->handle))) { query_is_finished_counter++; if(count_same_end_time != 0) { last2_point = last1_point; last1_point = new_point; } new_point = QUERY_POINT_EMPTY; new_point.sp.start_time_s = last1_point.sp.end_time_s; new_point.sp.end_time_s = now_end_time; // // if(debug_this) netdata_log_info("QUERY: is finished() returned true"); // break; } else query_is_finished_counter = 0; // fetch the new point { STORAGE_POINT sp; if(likely(storage_point_is_unset(next1_point))) { db_points_read_since_plan_switch++; sp = storage_engine_query_next_metric(ops->handle); ops->db_points_read_per_tier[ops->tier]++; ops->db_total_points_read++; if(unlikely(options & RRDR_OPTION_ABSOLUTE)) storage_point_make_positive(sp); } else { // ONE POINT READ-AHEAD sp = next1_point; storage_point_unset(next1_point); db_points_read_since_plan_switch = 1; } // ONE POINT READ-AHEAD if(unlikely(query_plan_should_switch_plan(ops, sp.end_time_s) && query_planer_next_plan(ops, now_end_time, new_point.sp.end_time_s))) { // The end time of the current point, crosses our plans (tiers) // so, we switched plan (tier) // // There are 2 cases now: // // A. the entire point of the previous plan is to the future of point from the next plan // B. part of the point of the previous plan overlaps with the point from the next plan STORAGE_POINT sp2 = storage_engine_query_next_metric(ops->handle); ops->db_points_read_per_tier[ops->tier]++; ops->db_total_points_read++; if(unlikely(options & RRDR_OPTION_ABSOLUTE)) storage_point_make_positive(sp); if(sp.start_time_s > sp2.start_time_s) // the point from the previous plan is useless sp = sp2; else // let the query run from the previous plan // but setting this will also cut off the interpolation // of the point from the previous plan next1_point = sp2; } new_point.sp = sp; new_point.added = false; query_point_set_id(new_point, ops->db_total_points_read); // if(debug_this) // netdata_log_info("QUERY: got point %zu, from time %ld to %ld // now from %ld to %ld // query from %ld to %ld", // new_point.id, new_point.start_time, new_point.end_time, now_start_time, now_end_time, after_wanted, before_wanted); // // get the right value from the point we got if(likely(!storage_point_is_unset(sp) && !storage_point_is_gap(sp))) { if(unlikely(use_anomaly_bit_as_value)) new_point.value = storage_point_anomaly_rate(new_point.sp); else { switch (ops->tier_query_fetch) { default: case TIER_QUERY_FETCH_AVERAGE: new_point.value = sp.sum / (NETDATA_DOUBLE)sp.count; break; case TIER_QUERY_FETCH_MIN: new_point.value = sp.min; break; case TIER_QUERY_FETCH_MAX: new_point.value = sp.max; break; case TIER_QUERY_FETCH_SUM: new_point.value = sp.sum; break; }; } } else new_point.value = NAN; } // check if the db is giving us zero duration points if(unlikely(db_points_read_since_plan_switch > 1 && new_point.sp.start_time_s == new_point.sp.end_time_s)) { internal_error(true, "QUERY: '%s', dimension '%s' next_metric() returned " "point %zu from %ld to %ld, that are both equal", qt->id, query_metric_id(qt, qm), new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s); new_point.sp.start_time_s = new_point.sp.end_time_s - ops->tier_ptr->db_update_every_s; } // check if the db is advancing the query if(unlikely(db_points_read_since_plan_switch > 1 && new_point.sp.end_time_s <= last1_point.sp.end_time_s)) { internal_error(true, "QUERY: '%s', dimension '%s' next_metric() returned " "point %zu from %ld to %ld, before the " "last point %zu from %ld to %ld, " "now is %ld to %ld", qt->id, query_metric_id(qt, qm), new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s, last1_point.id, last1_point.sp.start_time_s, last1_point.sp.end_time_s, now_start_time, now_end_time); count_same_end_time++; continue; } count_same_end_time = 0; // decide how to use this point if(likely(new_point.sp.end_time_s < now_end_time)) { // likely to favor tier0 // this db point ends before our now_end_time if(likely(new_point.sp.end_time_s >= now_start_time)) { // likely to favor tier0 // this db point ends after our now_start time query_add_point_to_group(r, new_point, ops, add_flush); new_point.added = true; } else { // we don't need this db point // it is totally outside our current time-frame // this is desirable for the first point of the query // because it allows us to interpolate the next point // at exactly the time we will want // we only log if this is not point 1 internal_error(new_point.sp.end_time_s < ops->plan_expanded_after && db_points_read_since_plan_switch > 1, "QUERY: '%s', dimension '%s' next_metric() " "returned point %zu from %ld time %ld, " "which is entirely before our current timeframe %ld to %ld " "(and before the entire query, after %ld, before %ld)", qt->id, query_metric_id(qt, qm), new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s, now_start_time, now_end_time, ops->plan_expanded_after, ops->plan_expanded_before); } } else { // the point ends in the future // so, we will interpolate it below, at the inner loop break; } } if(unlikely(count_same_end_time)) { internal_error(true, "QUERY: '%s', dimension '%s', the database does not advance the query," " it returned an end time less or equal to the end time of the last " "point we got %ld, %zu times", qt->id, query_metric_id(qt, qm), last1_point.sp.end_time_s, count_same_end_time); if(unlikely(new_point.sp.end_time_s <= last1_point.sp.end_time_s)) new_point.sp.end_time_s = now_end_time; } time_t stop_time = new_point.sp.end_time_s; if(unlikely(!storage_point_is_unset(next1_point) && next1_point.start_time_s >= now_end_time)) { // ONE POINT READ-AHEAD // the point crosses the start time of the // read ahead storage point we have read stop_time = next1_point.start_time_s; } // the inner loop // we have 3 points in memory: last2, last1, new // we select the one to use based on their timestamps internal_fatal(now_end_time > stop_time || points_added >= points_wanted, "QUERY: first part of query provides invalid point to interpolate (now_end_time %ld, stop_time %ld", now_end_time, stop_time); do { // now_start_time is wrong in this loop // but, we don't need it QUERY_POINT current_point; if(likely(now_end_time > new_point.sp.start_time_s)) { // it is time for our NEW point to be used current_point = new_point; new_point.added = true; // first copy, then set it, so that new_point will not be added again query_interpolate_point(current_point, last1_point, now_end_time); // internal_error(current_point.id > 0 // && last1_point.id == 0 // && current_point.end_time > after_wanted // && current_point.end_time > now_end_time, // "QUERY: '%s', dimension '%s', after %ld, before %ld, view update every %ld," // " query granularity %ld, interpolating point %zu (from %ld to %ld) at %ld," // " but we could really favor by having last_point1 in this query.", // qt->id, string2str(qm->dimension.id), // after_wanted, before_wanted, // ops.view_update_every, ops.query_granularity, // current_point.id, current_point.start_time, current_point.end_time, // now_end_time); } else if(likely(now_end_time <= last1_point.sp.end_time_s)) { // our LAST point is still valid current_point = last1_point; last1_point.added = true; // first copy, then set it, so that last1_point will not be added again query_interpolate_point(current_point, last2_point, now_end_time); // internal_error(current_point.id > 0 // && last2_point.id == 0 // && current_point.end_time > after_wanted // && current_point.end_time > now_end_time, // "QUERY: '%s', dimension '%s', after %ld, before %ld, view update every %ld," // " query granularity %ld, interpolating point %zu (from %ld to %ld) at %ld," // " but we could really favor by having last_point2 in this