Adding upstream version 1.37.1.upstream/1.37.1upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 1107 insertions, 0 deletions

diff --git a/web/api/queries/weights.c b/web/api/queries/weights.c
new file mode 100644
index 0000000..a9555a6
--- /dev/null
+++ b/web/api/queries/weights.c
@@ -0,0 +1,1107 @@
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#include "daemon/common.h"
+#include "database/KolmogorovSmirnovDist.h"
+
+#define MAX_POINTS 10000
+int enable_metric_correlations = CONFIG_BOOLEAN_YES;
+int metric_correlations_version = 1;
+WEIGHTS_METHOD default_metric_correlations_method = WEIGHTS_METHOD_MC_KS2;
+
+typedef struct weights_stats {
+    NETDATA_DOUBLE max_base_high_ratio;
+    size_t db_points;
+    size_t result_points;
+    size_t db_queries;
+    size_t db_points_per_tier[RRD_STORAGE_TIERS];
+    size_t binary_searches;
+} WEIGHTS_STATS;
+
+// ----------------------------------------------------------------------------
+// parse and render metric correlations methods
+
+static struct {
+    const char *name;
+    WEIGHTS_METHOD value;
+} weights_methods[] = {
+      { "ks2"          , WEIGHTS_METHOD_MC_KS2}
+    , { "volume"       , WEIGHTS_METHOD_MC_VOLUME}
+    , { "anomaly-rate" , WEIGHTS_METHOD_ANOMALY_RATE}
+    , { NULL           , 0 }
+};
+
+WEIGHTS_METHOD weights_string_to_method(const char *method) {
+    for(int i = 0; weights_methods[i].name ;i++)
+        if(strcmp(method, weights_methods[i].name) == 0)
+            return weights_methods[i].value;
+
+    return default_metric_correlations_method;
+}
+
+const char *weights_method_to_string(WEIGHTS_METHOD method) {
+    for(int i = 0; weights_methods[i].name ;i++)
+        if(weights_methods[i].value == method)
+            return weights_methods[i].name;
+
+    return "unknown";
+}
+
+// ----------------------------------------------------------------------------
+// The results per dimension are aggregated into a dictionary
+
+typedef enum {
+    RESULT_IS_BASE_HIGH_RATIO     = (1 << 0),
+    RESULT_IS_PERCENTAGE_OF_TIME  = (1 << 1),
+} RESULT_FLAGS;
+
+struct register_result {
+    RESULT_FLAGS flags;
+    RRDCONTEXT_ACQUIRED *rca;
+    RRDINSTANCE_ACQUIRED *ria;
+    RRDMETRIC_ACQUIRED *rma;
+    NETDATA_DOUBLE value;
+};
+
+static DICTIONARY *register_result_init() {
+    DICTIONARY *results = dictionary_create(DICT_OPTION_SINGLE_THREADED);
+    return results;
+}
+
+static void register_result_destroy(DICTIONARY *results) {
+    dictionary_destroy(results);
+}
+
+static void register_result(DICTIONARY *results,
+                            RRDCONTEXT_ACQUIRED *rca,
+                            RRDINSTANCE_ACQUIRED *ria,
+                            RRDMETRIC_ACQUIRED *rma,
+                            NETDATA_DOUBLE value,
+                            RESULT_FLAGS flags,
+                            WEIGHTS_STATS *stats,
+                            bool register_zero) {
+
+    if(!netdata_double_isnumber(value)) return;
+
+    // make it positive
+    NETDATA_DOUBLE v = fabsndd(value);
+
+    // no need to store zero scored values
+    if(unlikely(fpclassify(v) == FP_ZERO && !register_zero))
+        return;
+
+    // keep track of the max of the baseline / highlight ratio
+    if(flags & RESULT_IS_BASE_HIGH_RATIO && v > stats->max_base_high_ratio)
+        stats->max_base_high_ratio = v;
+
+    struct register_result t = {
+        .flags = flags,
+        .rca = rca,
+        .ria = ria,
+        .rma = rma,
+        .value = v
+    };
+
+    // we can use the pointer address or RMA as a unique key for each metric
+    char buf[20 + 1];
+    ssize_t len = snprintfz(buf, 20, "%p", rma);
+    dictionary_set_advanced(results, buf, len + 1, &t, sizeof(struct register_result), NULL);
+}
+
+// ----------------------------------------------------------------------------
+// Generation of JSON output for the results
+
+static void results_header_to_json(DICTIONARY *results __maybe_unused, BUFFER *wb,
+                                   time_t after, time_t before,
+                                   time_t baseline_after, time_t baseline_before,
+                                   size_t points, WEIGHTS_METHOD method,
+                                   RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
+                                   size_t examined_dimensions __maybe_unused, usec_t duration,
+                                   WEIGHTS_STATS *stats) {
+
+    buffer_sprintf(wb, "{\n"
+                       "\t\"after\": %lld,\n"
+                       "\t\"before\": %lld,\n"
+                       "\t\"duration\": %lld,\n"
+                       "\t\"points\": %zu,\n",
+                       (long long)after,
+                       (long long)before,
+                       (long long)(before - after),
+                       points
+                       );
+
+    if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME)
+        buffer_sprintf(wb, ""
+                           "\t\"baseline_after\": %lld,\n"
+                           "\t\"baseline_before\": %lld,\n"
+                           "\t\"baseline_duration\": %lld,\n"
+                           "\t\"baseline_points\": %zu,\n",
+                           (long long)baseline_after,
+                           (long long)baseline_before,