query.", // qt->id, string2str(qm->dimension.id), // after_wanted, before_wanted, ops.view_update_every, ops.query_granularity, // current_point.id, current_point.start_time, current_point.end_time, // now_end_time); } else { // a GAP, we don't have a value this time current_point = QUERY_POINT_EMPTY; } query_add_point_to_group(r, current_point, ops, add_flush); rrdr_line = rrdr_line_init(r, now_end_time, rrdr_line); size_t rrdr_o_v_index = rrdr_line * r->d + dim_id_in_rrdr; // find the place to store our values RRDR_VALUE_FLAGS *rrdr_value_options_ptr = &r->o[rrdr_o_v_index]; // update the dimension options if(likely(ops->group_points_non_zero)) r->od[dim_id_in_rrdr] |= RRDR_DIMENSION_NONZERO; // store the specific point options *rrdr_value_options_ptr = ops->group_value_flags; // store the group value NETDATA_DOUBLE group_value = time_grouping_flush(r, rrdr_value_options_ptr, add_flush); r->v[rrdr_o_v_index] = group_value; r->ar[rrdr_o_v_index] = storage_point_anomaly_rate(ops->group_point); if(likely(points_added || r->internal.queries_count)) { // find the min/max across all dimensions if(unlikely(group_value < min)) min = group_value; if(unlikely(group_value > max)) max = group_value; } else { // runs only when r->internal.queries_count == 0 && points_added == 0 // so, on the first point added for the query. min = max = group_value; } points_added++; ops->group_points_added = 0; ops->group_value_flags = RRDR_VALUE_NOTHING; ops->group_points_non_zero = 0; ops->group_point = STORAGE_POINT_UNSET; now_end_time += ops->view_update_every; } while(now_end_time <= stop_time && points_added < points_wanted); // the loop above increased "now" by ops->view_update_every, // but the main loop will increase it too, // so, let's undo the last iteration of this loop now_end_time -= ops->view_update_every; } query_planer_finalize_remaining_plans(ops); qm->query_points = ops->query_point; // fill the rest of the points with empty values while (points_added < points_wanted) { rrdr_line++; size_t rrdr_o_v_index = rrdr_line * r->d + dim_id_in_rrdr; r->o[rrdr_o_v_index] = RRDR_VALUE_EMPTY; r->v[rrdr_o_v_index] = 0.0; r->ar[rrdr_o_v_index] = 0.0; points_added++; } r->internal.queries_count++; r->view.min = min; r->view.max = max; r->stats.result_points_generated += points_added; r->stats.db_points_read += ops->db_total_points_read; for(size_t tr = 0; tr < storage_tiers ; tr++) qt->db.tiers[tr].points += ops->db_points_read_per_tier[tr]; } // ---------------------------------------------------------------------------- // fill the gap of a tier void store_metric_at_tier(RRDDIM *rd, size_t tier, struct rrddim_tier *t, STORAGE_POINT sp, usec_t now_ut); void rrdr_fill_tier_gap_from_smaller_tiers(RRDDIM *rd, size_t tier, time_t now_s) { if(unlikely(tier >= storage_tiers)) return; if(storage_tiers_backfill[tier] == RRD_BACKFILL_NONE) return; struct rrddim_tier *t = &rd->tiers[tier]; if(unlikely(!t)) return; time_t latest_time_s = storage_engine_latest_time_s(t->backend, t->db_metric_handle); time_t granularity = (time_t)t->tier_grouping * (time_t)rd->rrdset->update_every; time_t time_diff = now_s - latest_time_s; // if the user wants only NEW backfilling, and we don't have any data if(storage_tiers_backfill[tier] == RRD_BACKFILL_NEW && latest_time_s <= 0) return; // there is really nothing we can do if(now_s <= latest_time_s || time_diff < granularity) return; struct storage_engine_query_handle handle; // for each lower tier for(int read_tier = (int)tier - 1; read_tier >= 0 ; read_tier--){ time_t smaller_tier_first_time = storage_engine_oldest_time_s(rd->tiers[read_tier].backend, rd->tiers[read_tier].db_metric_handle); time_t smaller_tier_last_time = storage_engine_latest_time_s(rd->tiers[read_tier].backend, rd->tiers[read_tier].db_metric_handle); if(smaller_tier_last_time <= latest_time_s) continue; // it is as bad as we are long after_wanted = (latest_time_s < smaller_tier_first_time) ? smaller_tier_first_time : latest_time_s; long before_wanted = smaller_tier_last_time; struct rrddim_tier *tmp = &rd->tiers[read_tier]; storage_engine_query_init(tmp->backend, tmp->db_metric_handle, &handle, after_wanted, before_wanted, STORAGE_PRIORITY_HIGH); size_t points_read = 0; while(!storage_engine_query_is_finished(&handle)) { STORAGE_POINT sp = storage_engine_query_next_metric(&handle); points_read++; if(sp.end_time_s > latest_time_s) { latest_time_s = sp.end_time_s; store_metric_at_tier(rd, tier, t, sp, sp.end_time_s * USEC_PER_SEC); } } storage_engine_query_finalize(&handle); store_metric_collection_completed(); global_statistics_backfill_query_completed(points_read); //internal_error(true, "DBENGINE: backfilled chart '%s', dimension '%s', tier %d, from %ld to %ld, with %zu points from tier %d", // rd->rrdset->name, rd->name, tier, after_wanted, before_wanted, points, tr); } } // ---------------------------------------------------------------------------- // fill RRDR for the whole chart #ifdef NETDATA_INTERNAL_CHECKS static void rrd2rrdr_log_request_response_metadata(RRDR *r , RRDR_OPTIONS options __maybe_unused , RRDR_TIME_GROUPING group_method , bool aligned , size_t group , time_t resampling_time , size_t resampling_group , time_t after_wanted , time_t after_requested , time_t before_wanted , time_t before_requested , size_t points_requested , size_t points_wanted //, size_t after_slot //, size_t before_slot , const char *msg ) { QUERY_TARGET *qt = r->internal.qt; time_t first_entry_s = qt->db.first_time_s; time_t last_entry_s = qt->db.last_time_s; internal_error( true, "rrd2rrdr() on %s update every %ld with %s grouping %s (group: %zu, resampling_time: %ld, resampling_group: %zu), " "after (got: %ld, want: %ld, req: %ld, db: %ld), " "before (got: %ld, want: %ld, req: %ld, db: %ld), " "duration (got: %ld, want: %ld, req: %ld, db: %ld), " "points (got: %zu, want: %zu, req: %zu), " "%s" , qt->id , qt->window.query_granularity // grouping , (aligned) ? "aligned" : "unaligned" , time_grouping_method2string(group_method) , group , resampling_time , resampling_group // after , r->view.after , after_wanted , after_requested , first_entry_s // before , r->view.before , before_wanted , before_requested , last_entry_s // duration , (long)(r->view.before - r->view.after + qt->window.query_granularity) , (long)(before_wanted - after_wanted + qt->window.query_granularity) , (long)before_requested - after_requested , (long)((last_entry_s - first_entry_s) + qt->window.query_granularity) // points , r->rows , points_wanted , points_requested // message , msg ); } #endif // NETDATA_INTERNAL_CHECKS // #define DEBUG_QUERY_LOGIC 1 #ifdef DEBUG_QUERY_LOGIC #define query_debug_log_init() BUFFER *debug_log = buffer_create(1000) #define query_debug_log(args...) buffer_sprintf(debug_log, ##args) #define query_debug_log_fin() { \ netdata_log_info("QUERY: '%s', after:%ld, before:%ld, duration:%ld, points:%zu, res:%ld - wanted => after:%ld, before:%ld, points:%zu, group:%zu, granularity:%ld, resgroup:%ld, resdiv:" NETDATA_DOUBLE_FORMAT_AUTO " %s", qt->id, after_requested, before_requested, before_requested - after_requested, points_requested, resampling_time_requested, after_wanted, before_wanted, points_wanted, group, query_granularity, resampling_group, resampling_divisor, buffer_tostring(debug_log)); \ buffer_free(debug_log); \ debug_log = NULL; \ } #define query_debug_log_free() do { buffer_free(debug_log); } while(0) #else #define query_debug_log_init() debug_dummy() #define query_debug_log(args...) debug_dummy() #define query_debug_log_fin() debug_dummy() #define query_debug_log_free() debug_dummy() #endif bool query_target_calculate_window(QUERY_TARGET *qt) { if (unlikely(!qt)) return false; size_t points_requested = (long)qt->request.points; time_t after_requested = qt->request.after; time_t before_requested = qt->request.before; RRDR_TIME_GROUPING group_method = qt->request.time_group_method; time_t resampling_time_requested = qt->request.resampling_time; RRDR_OPTIONS options = qt->window.options; size_t tier = qt->request.tier; time_t update_every = qt->db.minimum_latest_update_every_s ? qt->db.minimum_latest_update_every_s : 1; // RULES // points_requested = 0 // the user wants all the natural points the database has // // after_requested = 0 // the user wants to start the query from the oldest point in our database // // before_requested = 0 // the user wants the query to end to the latest point in our database // // when natural points are wanted, the query has to be aligned to the update_every // of the database size_t points_wanted = points_requested; time_t after_wanted = after_requested; time_t before_wanted = before_requested; bool aligned = !