+                           (long long)(baseline_before - baseline_after),
+                           points << shifts
+                       );
+
+    buffer_sprintf(wb, ""
+                       "\t\"statistics\": {\n"
+                       "\t\t\"query_time_ms\": %f,\n"
+                       "\t\t\"db_queries\": %zu,\n"
+                       "\t\t\"query_result_points\": %zu,\n"
+                       "\t\t\"binary_searches\": %zu,\n"
+                       "\t\t\"db_points_read\": %zu,\n"
+                       "\t\t\"db_points_per_tier\": [ ",
+                       (double)duration / (double)USEC_PER_MS,
+                       stats->db_queries,
+                       stats->result_points,
+                       stats->binary_searches,
+                       stats->db_points
+                   );
+
+    for(size_t tier = 0; tier < storage_tiers ;tier++)
+        buffer_sprintf(wb, "%s%zu", tier?", ":"", stats->db_points_per_tier[tier]);
+
+    buffer_sprintf(wb, " ]\n"
+                       "\t},\n"
+                       "\t\"group\": \"%s\",\n"
+                       "\t\"method\": \"%s\",\n"
+                       "\t\"options\": \"",
+                       web_client_api_request_v1_data_group_to_string(group),
+                       weights_method_to_string(method)
+                   );
+
+    web_client_api_request_v1_data_options_to_buffer(wb, options);
+}
+
+static size_t registered_results_to_json_charts(DICTIONARY *results, BUFFER *wb,
+                                                time_t after, time_t before,
+                                                time_t baseline_after, time_t baseline_before,
+                                                size_t points, WEIGHTS_METHOD method,
+                                                RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
+                                                size_t examined_dimensions, usec_t duration,
+                                                WEIGHTS_STATS *stats) {
+
+    results_header_to_json(results, wb, after, before, baseline_after, baseline_before,
+                           points, method, group, options, shifts, examined_dimensions, duration, stats);
+
+    buffer_strcat(wb, "\",\n\t\"correlated_charts\": {\n");
+
+    size_t charts = 0, chart_dims = 0, total_dimensions = 0;
+    struct register_result *t;
+    RRDINSTANCE_ACQUIRED *last_ria = NULL; // never access this - we use it only for comparison
+    dfe_start_read(results, t) {
+        if(t->ria != last_ria) {
+            last_ria = t->ria;
+
+            if(charts) buffer_strcat(wb, "\n\t\t\t}\n\t\t},\n");
+            buffer_strcat(wb, "\t\t\"");
+            buffer_strcat(wb, rrdinstance_acquired_id(t->ria));
+            buffer_strcat(wb, "\": {\n");
+            buffer_strcat(wb, "\t\t\t\"context\": \"");
+            buffer_strcat(wb, rrdcontext_acquired_id(t->rca));
+            buffer_strcat(wb, "\",\n\t\t\t\"dimensions\": {\n");
+            charts++;
+            chart_dims = 0;
+        }
+        if (chart_dims) buffer_sprintf(wb, ",\n");
+        buffer_sprintf(wb, "\t\t\t\t\"%s\": " NETDATA_DOUBLE_FORMAT, rrdmetric_acquired_name(t->rma), t->value);
+        chart_dims++;
+        total_dimensions++;
+    }
+    dfe_done(t);
+
+    // close dimensions and chart
+    if (total_dimensions)
+        buffer_strcat(wb, "\n\t\t\t}\n\t\t}\n");
+
+    // close correlated_charts
+    buffer_sprintf(wb, "\t},\n"
+                       "\t\"correlated_dimensions\": %zu,\n"
+                       "\t\"total_dimensions_count\": %zu\n"
+                       "}\n",
+                   total_dimensions,
+                   examined_dimensions
+                   );
+
+    return total_dimensions;
+}
+
+static size_t registered_results_to_json_contexts(DICTIONARY *results, BUFFER *wb,
+                                                  time_t after, time_t before,
+                                                  time_t baseline_after, time_t baseline_before,
+                                                  size_t points, WEIGHTS_METHOD method,
+                                                  RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
+                                                  size_t examined_dimensions, usec_t duration,
+                                                  WEIGHTS_STATS *stats) {
+
+    results_header_to_json(results, wb, after, before, baseline_after, baseline_before,
+                           points, method, group, options, shifts, examined_dimensions, duration, stats);
+
+    buffer_strcat(wb, "\",\n\t\"contexts\": {\n");
+
+    size_t contexts = 0, charts = 0, total_dimensions = 0, context_dims = 0, chart_dims = 0;
+    NETDATA_DOUBLE contexts_total_weight = 0.0, charts_total_weight = 0.0;
+    struct register_result *t;
+    RRDCONTEXT_ACQUIRED *last_rca = NULL;
+    RRDINSTANCE_ACQUIRED *last_ria = NULL;
+    dfe_start_read(results, t) {
+
+        if(t->rca != last_rca) {
+            last_rca = t->rca;
+
+            if(contexts)
+                buffer_sprintf(wb, "\n"
+                                   "\t\t\t\t\t},\n"
+                                   "\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
+                                   "\t\t\t\t}\n\t\t\t},\n"