(options & RRDR_OPTION_NOT_ALIGNED); bool automatic_natural_points = (points_wanted == 0); bool relative_period_requested = false; bool natural_points = (options & RRDR_OPTION_NATURAL_POINTS) || automatic_natural_points; bool before_is_aligned_to_db_end = false; query_debug_log_init(); if (ABS(before_requested) <= API_RELATIVE_TIME_MAX || ABS(after_requested) <= API_RELATIVE_TIME_MAX) { relative_period_requested = true; natural_points = true; options |= RRDR_OPTION_NATURAL_POINTS; query_debug_log(":relative+natural"); } // if the user wants virtual points, make sure we do it if (options & RRDR_OPTION_VIRTUAL_POINTS) natural_points = false; // set the right flag about natural and virtual points if (natural_points) { options |= RRDR_OPTION_NATURAL_POINTS; if (options & RRDR_OPTION_VIRTUAL_POINTS) options &= ~RRDR_OPTION_VIRTUAL_POINTS; } else { options |= RRDR_OPTION_VIRTUAL_POINTS; if (options & RRDR_OPTION_NATURAL_POINTS) options &= ~RRDR_OPTION_NATURAL_POINTS; } if (after_wanted == 0 || before_wanted == 0) { relative_period_requested = true; time_t first_entry_s = qt->db.first_time_s; time_t last_entry_s = qt->db.last_time_s; if (first_entry_s == 0 || last_entry_s == 0) { internal_error(true, "QUERY: no data detected on query '%s' (db first_entry_t = %ld, last_entry_t = %ld)", qt->id, first_entry_s, last_entry_s); after_wanted = qt->window.after; before_wanted = qt->window.before; if(after_wanted == before_wanted) after_wanted = before_wanted - update_every; if (points_wanted == 0) { points_wanted = (before_wanted - after_wanted) / update_every; query_debug_log(":zero points_wanted %zu", points_wanted); } } else { query_debug_log(":first_entry_t %ld, last_entry_t %ld", first_entry_s, last_entry_s); if (after_wanted == 0) { after_wanted = first_entry_s; query_debug_log(":zero after_wanted %ld", after_wanted); } if (before_wanted == 0) { before_wanted = last_entry_s; before_is_aligned_to_db_end = true; query_debug_log(":zero before_wanted %ld", before_wanted); } if (points_wanted == 0) { points_wanted = (last_entry_s - first_entry_s) / update_every; query_debug_log(":zero points_wanted %zu", points_wanted); } } } if (points_wanted == 0) { points_wanted = 600; query_debug_log(":zero600 points_wanted %zu", points_wanted); } // convert our before_wanted and after_wanted to absolute rrdr_relative_window_to_absolute_query(&after_wanted, &before_wanted, NULL, unittest_running); query_debug_log(":relative2absolute after %ld, before %ld", after_wanted, before_wanted); if (natural_points && (options & RRDR_OPTION_SELECTED_TIER) && tier > 0 && storage_tiers > 1) { update_every = rrdset_find_natural_update_every_for_timeframe( qt, after_wanted, before_wanted, points_wanted, options, tier); if (update_every <= 0) update_every = qt->db.minimum_latest_update_every_s; query_debug_log(":natural update every %ld", update_every); } // this is the update_every of the query // it may be different to the update_every of the database time_t query_granularity = (natural_points) ? update_every : 1; if (query_granularity <= 0) query_granularity = 1; query_debug_log(":query_granularity %ld", query_granularity); // align before_wanted and after_wanted to query_granularity if (before_wanted % query_granularity) { before_wanted -= before_wanted % query_granularity; query_debug_log(":granularity align before_wanted %ld", before_wanted); } if (after_wanted % query_granularity) { after_wanted -= after_wanted % query_granularity; query_debug_log(":granularity align after_wanted %ld", after_wanted); } // automatic_natural_points is set when the user wants all the points available in the database if (automatic_natural_points) { points_wanted = (before_wanted - after_wanted + 1) / query_granularity; if (unlikely(points_wanted <= 0)) points_wanted = 1; query_debug_log(":auto natural points_wanted %zu", points_wanted); } time_t duration = before_wanted - after_wanted; // if the resampling time is too big, extend the duration to the past if (unlikely(resampling_time_requested > duration)) { after_wanted = before_wanted - resampling_time_requested; duration = before_wanted - after_wanted; query_debug_log(":resampling after_wanted %ld", after_wanted); } // if the duration is not aligned to resampling time // extend the duration to the past, to avoid a gap at the chart // only when the missing duration is above 1/10th of a point if (resampling_time_requested > query_granularity && duration % resampling_time_requested) { time_t delta = duration % resampling_time_requested; if (delta > resampling_time_requested / 10) { after_wanted -= resampling_time_requested - delta; duration = before_wanted - after_wanted; query_debug_log(":resampling2 after_wanted %ld", after_wanted); } } // the available points of the query size_t points_available = (duration + 1) / query_granularity; if (unlikely(points_available <= 0)) points_available = 1; query_debug_log(":points_available %zu", points_available); if (points_wanted > points_available) { points_wanted = points_available; query_debug_log(":max points_wanted %zu", points_wanted); } if(points_wanted > 86400 && !unittest_running) { points_wanted = 86400; query_debug_log(":absolute max points_wanted %zu", points_wanted); } // calculate the desired grouping of source data points size_t group = points_available / points_wanted; if (group == 0) group = 1; // round "group" to the closest integer if (points_available % points_wanted > points_wanted / 2) group++; query_debug_log(":group %zu", group); if (points_wanted * group * query_granularity < (size_t)duration) { // the grouping we are going to do, is not enough // to cover the entire duration requested, so // we have to change the number of points, to make sure we will // respect the timeframe as closely as possibly // let's see how many points are the optimal points_wanted = points_available / group; if (points_wanted * group < points_available) points_wanted++; if (unlikely(points_wanted == 0)) points_wanted = 1; query_debug_log(":optimal points %zu", points_wanted); } // resampling_time_requested enforces a certain grouping multiple NETDATA_DOUBLE resampling_divisor = 1.0; size_t resampling_group = 1; if (unlikely(resampling_time_requested > query_granularity)) { // the points we should group to satisfy gtime resampling_group = resampling_time_requested / query_granularity; if (unlikely(resampling_time_requested % query_granularity)) resampling_group++; query_debug_log(":resampling group %zu", resampling_group); // adapt group according to resampling_group if (unlikely(group < resampling_group)) { group = resampling_group; // do not allow grouping below the desired one query_debug_log(":group less res %zu", group); } if (unlikely(group % resampling_group)) { group += resampling_group - (group % resampling_group); // make sure group is multiple of resampling_group query_debug_log(":group mod res %zu", group); } // resampling_divisor = group / resampling_group; resampling_divisor = (NETDATA_DOUBLE) (group * query_granularity) / (NETDATA_DOUBLE) resampling_time_requested; query_debug_log(":resampling divisor " NETDATA_DOUBLE_FORMAT, resampling_divisor); } // now that we have group, align the requested timeframe to fit it. if (aligned && before_wanted % (group * query_granularity)) { if (before_is_aligned_to_db_end) before_wanted -= before_wanted % (time_t)(group * query_granularity); else before_wanted += (time_t)(group * query_granularity) - before_wanted % (time_t)(group * query_granularity); query_debug_log(":align before_wanted %ld", before_wanted); } after_wanted = before_wanted - (time_t)(points_wanted * group * query_granularity) + query_granularity; query_debug_log(":final after_wanted %ld", after_wanted); duration = before_wanted - after_wanted; query_debug_log(":final duration %ld", duration + 1); query_debug_log_fin(); internal_error(points_wanted != duration / (query_granularity * group) + 1, "QUERY: points_wanted %zu is not points %zu", points_wanted, (size_t)(duration / (query_granularity * group) + 1)); internal_error(group < resampling_group, "QUERY: group %zu is less than the desired group points %zu", group, resampling_group); internal_error(group > resampling_group && group % resampling_group, "QUERY: group %zu is not a multiple of the desired group points %zu", group, resampling_group); // ------------------------------------------------------------------------- // update QUERY_TARGET with our calculations qt->window.after = after_wanted; qt->window.before = before_wanted; qt->window.relative = relative_period_requested; qt->window.points = points_wanted; qt->window.group = group; qt->window.time_group_method = group_method; qt->window.time_group_options = qt->request.time_group_options; qt->window.query_granularity = query_granularity; qt->window.resampling_group = resampling_group; qt->window.resampling_divisor = resampling_divisor; qt->window.options = options; qt->window.tier = tier; qt->window.aligned = aligned; return true; } // ---------------------------------------------------------------------------- // group by struct group_by_label_key { DICTIONARY *values; }; static void group_by_label_key_insert_cb(const DICTIONARY_ITEM *item __maybe_unused, void *value, void *data) { // add the key to our r->label_keys global keys dictionary DICTIONARY *label_keys = data; dictionary_set(label_keys, dictionary_acquired_item_name(item), NULL, 0); // create a dictionary for the values of this key struct group_by_label_key *k = value; k->values = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE, NULL, 0); } static void group_by_label_key_delete_cb(const DICTIONARY_ITEM *item __maybe_unused, void *value, void *data __maybe_unused) { struct group_by_label_key *k = value; dictionary_destroy(k->values); } static int rrdlabels_traversal_cb_to_group_by_label_key(const char *name, const char *value, RRDLABEL_SRC ls __maybe_unused, void *data) { DICTIONARY *dl = data; struct group_by_label_key *k = dictionary_set(dl, name, NULL, sizeof(struct group_by_label_key)); dictionary_set(k->values, value, NULL, 0); return 1; } void rrdr_json_group_by_labels(BUFFER *wb, const char *key, RRDR *r, RRDR_OPTIONS options) { if(!r->label_keys || !r->dl) return; buffer_json_member_add_object(wb, key); void *t; dfe_start_read(r->label_keys, t) { buffer_json_member_add_array(wb, t_dfe.name); for(size_t d = 0; d < r->d ;d++) { if(!rrdr_dimension_should_be_exposed(r->od[d], options)) continue; struct group_by_label_key *k = dictionary_get(r->dl[d], t_dfe.name); if(k) { buffer_json_add_array_item_array(wb); void *tt; dfe_start_read(k->values, tt) { buffer_json_add_array_item_string(wb, tt_dfe.name); } dfe_done(tt); buffer_json_array_close(wb); } else buffer_json_add_array_item_string(wb, NULL); } buffer_json_array_close(wb); } dfe_done(t); buffer_json_object_close(wb); // key } static void rrd2rrdr_set_timestamps(RRDR *r) { QUERY_TARGET *qt = r->internal.qt; internal_fatal(qt->window.points != r->n, "QUERY: mismatch to the number of points in qt and r"); r->view.group = qt->window.group; r->view.update_every = (int) query_view_update_every(qt); r->view.before = qt->window.before; r->view.after = qt->window.after; r->time_grouping.points_wanted = qt->window.points; r->time_grouping.resampling_group = qt->window.resampling_group; r->time_grouping.resampling_divisor = qt->window.resampling_divisor; r->rows = qt->window.points; size_t points_wanted = qt->window.points; time_t after_wanted = qt->window.after; time_t before_wanted = qt->window.before; (void)before_wanted; time_t view_update_every = r->view.update_every; time_t query_granularity = (time_t)(r->view.update_every / r->view.group); size_t rrdr_line = 0; time_t first_point_end_time = after_wanted + view_update_every - query_granularity; time_t now_end_time = first_point_end_time; while (rrdr_line < points_wanted) { r->t[rrdr_line++] = now_end_time; now_end_time += view_update_every; } internal_fatal(r->t[0] != first_point_end_time, "QUERY: wrong first timestamp in the query"); internal_error(r->t[points_wanted - 1] != before_wanted, "QUERY: wrong last timestamp in the query, expected %ld, found %ld", before_wanted, r->t[points_wanted - 1]); } static void query_group_by_make_dimension_key(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) { buffer_flush(key); if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) { buffer_strcat(key, "__hidden_dimensions__"); } else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) { buffer_strcat(key, "selected"); } else { if (group_by & RRDR_GROUP_BY_DIMENSION) { buffer_fast_strcat(key, "|", 1); buffer_strcat(key, query_metric_name(qt, qm)); } if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) { buffer_fast_strcat(key, "|", 1); buffer_strcat(key, string2str(query_instance_id_fqdn(qi, qt->request.version))); } if (group_by & RRDR_GROUP_BY_LABEL) { RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria); for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) { buffer_fast_strcat(key, "|", 1); rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]"); } } if (group_by & RRDR_GROUP_BY_NODE) { buffer_fast_strcat(key, "|", 1); buffer_strcat(key, qn->rrdhost->machine_guid); } if (group_by & RRDR_GROUP_BY_CONTEXT) { buffer_fast_strcat(key, "|", 1); buffer_strcat(key, rrdcontext_acquired_id(qc->rca)); } if (group_by & RRDR_GROUP_BY_UNITS) { buffer_fast_strcat(key, "|", 1); buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria)); } } } static void query_group_by_make_dimension_id(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) { buffer_flush(key); if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) { buffer_strcat(key, "__hidden_dimensions__"); } else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) { buffer_strcat(key, "selected"); } else { if (group_by & RRDR_GROUP_BY_DIMENSION) { buffer_strcat(key, query_metric_name(qt, qm)); } if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); if (group_by & RRDR_GROUP_BY_NODE) buffer_strcat(key, rrdinstance_acquired_id(qi->ria)); else buffer_strcat(key, string2str(query_instance_id_fqdn(qi, qt->request.version))); } if (group_by & RRDR_GROUP_BY_LABEL) { RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria); for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]"); } } if (group_by & RRDR_GROUP_BY_NODE) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, qn->rrdhost->machine_guid); } if (group_by & RRDR_GROUP_BY_CONTEXT) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, rrdcontext_acquired_id(qc->rca)); } if (group_by & RRDR_GROUP_BY_UNITS) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria)); } } } static void query_group_by_make_dimension_name(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) { buffer_flush(key); if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) { buffer_strcat(key, "__hidden_dimensions__"); } else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) { buffer_strcat(key, "selected"); } else { if (group_by & RRDR_GROUP_BY_DIMENSION) { buffer_strcat(key, query_metric_name(qt, qm)); } if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); if (group_by & RRDR_GROUP_BY_NODE) buffer_strcat(key, rrdinstance_acquired_name(qi->ria)); else buffer_strcat(key, string2str(query_instance_name_fqdn(qi, qt->request.version))); } if (group_by & RRDR_GROUP_BY_LABEL) { RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria); for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]"); } } if (group_by & RRDR_GROUP_BY_NODE) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, rrdhost_hostname(qn->rrdhost)); } if (group_by & RRDR_GROUP_BY_CONTEXT) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, rrdcontext_acquired_id(qc->rca)); } if (group_by & RRDR_GROUP_BY_UNITS) { if (buffer_strlen(key) != 0) buffer_fast_strcat(key, ",", 1); buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria)); } } } struct rrdr_group_by_entry { size_t priority; size_t count; STRING *id; STRING *name; STRING *units; RRDR_DIMENSION_FLAGS od; DICTIONARY *dl; }; static RRDR *rrd2rrdr_group_by_initialize(ONEWAYALLOC *owa, QUERY_TARGET *qt) { RRDR *r_tmp = NULL; RRDR_OPTIONS options = qt->window.options; if(qt->request.version < 2) { // v1 query RRDR *r = rrdr_create(owa, qt, qt->query.used, qt->window.points); if(unlikely(!r)) { internal_error(true, "QUERY: cannot create RRDR for %s, after=%ld, before=%ld, dimensions=%u, points=%zu", qt->id, qt->window.after, qt->window.before, qt->query.used, qt->window.points); return NULL; } r->group_by.r = NULL; for(size_t d = 0; d < qt->query.used ; d++) { QUERY_METRIC *qm = query_metric(qt, d); QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id); r->di[d] = rrdmetric_acquired_id_dup(qd->rma); r->dn[d] = rrdmetric_acquired_name_dup(qd->rma); } rrd2rrdr_set_timestamps(r); return r; } // v2 query // parse all the group-by label keys for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) { if (qt->request.group_by[g].group_by & RRDR_GROUP_BY_LABEL && qt->request.group_by[g].group_by_label && *qt->request.group_by[g].group_by_label) qt->group_by[g].used = quoted_strings_splitter_query_group_by_label( qt->request.group_by[g].group_by_label, qt->group_by[g].label_keys, GROUP_BY_MAX_LABEL_KEYS); if (!qt->group_by[g].used) qt->request.group_by[g].group_by &= ~RRDR_GROUP_BY_LABEL; } // make sure there are valid group-by methods for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) { if(!(qt->request.group_by[g].group_by & SUPPORTED_GROUP_BY_METHODS)) qt->request.group_by[g].group_by = (g == 0) ? RRDR_GROUP_BY_DIMENSION : RRDR_GROUP_BY_NONE; } bool query_has_percentage_of_group = query_target_has_percentage_of_group(qt); // merge all group-by options to upper levels, // so that the top level has all the groupings of the inner levels, // and each subsequent level has all the groupings of its inner levels. for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES - 1 ;g++) { if(qt->request.group_by[g].group_by == RRDR_GROUP_BY_NONE) continue; if(qt->request.group_by[g].group_by == RRDR_GROUP_BY_SELECTED) { for (size_t r = g + 1; r < MAX_QUERY_GROUP_BY_PASSES; r++) qt->request.group_by[r].group_by = RRDR_GROUP_BY_NONE; } else { for (size_t r = g + 1; r < MAX_QUERY_GROUP_BY_PASSES; r++) { if (qt->request.group_by[r].group_by == RRDR_GROUP_BY_NONE) continue; if (qt->request.group_by[r].group_by != RRDR_GROUP_BY_SELECTED) { if(qt->request.group_by[r].group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE) qt->request.group_by[g].group_by |= RRDR_GROUP_BY_INSTANCE; else qt->request.group_by[g].group_by |= qt->request.group_by[r].group_by; if(qt->request.group_by[r].group_by & RRDR_GROUP_BY_LABEL) { for (size_t lr = 0; lr < qt->group_by[r].used; lr++) { bool found = false; for (size_t lg = 0; lg < qt->group_by[g].used; lg++) { if (strcmp(qt->group_by[g].label_keys[lg], qt->group_by[r].label_keys[lr]) == 0) { found = true; break; } } if (!found && qt->group_by[g].used < GROUP_BY_MAX_LABEL_KEYS * MAX_QUERY_GROUP_BY_PASSES) qt->group_by[g].label_keys[qt->group_by[g].used++] = qt->group_by[r].label_keys[lr]; } } } } } } int added = 0; RRDR *first_r = NULL, *last_r = NULL; BUFFER *key = buffer_create(0, NULL); struct rrdr_group_by_entry *entries = onewayalloc_mallocz(owa, qt->query.used * sizeof(struct rrdr_group_by_entry)); DICTIONARY *groups = dictionary_create(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE); DICTIONARY *label_keys = NULL; for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) { RRDR_GROUP_BY group_by = qt->request.group_by[g].group_by; RRDR_GROUP_BY_FUNCTION aggregation_method = qt->request.group_by[g].aggregation; if(group_by == RRDR_GROUP_BY_NONE) break; memset(entries, 0, qt->query.used * sizeof(struct rrdr_group_by_entry)); dictionary_flush(groups); added = 0; size_t hidden_dimensions = 0; bool final_grouping = (g == MAX_QUERY_GROUP_BY_PASSES - 1 || qt->request.group_by[g + 1].group_by == RRDR_GROUP_BY_NONE) ? true : false; if (final_grouping && (options & RRDR_OPTION_GROUP_BY_LABELS)) label_keys = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE, NULL, 0); QUERY_INSTANCE *last_qi = NULL; size_t priority = 0; time_t update_every_max = 0; for (size_t d = 0; d < qt->query.used; d++) { QUERY_METRIC *qm = query_metric(qt, d); QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id); QUERY_INSTANCE *qi = query_instance(qt, qm->link.query_instance_id); QUERY_CONTEXT *qc = query_context(qt, qm->link.query_context_id); QUERY_NODE *qn = query_node(qt, qm->link.query_node_id); if (qi != last_qi) { last_qi = qi; time_t update_every = rrdinstance_acquired_update_every(qi->ria); if (update_every > update_every_max) update_every_max = update_every; } priority = qd->priority; if(qm->status & RRDR_DIMENSION_HIDDEN) hidden_dimensions++; // -------------------------------------------------------------------- // generate the group by key query_group_by_make_dimension_key(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group); // lookup the key in the dictionary int pos = -1; int *set = dictionary_set(groups, buffer_tostring(key), &pos, sizeof(pos)); if (*set == -1) { // the key just added to the dictionary *set = pos = added++; // ---------------------------------------------------------------- // generate the dimension id query_group_by_make_dimension_id(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group); entries[pos].id = string_strdupz(buffer_tostring(key)); // ---------------------------------------------------------------- // generate the dimension name query_group_by_make_dimension_name(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group); entries[pos].name = string_strdupz(buffer_tostring(key)); // add the rest of the info entries[pos].units = rrdinstance_acquired_units_dup(qi->ria); entries[pos].priority = priority; if (label_keys) { entries[pos].dl = dictionary_create_advanced( DICT_OPTION_SINGLE_THREADED | DICT_OPTION_FIXED_SIZE | DICT_OPTION_DONT_OVERWRITE_VALUE, NULL, sizeof(struct group_by_label_key)); dictionary_register_insert_callback(entries[pos].dl, group_by_label_key_insert_cb, label_keys); dictionary_register_delete_callback(entries[pos].dl, group_by_label_key_delete_cb, label_keys); } } else { // the key found in the dictionary pos = *set; } entries[pos].count++; if (unlikely(priority < entries[pos].priority)) entries[pos].priority = priority; if(g > 0) last_r->dgbs[qm->grouped_as.slot] = pos; else qm->grouped_as.first_slot = pos; qm->grouped_as.slot = pos; qm->grouped_as.id = entries[pos].id; qm->grouped_as.name = entries[pos].name; qm->grouped_as.units = entries[pos].units; // copy the dimension flags decided by the query target // we need this, because if a dimension is explicitly selected // the query target adds to it the non-zero flag qm->status |= RRDR_DIMENSION_GROUPED; if(query_has_percentage_of_group) // when the query has percentage of group // there will be no hidden dimensions in the final query, // so we have to remove the hidden flag from all dimensions entries[pos].od |= qm->status & ~RRDR_DIMENSION_HIDDEN; else entries[pos].od |= qm->status; if (entries[pos].dl) rrdlabels_walkthrough_read(rrdinstance_acquired_labels(qi->ria), rrdlabels_traversal_cb_to_group_by_label_key, entries[pos].dl); } RRDR *r = rrdr_create(owa, qt, added, qt->window.points); if (!r) { internal_error(true, "QUERY: cannot create group by RRDR for %s, after=%ld, before=%ld, dimensions=%d, points=%zu", qt->id, qt->window.after, qt->window.before, added, qt->window.points); goto cleanup; } // prevent double free at cleanup in case of error added = 0; // link this RRDR if(!last_r) first_r = last_r = r; else last_r->group_by.r = r; last_r = r; rrd2rrdr_set_timestamps(r); r->dp = onewayalloc_callocz(owa, r->d, sizeof(*r->dp)); r->dview = onewayalloc_callocz(owa, r->d, sizeof(*r->dview)); r->dgbc = onewayalloc_callocz(owa, r->d, sizeof(*r->dgbc)); r->gbc = onewayalloc_callocz(owa, r->n * r->d, sizeof(*r->gbc)); r->dqp = onewayalloc_callocz(owa, r->d, sizeof(STORAGE_POINT)); if(hidden_dimensions && ((group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE) || (aggregation_method == RRDR_GROUP_BY_FUNCTION_PERCENTAGE))) // this is where we are going to group the hidden dimensions r->vh = onewayalloc_mallocz(owa, r->n * r->d * sizeof(*r->vh)); if(!final_grouping) // this is where we are going to store the slot in the next RRDR // that we are going to group by the dimension of this RRDR r->dgbs = onewayalloc_callocz(owa, r->d, sizeof(*r->dgbs)); if (label_keys) { r->dl = onewayalloc_callocz(owa, r->d, sizeof(DICTIONARY *)); r->label_keys = label_keys; label_keys = NULL; } // zero r (dimension options, names, and ids) // this is required, because group-by may lead to empty dimensions for (size_t d = 0; d < r->d; d++) { r->di[d] = entries[d].id; r->dn[d] = entries[d].name; r->od[d] = entries[d].od; r->du[d] = entries[d].units; r->dp[d] = entries[d].priority; r->dgbc[d] = entries[d].count; if (r->dl) r->dl[d] = entries[d].dl; } // initialize partial trimming r->partial_data_trimming.max_update_every = update_every_max * 2; r->partial_data_trimming.expected_after = (!query_target_aggregatable(qt) && qt->window.before >= qt->window.now - r->partial_data_trimming.max_update_every) ? qt->window.before - r->partial_data_trimming.max_update_every : qt->window.before; r->partial_data_trimming.trimmed_after = qt->window.before; // make all values empty for (size_t i = 0; i != r->n; i++) { NETDATA_DOUBLE *cn = &r->v[i * r->d]; RRDR_VALUE_FLAGS *co = &r->o[i * r->d]; NETDATA_DOUBLE *ar = &r->ar[i * r->d]; NETDATA_DOUBLE *vh = r->vh ? &r->vh[i * r->d] : NULL; for (size_t d = 0; d < r->d; d++) { cn[d] = NAN; ar[d] = 0.0; co[d] = RRDR_VALUE_EMPTY; if(vh) vh[d] = NAN; } } } if(!first_r || !last_r) goto cleanup; r_tmp = rrdr_create(owa, qt, 1, qt->window.points); if (!r_tmp) { internal_error(true, "QUERY: cannot create group by temporary RRDR for %s, after=%ld, before=%ld, dimensions=%d, points=%zu", qt->id, qt->window.after, qt->window.before, 1, qt->window.points); goto cleanup; } rrd2rrdr_set_timestamps(r_tmp); r_tmp->group_by.r = first_r; cleanup: if(!first_r || !last_r || !r_tmp) { if(r_tmp) { r_tmp->group_by.r = NULL; rrdr_free(owa, r_tmp); } if(first_r) { RRDR *r = first_r; while (r) { r_tmp = r->group_by.r; r->group_by.r = NULL; rrdr_free(owa, r); r = r_tmp; } } if(entries && added) { for (int d = 0; d < added; d++) { string_freez(entries[d].id); string_freez(entries[d].name); string_freez(entries[d].units); dictionary_destroy(entries[d].dl); } } dictionary_destroy(label_keys); first_r = last_r = r_tmp = NULL; } buffer_free(key); onewayalloc_freez(owa, entries); dictionary_destroy(groups); return r_tmp; } static void rrd2rrdr_group_by_add_metric(RRDR *r_dst, size_t d_dst, RRDR *r_tmp, size_t d_tmp, RRDR_GROUP_BY_FUNCTION group_by_aggregate_function, STORAGE_POINT *query_points, size_t pass __maybe_unused) { if(!r_tmp || r_dst == r_tmp || !(r_tmp->od[d_tmp] & RRDR_DIMENSION_QUERIED)) return; internal_fatal(r_dst->n != r_tmp->n, "QUERY: group-by source and destination do not have the same number of rows"); internal_fatal(d_dst >= r_dst->d, "QUERY: group-by destination dimension number exceeds destination RRDR size"); internal_fatal(d_tmp >= r_tmp->d, "QUERY: group-by source dimension number exceeds source RRDR size"); internal_fatal(!r_dst->dqp, "QUERY: group-by destination is not properly prepared (missing dqp array)"); internal_fatal(!r_dst->gbc, "QUERY: group-by destination is not properly prepared (missing gbc array)"); bool hidden_dimension_on_percentage_of_group = (r_tmp->od[d_tmp] & RRDR_DIMENSION_HIDDEN) && r_dst->vh; if(!hidden_dimension_on_percentage_of_group) { r_dst->od[d_dst] |= r_tmp->od[d_tmp]; storage_point_merge_to(r_dst->dqp[d_dst], *query_points); } // do the group_by for(size_t i = 0; i != rrdr_rows(r_tmp) ; i++) { size_t idx_tmp = i * r_tmp->d + d_tmp; NETDATA_DOUBLE n_tmp = r_tmp->v[ idx_tmp ]; RRDR_VALUE_FLAGS o_tmp = r_tmp->o[ idx_tmp ]; NETDATA_DOUBLE ar_tmp = r_tmp->ar[ idx_tmp ]; if(o_tmp & RRDR_VALUE_EMPTY) continue; size_t idx_dst = i * r_dst->d + d_dst; NETDATA_DOUBLE *cn = (hidden_dimension_on_percentage_of_group) ? &r_dst->vh[ idx_dst ] : &r_dst->v[ idx_dst ]; RRDR_VALUE_FLAGS *co = &r_dst->o[ idx_dst ]; NETDATA_DOUBLE *ar = &r_dst->ar[ idx_dst ]; uint32_t *gbc = &r_dst->gbc[ idx_dst ]; switch(group_by_aggregate_function) { default: case RRDR_GROUP_BY_FUNCTION_AVERAGE: case RRDR_GROUP_BY_FUNCTION_SUM: case RRDR_GROUP_BY_FUNCTION_PERCENTAGE: if(isnan(*cn)) *cn = n_tmp; else *cn += n_tmp; break; case RRDR_GROUP_BY_FUNCTION_MIN: if(isnan(*cn) || n_tmp < *cn) *cn = n_tmp; break; case RRDR_GROUP_BY_FUNCTION_MAX: if(isnan(*cn) || n_tmp > *cn) *cn = n_tmp; break; } if(!hidden_dimension_on_percentage_of_group) { *co &= ~RRDR_VALUE_EMPTY; *co |= (o_tmp & (RRDR_VALUE_RESET | RRDR_VALUE_PARTIAL)); *ar += ar_tmp; (*gbc)++; } } } static void rrdr2rrdr_group_by_partial_trimming(RRDR *r) { time_t trimmable_after = r->partial_data_trimming.expected_after; // find the point just before the trimmable ones ssize_t i = (ssize_t)r->n - 1; for( ; i >= 0 ;i--) { if (r->t[i] < trimmable_after) break; } if(unlikely(i < 0)) return; // internal_error(true, "Found trimmable index %zd (from 0 to %zu)", i, r->n - 1); size_t last_row_gbc = 0; for (; i < (ssize_t)r->n; i++) { size_t row_gbc = 0; for (size_t d = 0; d < r->d; d++) { if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED))) continue; row_gbc += r->gbc[ i * r->d + d ]; } // internal_error(true, "GBC of index %zd is %zu", i, row_gbc); if (unlikely(r->t[i] >= trimmable_after && (row_gbc < last_row_gbc || !row_gbc))) { // discard the rest of the points // internal_error(true, "Discarding points %zd to %zu", i, r->n - 1); r->partial_data_trimming.trimmed_after = r->t[i]; r->rows = i; break; } else last_row_gbc = row_gbc; } } static void rrdr2rrdr_group_by_calculate_percentage_of_group(RRDR *r) { if(!r->vh) return; if(query_target_aggregatable(r->internal.qt) && query_has_group_by_aggregation_percentage(r->internal.qt)) return; for(size_t i = 0; i < r->n ;i++) { NETDATA_DOUBLE *cn = &r->v[ i * r->d ]; NETDATA_DOUBLE *ch = &r->vh[ i * r->d ]; for(size_t d = 0; d < r->d ;d++) { NETDATA_DOUBLE n = cn[d]; NETDATA_DOUBLE h = ch[d]; if(isnan(n)) cn[d] = 0.0; else if(isnan(h)) cn[d] = 100.0; else cn[d] = n * 100.0 / (n + h); } } } static void rrd2rrdr_convert_values_to_percentage_of_total(RRDR *r) { if(!