+                                   "\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n\t\t},\n"
+                        , charts_total_weight / (double)chart_dims
+                        , contexts_total_weight / (double)context_dims);
+
+            buffer_strcat(wb, "\t\t\"");
+            buffer_strcat(wb, rrdcontext_acquired_id(t->rca));
+            buffer_strcat(wb, "\": {\n\t\t\t\"charts\":{\n");
+
+            contexts++;
+            charts = 0;
+            context_dims = 0;
+            contexts_total_weight = 0.0;
+
+            last_ria = NULL;
+        }
+
+        if(t->ria != last_ria) {
+            last_ria = t->ria;
+
+            if(charts)
+                buffer_sprintf(wb, "\n"
+                                   "\t\t\t\t\t},\n"
+                                   "\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
+                                   "\t\t\t\t},\n"
+                                   , charts_total_weight / (double)chart_dims);
+
+            buffer_strcat(wb, "\t\t\t\t\"");
+            buffer_strcat(wb, rrdinstance_acquired_id(t->ria));
+            buffer_strcat(wb, "\": {\n");
+            buffer_strcat(wb, "\t\t\t\t\t\"dimensions\": {\n");
+
+            charts++;
+            chart_dims = 0;
+            charts_total_weight = 0.0;
+        }
+
+        if (chart_dims) buffer_sprintf(wb, ",\n");
+        buffer_sprintf(wb, "\t\t\t\t\t\t\"%s\": " NETDATA_DOUBLE_FORMAT, rrdmetric_acquired_name(t->rma), t->value);
+        charts_total_weight += t->value;
+        contexts_total_weight += t->value;
+        chart_dims++;
+        context_dims++;
+        total_dimensions++;
+    }
+    dfe_done(t);
+
+    // close dimensions and chart
+    if (total_dimensions)
+        buffer_sprintf(wb, "\n"
+                           "\t\t\t\t\t},\n"
+                           "\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
+                           "\t\t\t\t}\n"
+                           "\t\t\t},\n"
+                           "\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
+                           "\t\t}\n"
+                           , charts_total_weight / (double)chart_dims
+                           , contexts_total_weight / (double)context_dims);
+
+    // close correlated_charts
+    buffer_sprintf(wb, "\t},\n"
+                       "\t\"weighted_dimensions\": %zu,\n"
+                       "\t\"total_dimensions_count\": %zu\n"
+                       "}\n",
+                   total_dimensions,
+                   examined_dimensions
+    );
+
+    return total_dimensions;
+}
+
+// ----------------------------------------------------------------------------
+// KS2 algorithm functions
+
+typedef long int DIFFS_NUMBERS;
+#define DOUBLE_TO_INT_MULTIPLIER 100000
+
+static inline int binary_search_bigger_than(const DIFFS_NUMBERS arr[], int left, int size, DIFFS_NUMBERS K) {
+    // binary search to find the index the smallest index
+    // of the first value in the array that is greater than K
+
+    int right = size;
+    while(left < right) {
+        int middle = (int)(((unsigned int)(left + right)) >> 1);
+
+        if(arr[middle] > K)
+            right = middle;
+
+        else
+            left = middle + 1;
+    }
+
+    return left;
+}
+
+int compare_diffs(const void *left, const void *right) {
+    DIFFS_NUMBERS lt = *(DIFFS_NUMBERS *)left;
+    DIFFS_NUMBERS rt = *(DIFFS_NUMBERS *)right;
+
+    // https://stackoverflow.com/a/3886497/1114110
+    return (lt > rt) - (lt < rt);
+}
+
+static size_t calculate_pairs_diff(DIFFS_NUMBERS *diffs, NETDATA_DOUBLE *arr, size_t size) {
+    NETDATA_DOUBLE *last = &arr[size - 1];
+    size_t added = 0;
+
+    while(last > arr) {
+        NETDATA_DOUBLE second = *last--;
+        NETDATA_DOUBLE first  = *last;
+        *diffs++ = (DIFFS_NUMBERS)((first - second) * (NETDATA_DOUBLE)DOUBLE_TO_INT_MULTIPLIER);
+        added++;
+    }
+
+    return added;
+}
+
+static double ks_2samp(
+        DIFFS_NUMBERS baseline_diffs[], int base_size,
+        DIFFS_NUMBERS highlight_diffs[], int high_size,
+        uint32_t base_shifts) {
+
+    qsort(baseline_diffs, base_size, sizeof(DIFFS_NUMBERS), compare_diffs);
+    qsort(highlight_diffs, high_size, sizeof(DIFFS_NUMBERS), compare_diffs);
+
+    // Now we should be calculating this:
+    //
+    // For each number in the diffs arrays, we should find the index of the
+    // number bigger than them in both arrays and calculate the % of this index
+    // vs the total array size. Once we have the 2 percentages, we should find
+    // the min and max across the delta of all of them.
+    //
+    // It should look like this:
+    //
+    // base_pcent = binary_search_bigger_than(...) / base_size;
+    // high_pcent = binary_search_bigger_than(...) / high_size;
+    // delta = base_pcent - high_pcent;
+    // if(delta < min) min = delta;
+    // if(delta > max) max = delta;
+    //
+    // This would require a lot of multiplications and divisions.
+    //
+    // To speed it up, we do the binary search to find the index of each number
+    // but, then we divide the base index by the power of two number (shifts) it
+    // is bigger than high index. So the 2 indexes are now comparable.
+    // We also keep track of the original indexes with min and max, to properly
+    // calculate their percentages once the loops finish.
+
+
+    // initialize min and max using the first number of baseline_diffs
+    DIFFS_NUMBERS K = baseline_diffs[0];
+    int base_idx = binary_search_bigger_than(baseline_diffs, 1, base_size, K);
+    int high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K);
+    int delta = base_idx - (high_idx << base_shifts);
+    int min = delta, max = delta;
+    int base_min_idx = base_idx;
+    int base_max_idx = base_idx;
+    int high_min_idx = high_idx;
+    int high_max_idx = high_idx;
+
+    // do the baseline_diffs starting from 1 (we did position 0 above)
+    for(int i = 1; i < base_size; i++) {
+        K = baseline_diffs[i];
+        base_idx = binary_search_bigger_than(baseline_diffs, i + 1, base_size, K); // starting from i, since data1 is sorted
+        high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K);
+
+        delta = base_idx - (high_idx << base_shifts);
+        if(delta < min) {
+            min = delta;
+            base_min_idx = base_idx;
+            high_min_idx = high_idx;
+        }
+        else if(delta > max) {
+            max = delta;
+            base_max_idx = base_idx;
+            high_max_idx = high_idx;
+        }
+    }
+
+    // do the highlight_diffs starting from 0
+    for(int i = 0; i < high_size; i++) {
+        K = highlight_diffs[i];
+        base_idx = binary_search_bigger_than(baseline_diffs, 0, base_size, K);
+        high_idx = binary_search_bigger_than(highlight_diffs, i + 1, high_size, K); // starting from i, since data2 is sorted
+
+        delta = base_idx - (high_idx << base_shifts);
+        if(delta < min) {
+            min = delta;
+            base_min_idx = base_idx;
+            high_min_idx = high_idx;
+        }
+        else if(delta > max) {
+            max = delta;
+            base_max_idx = base_idx;
+            high_max_idx = high_idx;
+        }
+    }
+
+    // now we have the min, max and their indexes
+    // properly calculate min and max as dmin and dmax
+    double dbase_size = (double)base_size;
+    double dhigh_size = (double)high_size;
+    double dmin = ((double)base_min_idx / dbase_size) - ((double)high_min_idx / dhigh_size);
+    double dmax = ((double)base_max_idx / dbase_size) - ((double)high_max_idx / dhigh_size);
+
+    dmin = -dmin;
+    if(islessequal(dmin, 0.0)) dmin = 0.0;
+    else if(isgreaterequal(dmin, 1.0)) dmin = 1.0;
+
+    double d;
+    if(isgreaterequal(dmin, dmax)) d = dmin;
+    else d = dmax;
+
+    double en = round(dbase_size * dhigh_size / (dbase_size + dhigh_size));
+
+    // under these conditions, KSfbar() crashes
+    if(unlikely(isnan(en) || isinf(en) || en == 0.0 || isnan(d) || isinf(d)))
+        return NAN;
+
+    return KSfbar((int)en, d);
+}
+
+static double kstwo(
+    NETDATA_DOUBLE baseline[], int baseline_points,
+    NETDATA_DOUBLE highlight[], int highlight_points,
+    uint32_t base_shifts) {
+
+    // -1 in size, since the calculate_pairs_diffs() returns one less point
+    DIFFS_NUMBERS baseline_diffs[baseline_points - 1];
+    DIFFS_NUMBERS highlight_diffs[highlight_points - 1];
+
+    int base_size = (int)calculate_pairs_diff(baseline_diffs, baseline, baseline_points);
+    int high_size = (int)calculate_pairs_diff(highlight_diffs, highlight, highlight_points);
+
+    if(unlikely(!base_size || !high_size))
+        return NAN;
+
+    if(unlikely(base_size != baseline_points - 1 || high_size != highlight_points - 1)) {
+        error("Metric correlations: internal error - calculate_pairs_diff() returns the wrong number of entries");
+        return NAN;
+    }
+
+    return ks_2samp(baseline_diffs, base_size, highlight_diffs, high_size, base_shifts);
+}
+
+NETDATA_DOUBLE *rrd2rrdr_ks2(
+        ONEWAYALLOC *owa, RRDHOST *host,
+        RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
+        time_t after, time_t before, size_t points, RRDR_OPTIONS options,
+        RRDR_GROUPING group_method, const char *group_options, size_t tier,
+        WEIGHTS_STATS *stats,
+        size_t *entries
+        ) {
+
+    NETDATA_DOUBLE *ret = NULL;
+
+    QUERY_TARGET_REQUEST qtr = {
+            .host = host,
+            .rca = rca,
+            .ria = ria,
+            .rma = rma,
+            .after = after,
+            .before = before,
+            .points = points,
+            .options = options,
+            .group_method = group_method,
+            .group_options = group_options,
+            .tier = tier,
+            .query_source = QUERY_SOURCE_API_WEIGHTS,
+    };
+
+    RRDR *r = rrd2rrdr(owa, query_target_create(&qtr));
+    if(!r)
+        goto cleanup;
+
+    stats->db_queries++;
+    stats->result_points += r->internal.result_points_generated;
+    stats->db_points += r->internal.db_points_read;