(r->internal.qt->window.options & RRDR_OPTION_PERCENTAGE) || query_target_aggregatable(r->internal.qt)) return; size_t global_min_max_values = 0; NETDATA_DOUBLE global_min = NAN, global_max = NAN; for(size_t i = 0; i != r->n ;i++) { NETDATA_DOUBLE *cn = &r->v[ i * r->d ]; RRDR_VALUE_FLAGS *co = &r->o[ i * r->d ]; NETDATA_DOUBLE total = 0; for (size_t d = 0; d < r->d; d++) { if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED))) continue; if(co[d] & RRDR_VALUE_EMPTY) continue; total += cn[d]; } if(total == 0.0) total = 1.0; for (size_t d = 0; d < r->d; d++) { if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED))) continue; if(co[d] & RRDR_VALUE_EMPTY) continue; NETDATA_DOUBLE n = cn[d]; n = cn[d] = n * 100.0 / total; if(unlikely(!global_min_max_values++)) global_min = global_max = n; else { if(n < global_min) global_min = n; if(n > global_max) global_max = n; } } } r->view.min = global_min; r->view.max = global_max; if(!r->dview) // v1 query return; // v2 query for (size_t d = 0; d < r->d; d++) { if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED))) continue; size_t count = 0; NETDATA_DOUBLE min = 0.0, max = 0.0, sum = 0.0, ars = 0.0; for(size_t i = 0; i != r->rows ;i++) { // we use r->rows to respect trimming size_t idx = i * r->d + d; RRDR_VALUE_FLAGS o = r->o[ idx ]; if (o & RRDR_VALUE_EMPTY) continue; NETDATA_DOUBLE ar = r->ar[ idx ]; ars += ar; NETDATA_DOUBLE n = r->v[ idx ]; sum += n; if(!count++) min = max = n; else { if(n < min) min = n; if(n > max) max = n; } } r->dview[d] = (STORAGE_POINT) { .sum = sum, .count = count, .min = min, .max = max, .anomaly_count = (size_t)(ars * (NETDATA_DOUBLE)count), }; } } static RRDR *rrd2rrdr_group_by_finalize(RRDR *r_tmp) { QUERY_TARGET *qt = r_tmp->internal.qt; if(!r_tmp->group_by.r) { // v1 query rrd2rrdr_convert_values_to_percentage_of_total(r_tmp); return r_tmp; } // v2 query // do the additional passes on RRDRs RRDR *last_r = r_tmp->group_by.r; rrdr2rrdr_group_by_calculate_percentage_of_group(last_r); RRDR *r = last_r->group_by.r; size_t pass = 0; while(r) { pass++; for(size_t d = 0; d < last_r->d ;d++) { rrd2rrdr_group_by_add_metric(r, last_r->dgbs[d], last_r, d, qt->request.group_by[pass].aggregation, &last_r->dqp[d], pass); } rrdr2rrdr_group_by_calculate_percentage_of_group(r); last_r = r; r = last_r->group_by.r; } // free all RRDRs except the last one r = r_tmp; while(r != last_r) { r_tmp = r->group_by.r; r->group_by.r = NULL; rrdr_free(r->internal.owa, r); r = r_tmp; } r = last_r; // find the final aggregation RRDR_GROUP_BY_FUNCTION aggregation = qt->request.group_by[0].aggregation; for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) if(qt->request.group_by[g].group_by != RRDR_GROUP_BY_NONE) aggregation = qt->request.group_by[g].aggregation; if(!query_target_aggregatable(qt) && r->partial_data_trimming.expected_after < qt->window.before) rrdr2rrdr_group_by_partial_trimming(r); // apply averaging, remove RRDR_VALUE_EMPTY, find the non-zero dimensions, min and max size_t global_min_max_values = 0; size_t dimensions_nonzero = 0; NETDATA_DOUBLE global_min = NAN, global_max = NAN; for (size_t d = 0; d < r->d; d++) { if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED))) continue; size_t points_nonzero = 0; NETDATA_DOUBLE min = 0, max = 0, sum = 0, ars = 0; size_t count = 0; for(size_t i = 0; i != r->n ;i++) { size_t idx = i * r->d + d; NETDATA_DOUBLE *cn = &r->v[ idx ]; RRDR_VALUE_FLAGS *co = &r->o[ idx ]; NETDATA_DOUBLE *ar = &r->ar[ idx ]; uint32_t gbc = r->gbc[ idx ]; if(likely(gbc)) { *co &= ~RRDR_VALUE_EMPTY; if(gbc != r->dgbc[d]) *co |= RRDR_VALUE_PARTIAL; NETDATA_DOUBLE n; sum += *cn; ars += *ar; if(aggregation == RRDR_GROUP_BY_FUNCTION_AVERAGE && !query_target_aggregatable(qt)) n = (*cn /= gbc); else n = *cn; if(!query_target_aggregatable(qt)) *ar /= gbc; if(islessgreater(n, 0.0)) points_nonzero++; if(unlikely(!count)) min = max = n; else { if(n < min) min = n; if(n > max) max = n; } if(unlikely(!global_min_max_values++)) global_min = global_max = n; else { if(n < global_min) global_min = n; if(n > global_max) global_max = n; } count += gbc; } } if(points_nonzero) { r->od[d] |= RRDR_DIMENSION_NONZERO; dimensions_nonzero++; } r->dview[d] = (STORAGE_POINT) { .sum = sum, .count = count, .min = min, .max = max, .anomaly_count = (size_t)(ars * RRDR_DVIEW_ANOMALY_COUNT_MULTIPLIER / 100.0), }; } r->view.min = global_min; r->view.max = global_max; if(!dimensions_nonzero && (qt->window.options & RRDR_OPTION_NONZERO)) { // all dimensions are zero // remove the nonzero option qt->window.options &= ~RRDR_OPTION_NONZERO; } rrd2rrdr_convert_values_to_percentage_of_total(r); // update query instance counts in query host and query context { size_t h = 0, c = 0, i = 0; for(; h < qt->nodes.used ; h++) { QUERY_NODE *qn = &qt->nodes.array[h]; for(; c < qt->contexts.used ;c++) { QUERY_CONTEXT *qc = &qt->contexts.array[c]; if(!rrdcontext_acquired_belongs_to_host(qc->rca, qn->rrdhost)) break; for(; i < qt->instances.used ;i++) { QUERY_INSTANCE *qi = &qt->instances.array[i]; if(!rrdinstance_acquired_belongs_to_context(qi->ria, qc->rca)) break; if(qi->metrics.queried) { qc->instances.queried++; qn->instances.queried++; } else if(qi->metrics.failed) { qc->instances.failed++; qn->instances.failed++; } } } } } return r; } // ---------------------------------------------------------------------------- // query entry point RRDR *rrd2rrdr_legacy( ONEWAYALLOC *owa, RRDSET *st, size_t points, time_t after, time_t before, RRDR_TIME_GROUPING group_method, time_t resampling_time, RRDR_OPTIONS options, const char *dimensions, const char *group_options, time_t timeout_ms, size_t tier, QUERY_SOURCE query_source, STORAGE_PRIORITY priority) { QUERY_TARGET_REQUEST qtr = { .version = 1, .st = st, .points = points, .after = after, .before = before, .time_group_method = group_method, .resampling_time = resampling_time, .options = options, .dimensions = dimensions, .time_group_options = group_options, .timeout_ms = timeout_ms, .tier = tier, .query_source = query_source, .priority = priority, }; QUERY_TARGET *qt = query_target_create(&qtr); RRDR *r = rrd2rrdr(owa, qt); if(!r) { query_target_release(qt); return NULL; } r->internal.release_with_rrdr_qt = qt; return r; } RRDR *rrd2rrdr(ONEWAYALLOC *owa, QUERY_TARGET *qt) { if(!qt || !owa) return NULL; // qt.window members are the WANTED ones. // qt.request members are the REQUESTED ones. RRDR *r_tmp = rrd2rrdr_group_by_initialize(owa, qt); if(!r_tmp) return NULL; // the RRDR we group-by at RRDR *r = (r_tmp->group_by.r) ? r_tmp->group_by.r : r_tmp; // the final RRDR to return to callers RRDR *last_r = r_tmp; while(last_r->group_by.r) last_r = last_r->group_by.r; if(qt->window.relative) last_r->view.flags |= RRDR_RESULT_FLAG_RELATIVE; else last_r->view.flags |= RRDR_RESULT_FLAG_ABSOLUTE; // ------------------------------------------------------------------------- // assign the processor functions rrdr_set_grouping_function(r_tmp, qt->window.time_group_method); // allocate any memory required by the grouping method