+    for(size_t tr = 0; tr < storage_tiers ; tr++)
+        stats->db_points_per_tier[tr] += r->internal.tier_points_read[tr];
+
+    if(r->d != 1) {
+        error("WEIGHTS: on query '%s' expected 1 dimension in RRDR but got %zu", r->internal.qt->id, r->d);
+        goto cleanup;
+    }
+
+    if(unlikely(r->od[0] & RRDR_DIMENSION_HIDDEN))
+        goto cleanup;
+
+    if(unlikely(!(r->od[0] & RRDR_DIMENSION_NONZERO)))
+        goto cleanup;
+
+    if(rrdr_rows(r) < 2)
+        goto cleanup;
+
+    *entries = rrdr_rows(r);
+    ret = onewayalloc_mallocz(owa, sizeof(NETDATA_DOUBLE) * rrdr_rows(r));
+
+    // copy the points of the dimension to a contiguous array
+    // there is no need to check for empty values, since empty values are already zero
+    // https://github.com/netdata/netdata/blob/6e3144683a73a2024d51425b20ecfd569034c858/web/api/queries/average/average.c#L41-L43
+    memcpy(ret, r->v, rrdr_rows(r) * sizeof(NETDATA_DOUBLE));
+
+cleanup:
+    rrdr_free(owa, r);
+    return ret;
+}
+
+static void rrdset_metric_correlations_ks2(
+        RRDHOST *host,
+        RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
+        DICTIONARY *results,
+        time_t baseline_after, time_t baseline_before,
+        time_t after, time_t before,
+        size_t points, RRDR_OPTIONS options,
+        RRDR_GROUPING group_method, const char *group_options, size_t tier,
+        uint32_t shifts,
+        WEIGHTS_STATS *stats, bool register_zero
+        ) {
+
+    options |= RRDR_OPTION_NATURAL_POINTS;
+
+    ONEWAYALLOC *owa = onewayalloc_create(16 * 1024);
+
+    size_t high_points = 0;
+    NETDATA_DOUBLE *highlight = rrd2rrdr_ks2(
+            owa, host, rca, ria, rma, after, before, points,
+            options, group_method, group_options, tier, stats, &high_points);
+
+    if(!highlight)
+        goto cleanup;
+
+    size_t base_points = 0;
+    NETDATA_DOUBLE *baseline = rrd2rrdr_ks2(
+            owa, host, rca, ria, rma, baseline_after, baseline_before, high_points << shifts,
+            options, group_method, group_options, tier, stats, &base_points);
+
+    if(!baseline)
+        goto cleanup;
+
+    stats->binary_searches += 2 * (base_points - 1) + 2 * (high_points - 1);
+
+    double prob = kstwo(baseline, (int)base_points, highlight, (int)high_points, shifts);
+    if(!isnan(prob) && !isinf(prob)) {
+
+        // these conditions should never happen, but still let's check
+        if(unlikely(prob < 0.0)) {
+            error("Metric correlations: kstwo() returned a negative number: %f", prob);
+            prob = -prob;
+        }
+        if(unlikely(prob > 1.0)) {
+            error("Metric correlations: kstwo() returned a number above 1.0: %f", prob);
+            prob = 1.0;
+        }
+
+        // to spread the results evenly, 0.0 needs to be the less correlated and 1.0 the most correlated
+        // so, we flip the result of kstwo()
+        register_result(results, rca, ria, rma, 1.0 - prob, RESULT_IS_BASE_HIGH_RATIO, stats, register_zero);
+    }
+
+cleanup:
+    onewayalloc_destroy(owa);
+}
+
+// ----------------------------------------------------------------------------
+// VOLUME algorithm functions
+
+static void merge_query_value_to_stats(QUERY_VALUE *qv, WEIGHTS_STATS *stats) {
+    stats->db_queries++;
+    stats->result_points += qv->result_points;
+    stats->db_points += qv->points_read;
+    for(size_t tier = 0; tier < storage_tiers ; tier++)
+        stats->db_points_per_tier[tier] += qv->storage_points_per_tier[tier];
+}
+
+static void rrdset_metric_correlations_volume(
+        RRDHOST *host,
+        RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
+        DICTIONARY *results,
+        time_t baseline_after, time_t baseline_before,
+        time_t after, time_t before,
+        RRDR_OPTIONS options, RRDR_GROUPING group_method, const char *group_options,
+        size_t tier,
+        WEIGHTS_STATS *stats, bool register_zero) {
+
+    options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_ABSOLUTE | RRDR_OPTION_NATURAL_POINTS;
+
+    QUERY_VALUE baseline_average = rrdmetric2value(host, rca, ria, rma, baseline_after, baseline_before, options, group_method, group_options, tier, 0, QUERY_SOURCE_API_WEIGHTS);
+    merge_query_value_to_stats(&baseline_average, stats);
+
+    if(!netdata_double_isnumber(baseline_average.value)) {
+        // this means no data for the baseline window, but we may have data for the highlighted one - assume zero
+        baseline_average.value = 0.0;
+    }
+
+    QUERY_VALUE highlight_average = rrdmetric2value(host, rca, ria, rma, after, before, options, group_method, group_options, tier, 0, QUERY_SOURCE_API_WEIGHTS);
+    merge_query_value_to_stats(&highlight_average, stats);