r_tmp->time_grouping.create(r_tmp, qt->window.time_group_options); // ------------------------------------------------------------------------- // do the work for each dimension time_t max_after = 0, min_before = 0; size_t max_rows = 0; long dimensions_used = 0, dimensions_nonzero = 0; size_t last_db_points_read = 0; size_t last_result_points_generated = 0; internal_fatal(released_ops, "QUERY: released_ops should be NULL when the query starts"); QUERY_ENGINE_OPS **ops = NULL; if(qt->query.used) ops = onewayalloc_callocz(owa, qt->query.used, sizeof(QUERY_ENGINE_OPS *)); size_t capacity = libuv_worker_threads * 10; size_t max_queries_to_prepare = (qt->query.used > (capacity - 1)) ? (capacity - 1) : qt->query.used; size_t queries_prepared = 0; while(queries_prepared < max_queries_to_prepare) { // preload another query ops[queries_prepared] = rrd2rrdr_query_ops_prep(r_tmp, queries_prepared); queries_prepared++; } QUERY_NODE *last_qn = NULL; usec_t last_ut = now_monotonic_usec(); usec_t last_qn_ut = last_ut; for(size_t d = 0; d < qt->query.used ; d++) { QUERY_METRIC *qm = query_metric(qt, d); QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id); QUERY_INSTANCE *qi = query_instance(qt, qm->link.query_instance_id); QUERY_CONTEXT *qc = query_context(qt, qm->link.query_context_id); QUERY_NODE *qn = query_node(qt, qm->link.query_node_id); usec_t now_ut = last_ut; if(qn != last_qn) { if(last_qn) last_qn->duration_ut = now_ut - last_qn_ut; last_qn = qn; last_qn_ut = now_ut; } if(queries_prepared < qt->query.used) { // preload another query ops[queries_prepared] = rrd2rrdr_query_ops_prep(r_tmp, queries_prepared); queries_prepared++; } size_t dim_in_rrdr_tmp = (r_tmp != r) ? 0 : d; // set the query target dimension options to rrdr r_tmp->od[dim_in_rrdr_tmp] = qm->status; // reset the grouping for the new dimension r_tmp->time_grouping.reset(r_tmp); if(ops[d]) { rrd2rrdr_query_execute(r_tmp, dim_in_rrdr_tmp, ops[d]); r_tmp->od[dim_in_rrdr_tmp] |= RRDR_DIMENSION_QUERIED; now_ut = now_monotonic_usec(); qm->duration_ut = now_ut - last_ut; last_ut = now_ut; if(r_tmp != r) { // copy back whatever got updated from the temporary r // the query updates RRDR_DIMENSION_NONZERO qm->status = r_tmp->od[dim_in_rrdr_tmp]; // the query updates these r->view.min = r_tmp->view.min; r->view.max = r_tmp->view.max; r->view.after = r_tmp->view.after; r->view.before = r_tmp->view.before; r->rows = r_tmp->rows; rrd2rrdr_group_by_add_metric(r, qm->grouped_as.first_slot, r_tmp, dim_in_rrdr_tmp, qt->request.group_by[0].aggregation, &qm->query_points, 0); } rrd2rrdr_query_ops_release(ops[d]); // reuse this ops allocation ops[d] = NULL; qi->metrics.queried++; qc->metrics.queried++; qn->metrics.queried++; qd->status |= QUERY_STATUS_QUERIED; qm->status |= RRDR_DIMENSION_QUERIED; if(qt->request.version >= 2) { // we need to make the query points positive now // since we will aggregate it across multiple dimensions storage_point_make_positive(qm->query_points); storage_point_merge_to(qi->query_points, qm->query_points); storage_point_merge_to(qc->query_points, qm->query_points); storage_point_merge_to(qn->query_points, qm->query_points); storage_point_merge_to(qt->query_points, qm->query_points); } } else { qi->metrics.failed++; qc->metrics.failed++; qn->metrics.failed++; qd->status |= QUERY_STATUS_FAILED; qm->status |= RRDR_DIMENSION_FAILED; continue; } global_statistics_rrdr_query_completed( 1, r_tmp->stats.db_points_read - last_db_points_read, r_tmp->stats.result_points_generated - last_result_points_generated, qt->request.query_source); last_db_points_read = r_tmp->stats.db_points_read; last_result_points_generated = r_tmp->stats.result_points_generated; if(qm->status & RRDR_DIMENSION_NONZERO) dimensions_nonzero++; // verify all dimensions are aligned if(unlikely(!dimensions_used)) { min_before = r->view.before; max_after = r->view.after; max_rows = r->rows; } else { if(r->view.after != max_after) { internal_error(true, "QUERY: 'after' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu", rrdinstance_acquired_id(qi->ria), (size_t)max_after, rrdmetric_acquired_id(qd->rma), (size_t)r->view.after); r->view.after = (r->view.after > max_after) ? r->view.after : max_after; } if(r->view.before != min_before) { internal_error(true, "QUERY: 'before' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu", rrdinstance_acquired_id(qi->ria), (size_t)min_before, rrdmetric_acquired_id(qd->rma), (size_t)r->view.before); r->view.before = (r->view.before < min_before) ? r->view.before : min_before; } if(r->rows != max_rows) { internal_error(true, "QUERY: 'rows' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu", rrdinstance_acquired_id(qi->ria), (size_t)max_rows, rrdmetric_acquired_id(qd->rma), (size_t)r->rows); r->rows = (r->rows > max_rows) ? r->rows : max_rows; } } dimensions_used++; bool cancel = false; if (qt->request.interrupt_callback && qt->request.interrupt_callback(qt->request.interrupt_callback_data)) { cancel = true; netdata_log_access("QUERY INTERRUPTED"); } if (qt->request.timeout_ms && ((NETDATA_DOUBLE)(now_ut - qt->timings.received_ut) / 1000.0) > (NETDATA_DOUBLE)qt->request.timeout_ms) { cancel = true; netdata_log_access("QUERY CANCELED RUNTIME EXCEEDED %0.2f ms (LIMIT %lld ms)", (NETDATA_DOUBLE)(now_ut - qt->timings.received_ut) / 1000.0, (long long)qt->request.timeout_ms); } if(cancel) { r->view.flags |= RRDR_RESULT_FLAG_CANCEL; for(size_t i = d + 1; i < queries_prepared ; i++) { if(ops[i]) { query_planer_finalize_remaining_plans(ops[i]); rrd2rrdr_query_ops_release(ops[i]); ops[i] = NULL; } } break; } } // free all resources used by the grouping method r_tmp->time_grouping.free(r_tmp); // get the final RRDR to send to the caller r = rrd2rrdr_group_by_finalize(r_tmp); #ifdef NETDATA_INTERNAL_CHECKS if (dimensions_used && !(r->view.flags & RRDR_RESULT_FLAG_CANCEL)) { if(r->internal.log) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ r->internal.log); if(r->rows != qt->window.points) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ "got 'points' is not wanted 'points'"); if(qt->window.aligned && (r->view.before % query_view_update_every(qt)) != 0) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ "'before' is not aligned but alignment is required"); // 'after' should not be aligned, since we start inside the first group //if(qt->window.aligned && (r->after % group) != 0) // rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, after_requested, before_wanted, before_requested, points_requested, points_wanted, after_slot, before_slot, "'after' is not aligned but alignment is required"); if(r->view.before != qt->window.before) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ "chart is not aligned to requested 'before'"); if(r->view.before != qt->window.before) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ "got 'before' is not wanted 'before'"); // reported 'after' varies, depending on group if(r->view.after != qt->window.after) rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, qt->request.after, qt->window.before, qt->request.before, qt->request.points, qt->window.points, /*after_slot, before_slot,*/ "got 'after' is not wanted 'after'"); } #endif // free the query pipelining ops for(size_t d = 0; d < qt->query.used ; d++) { rrd2rrdr_query_ops_release(ops[d]); ops[d] = NULL; } rrd2rrdr_query_ops_freeall(r); internal_fatal(released_ops, "QUERY: released_ops should be NULL when the query ends"); onewayalloc_freez(owa, ops); if(likely(dimensions_used && (qt->window.options & RRDR_OPTION_NONZERO) && !dimensions_nonzero)) // when all the dimensions are zero, we should return all of them qt->window.options &= ~RRDR_OPTION_NONZERO; qt->timings.executed_ut = now_monotonic_usec(); return r; }