+
+    if(!netdata_double_isnumber(highlight_average.value))
+        return;
+
+    if(baseline_average.value == highlight_average.value) {
+        // they are the same - let's move on
+        return;
+    }
+
+    char highlight_countif_options[50 + 1];
+    snprintfz(highlight_countif_options, 50, "%s" NETDATA_DOUBLE_FORMAT, highlight_average.value < baseline_average.value ? "<" : ">", baseline_average.value);
+    QUERY_VALUE highlight_countif = rrdmetric2value(host, rca, ria, rma, after, before, options, RRDR_GROUPING_COUNTIF, highlight_countif_options, tier, 0, QUERY_SOURCE_API_WEIGHTS);
+    merge_query_value_to_stats(&highlight_countif, stats);
+
+    if(!netdata_double_isnumber(highlight_countif.value)) {
+        info("WEIGHTS: highlighted countif query failed, but highlighted average worked - strange...");
+        return;
+    }
+
+    // this represents the percentage of time
+    // the highlighted window was above/below the baseline window
+    // (above or below depending on their averages)
+    highlight_countif.value = highlight_countif.value / 100.0; // countif returns 0 - 100.0
+
+    RESULT_FLAGS flags;
+    NETDATA_DOUBLE pcent = NAN;
+    if(isgreater(baseline_average.value, 0.0) || isless(baseline_average.value, 0.0)) {
+        flags = RESULT_IS_BASE_HIGH_RATIO;
+        pcent = (highlight_average.value - baseline_average.value) / baseline_average.value * highlight_countif.value;
+    }
+    else {
+        flags = RESULT_IS_PERCENTAGE_OF_TIME;
+        pcent = highlight_countif.value;
+    }
+
+    register_result(results, rca, ria, rma, pcent, flags, stats, register_zero);
+}
+
+// ----------------------------------------------------------------------------
+// ANOMALY RATE algorithm functions
+
+static void rrdset_weights_anomaly_rate(
+        RRDHOST *host,
+        RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
+        DICTIONARY *results,
+        time_t after, time_t before,
+        RRDR_OPTIONS options, RRDR_GROUPING group_method, const char *group_options,
+        size_t tier,
+        WEIGHTS_STATS *stats, bool register_zero) {
+
+    options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_ANOMALY_BIT | RRDR_OPTION_NATURAL_POINTS;
+
+    QUERY_VALUE qv = rrdmetric2value(host, rca, ria, rma, after, before, options, group_method, group_options, tier, 0, QUERY_SOURCE_API_WEIGHTS);
+    merge_query_value_to_stats(&qv, stats);
+
+    if(netdata_double_isnumber(qv.value))
+        register_result(results, rca, ria, rma, qv.value, 0, stats, register_zero);
+}
+
+// ----------------------------------------------------------------------------
+
+int compare_netdata_doubles(const void *left, const void *right) {
+    NETDATA_DOUBLE lt = *(NETDATA_DOUBLE *)left;
+    NETDATA_DOUBLE rt = *(NETDATA_DOUBLE *)right;
+
+    // https://stackoverflow.com/a/3886497/1114110
+    return (lt > rt) - (lt < rt);
+}
+
+static inline int binary_search_bigger_than_netdata_double(const NETDATA_DOUBLE arr[], int left, int size, NETDATA_DOUBLE K) {
+    // binary search to find the index the smallest index
+    // of the first value in the array that is greater than K
+
+    int right = size;
+    while(left < right) {
+        int middle = (int)(((unsigned int)(left + right)) >> 1);
+
+        if(arr[middle] > K)
+            right = middle;
+
+        else
+            left = middle + 1;
+    }
+
+    return left;
+}
+
+// ----------------------------------------------------------------------------
+// spread the results evenly according to their value
+
+static size_t spread_results_evenly(DICTIONARY *results, WEIGHTS_STATS *stats) {
+    struct register_result *t;
+
+    // count the dimensions
+    size_t dimensions = dictionary_entries(results);
+    if(!dimensions) return 0;
+
+    if(stats->max_base_high_ratio == 0.0)
+        stats->max_base_high_ratio = 1.0;
+
+    // create an array of the right size and copy all the values in it
+    NETDATA_DOUBLE slots[dimensions];
+    dimensions = 0;
+    dfe_start_read(results, t) {
+        if(t->flags & (RESULT_IS_PERCENTAGE_OF_TIME))
+            t->value = t->value * stats->max_base_high_ratio;
+
+        slots[dimensions++] = t->value;
+    }
+    dfe_done(t);
+
+    // sort the array with the values of all dimensions
+    qsort(slots, dimensions, sizeof(NETDATA_DOUBLE), compare_netdata_doubles);
+
+    // skip the duplicates in the sorted array
+    NETDATA_DOUBLE last_value = NAN;
+    size_t unique_values = 0;
+    for(size_t i = 0; i < dimensions ;i++) {
+        if(likely(slots[i] != last_value))
+            slots[unique_values++] = last_value = slots[i];
+    }
+
+    // this cannot happen, but coverity thinks otherwise...
+    if(!unique_values)
+        unique_values = dimensions;
+
+    // calculate the weight of each slot, using the number of unique values
+    NETDATA_DOUBLE slot_weight = 1.0 / (NETDATA_DOUBLE)unique_values;
+
+    dfe_start_read(results, t) {
+        int slot = binary_search_bigger_than_netdata_double(slots, 0, (int)unique_values, t->value);
+        NETDATA_DOUBLE v = slot * slot_weight;
+        if(unlikely(v > 1.0)) v = 1.0;
+        v = 1.0 - v;
+        t->value = v;
+    }
+    dfe_done(t);
+
+    return dimensions;
+}
+
+// ----------------------------------------------------------------------------
+// The main function
+
+int web_api_v1_weights(
+        RRDHOST *host, BUFFER *wb, WEIGHTS_METHOD method, WEIGHTS_FORMAT format,
+        RRDR_GROUPING group, const char *group_options,
+        time_t baseline_after, time_t baseline_before,
+        time_t after, time_t before,
+        size_t points, RRDR_OPTIONS options, SIMPLE_PATTERN *contexts, size_t tier, size_t timeout) {
+
+    WEIGHTS_STATS stats = {};
+
+    DICTIONARY *results = register_result_init();
+    DICTIONARY *metrics = NULL;
+    char *error = NULL;
+    int resp = HTTP_RESP_OK;
+
+    // if the user didn't give a timeout
+    // assume 60 seconds
+    if(!timeout)
+        timeout = 60 * MSEC_PER_SEC;
+
+    // if the timeout is less than 1 second
+    // make it at least 1 second
+    if(timeout < (long)(1 * MSEC_PER_SEC))
+        timeout = 1 * MSEC_PER_SEC;
+
+    usec_t timeout_usec = timeout * USEC_PER_MS;
+    usec_t started_usec = now_realtime_usec();
+
+    if(!rrdr_relative_window_to_absolute(&after, &before))
+        buffer_no_cacheable(wb);
+
+    if (before <= after) {
+        resp = HTTP_RESP_BAD_REQUEST;
+        error = "Invalid selected time-range.";
+        goto cleanup;
+    }
+
+    uint32_t shifts = 0;
+    if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME) {
+        if(!points) points = 500;
+
+        if(baseline_before <= API_RELATIVE_TIME_MAX)
+            baseline_before += after;
+
+        rrdr_relative_window_to_absolute(&baseline_after, &baseline_before);
+
+        if (baseline_before <= baseline_after) {
+            resp = HTTP_RESP_BAD_REQUEST;
+            error = "Invalid baseline time-range.";
+            goto cleanup;
+        }
+
+        // baseline should be a power of two multiple of highlight
+        long long base_delta = baseline_before - baseline_after;
+        long long high_delta = before - after;
+        uint32_t multiplier = (uint32_t)round((double)base_delta / (double)high_delta);
+
+        // check if the multiplier is a power of two
+        // https://stackoverflow.com/a/600306/1114110
+        if((multiplier & (multiplier - 1)) != 0) {
+            // it is not power of two
+            // let's find the closest power of two
+            // https://stackoverflow.com/a/466242/1114110
+            multiplier--;
+            multiplier |= multiplier >> 1;
+            multiplier |= multiplier >> 2;
+            multiplier |= multiplier >> 4;
+            multiplier |= multiplier >> 8;
+            multiplier |= multiplier >> 16;
+            multiplier++;
+        }
+
+        // convert the multiplier to the number of shifts
+        // we need to do, to divide baseline numbers to match
+        // the highlight ones
+        while(multiplier > 1) {
+            shifts++;
+            multiplier = multiplier >> 1;
+        }
+
+        // if the baseline size will not comply to MAX_POINTS
+        // lower the window of the baseline
+        while(shifts && (points << shifts) > MAX_POINTS)
+            shifts--;
+
+        // if the baseline size still does not comply to MAX_POINTS
+        // lower the resolution of the highlight and the baseline
+        while((points << shifts) > MAX_POINTS)
+            points = points >> 1;
+
+        if(points < 15) {
+            resp = HTTP_RESP_BAD_REQUEST;
+            error = "Too few points available, at least 15 are needed.";
+            goto cleanup;
+        }
+
+        // adjust the baseline to be multiplier times bigger than the highlight
+        baseline_after = baseline_before - (high_delta << shifts);
+    }
+
+    size_t examined_dimensions = 0;
+
+    bool register_zero = true;
+    if(options & RRDR_OPTION_NONZERO) {
+        register_zero = false;
+        options &= ~RRDR_OPTION_NONZERO;
+    }
+
+    metrics = rrdcontext_all_metrics_to_dict(host, contexts);
+    struct metric_entry *me;
+
+    // for every metric_entry in the dictionary
+    dfe_start_read(metrics, me) {
+        usec_t now_usec = now_realtime_usec();
+        if(now_usec - started_usec > timeout_usec) {
+            error = "timed out";
+            resp = HTTP_RESP_GATEWAY_TIMEOUT;
+            goto cleanup;
+        }
+
+        examined_dimensions++;
+
+        switch(method) {
+            case WEIGHTS_METHOD_ANOMALY_RATE:
+                options |= RRDR_OPTION_ANOMALY_BIT;
+                rrdset_weights_anomaly_rate(
+                        host,
+                        me->rca, me->ria, me->rma,
+                        results,
+                        after, before,
+                        options, group, group_options, tier,
+                        &stats, register_zero
+                        );
+                break;
+
+            case WEIGHTS_METHOD_MC_VOLUME:
+                rrdset_metric_correlations_volume(
+                        host,
+                        me->rca, me->ria, me->rma,
+                        results,
+                        baseline_after, baseline_before,
+                        after, before,
+                        options, group, group_options, tier,
+                        &stats, register_zero
+                        );
+                break;
+
+            default:
+            case WEIGHTS_METHOD_MC_KS2:
+                rrdset_metric_correlations_ks2(
+                        host,
+                        me->rca, me->ria, me->rma,
+                        results,
+                        baseline_after, baseline_before,
+                        after, before, points,
+                        options, group, group_options, tier, shifts,
+                        &stats, register_zero
+                        );
+                break;
+        }
+    }
+    dfe_done(me);
+
+    if(!register_zero)
+        options |= RRDR_OPTION_NONZERO;
+
+    if(!(options & RRDR_OPTION_RETURN_RAW))
+        spread_results_evenly(results, &stats);
+
+    usec_t ended_usec = now_realtime_usec();
+
+    // generate the json output we need
+    buffer_flush(wb);
+
+    size_t added_dimensions = 0;
+    switch(format) {
+        case WEIGHTS_FORMAT_CHARTS:
+            added_dimensions =
+                    registered_results_to_json_charts(
+                            results, wb,
+                            after, before,
+                            baseline_after, baseline_before,
+                            points, method, group, options, shifts,
+                            examined_dimensions,
+                            ended_usec - started_usec, &stats);
+            break;
+
+        default:
+        case WEIGHTS_FORMAT_CONTEXTS:
+            added_dimensions =
+                    registered_results_to_json_contexts(
+                            results, wb,
+                            after, before,
+                            baseline_after, baseline_before,
+                            points, method, group, options, shifts,
+                            examined_dimensions,
+                            ended_usec - started_usec, &stats);
+            break;
+    }
+
+    if(!added_dimensions) {
+        error = "no results produced.";
+        resp = HTTP_RESP_NOT_FOUND;
+    }
+
+cleanup:
+    if(metrics) dictionary_destroy(metrics);
+    if(results) register_result_destroy(results);
+
+    if(error) {
+        buffer_flush(wb);
+        buffer_sprintf(wb, "{\"error\": \"%s\" }", error);
+    }
+
+    return resp;
+}
+
+// ----------------------------------------------------------------------------
+// unittest
+
+/*
+
+Unit tests against the output of this:
+
+https://github.com/scipy/scipy/blob/4cf21e753cf937d1c6c2d2a0e372fbc1dbbeea81/scipy/stats/_stats_py.py#L7275-L7449
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import scipy as sp
+from scipy import stats
+
+data1 = np.array([ 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 ])
+data2 = np.array([365, -123, 0])
+data1 = np.sort(data1)
+data2 = np.sort(data2)
+n1 = data1.shape[0]
+n2 = data2.shape[0]
+data_all = np.concatenate([data1, data2])
+cdf1 = np.searchsorted(data1, data_all, side='right') / n1
+cdf2 = np.searchsorted(data2, data_all, side='right') / n2
+print(data_all)
+print("\ndata1", data1, cdf1)
+print("\ndata2", data2, cdf2)
+cddiffs = cdf1 - cdf2
+print("\ncddiffs", cddiffs)
+minS = np.clip(-np.min(cddiffs), 0, 1)
+maxS = np.max(cddiffs)
+print("\nmin", minS)
+print("max", maxS)
+m, n = sorted([float(n1), float(n2)], reverse=True)
+en = m * n / (m + n)
+d = max(minS, maxS)
+prob = stats.distributions.kstwo.sf(d, np.round(en))
+print("\nprob", prob)
+
+*/
+
+static int double_expect(double v, const char *str, const char *descr) {
+    char buf[100 + 1];
+    snprintfz(buf, 100, "%0.6f", v);
+    int ret = strcmp(buf, str) ? 1 : 0;
+
+    fprintf(stderr, "%s %s, expected %s, got %s\n", ret?"FAILED":"OK", descr, str, buf);
+    return ret;
+}
+
+static int mc_unittest1(void) {
+    int bs = 3, hs = 3;
+    DIFFS_NUMBERS base[3] = { 1, 2, 3 };
+    DIFFS_NUMBERS high[3] = { 3, 4, 6 };
+
+    double prob = ks_2samp(base, bs, high, hs, 0);
+    return double_expect(prob, "0.222222", "3x3");
+}
+
+static int mc_unittest2(void) {
+    int bs = 6, hs = 3;
+    DIFFS_NUMBERS base[6] = { 1, 2, 3, 10, 10, 15 };
+    DIFFS_NUMBERS high[3] = { 3, 4, 6 };
+
+    double prob = ks_2samp(base, bs, high, hs, 1);
+    return double_expect(prob, "0.500000", "6x3");
+}
+
+static int mc_unittest3(void) {
+    int bs = 12, hs = 3;
+    DIFFS_NUMBERS base[12] = { 1, 2, 3, 10, 10, 15, 111, 19999, 8, 55, -1, -73 };
+    DIFFS_NUMBERS high[3] = { 3, 4, 6 };
+
+    double prob = ks_2samp(base, bs, high, hs, 2);
+    return double_expect(prob, "0.347222", "12x3");
+}
+
+static int mc_unittest4(void) {
+    int bs = 12, hs = 3;
+    DIFFS_NUMBERS base[12] = { 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 };
+    DIFFS_NUMBERS high[3] = { 365, -123, 0 };
+
+    double prob = ks_2samp(base, bs, high, hs, 2);
+    return double_expect(prob, "0.777778", "12x3");
+}
+
+int mc_unittest(void) {
+    int errors = 0;
+
+    errors += mc_unittest1();
+    errors += mc_unittest2();
+    errors += mc_unittest3();
+    errors += mc_unittest4();
+
+    return errors;
+}
+