Adding upstream version 1.46.3.

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

2 files changed, 0 insertions, 282 deletions

diff --git a/docs/store/change-metrics-storage.md b/docs/store/change-metrics-storage.md
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-# Change how long Netdata stores metrics
-
-The Netdata Agent uses a custom made time-series database (TSDB), named the 
-[`dbengine`](https://github.com/netdata/netdata/blob/master/database/engine/README.md), to store metrics.
-
-To see the number of metrics stored and the retention in days per tier, use the `/api/v1/dbengine_stats` endpoint. 
-
-To increase or decrease the metric retention time, you just [configure](#configure-metric-retention) 
-the number of storage tiers and the space allocated to each one. The effect of these two parameters 
-on the maximum retention and the memory used by Netdata is described in detail, below. 
-
-## Calculate the system resources (RAM, disk space) needed to store metrics
-
-### Effect of storage tiers and disk space on retention
-
-3 tiers are enabled by default in Netdata, with the following configuration:
-
-```
-[db]
-    mode = dbengine
-    
-    # per second data collection
-    update every = 1
-    
-    # number of tiers used (1 to 5, 3 being default)
-    storage tiers = 3
-    
-    # Tier 0, per second data
-    dbengine multihost disk space MB = 256
-    
-    # Tier 1, per minute data
-    dbengine tier 1 multihost disk space MB = 128
-    dbengine tier 1 update every iterations = 60
-    
-    # Tier 2, per hour data
-    dbengine tier 2 multihost disk space MB = 64
-    dbengine tier 2 update every iterations = 60
-```
-
-The default "update every iterations" of 60 means that if a metric is collected per second in Tier 0, then
-we will have a data point every minute in tier 1 and every hour in tier 2. 
-
-Up to 5 tiers are supported. You may add, or remove tiers and/or modify these multipliers, as long as the 
-product of all the "update every iterations" does not exceed 65535 (number of points for each tier0 point).
-
-e.g. If you simply add a fourth tier by setting `storage tiers = 4` and define the disk space for the new tier, 
-the product of the "update every iterations" will be 60 \* 60 \* 60 = 216,000, which is > 65535. So you'd need to reduce  
-the `update every iterations` of the tiers, to stay under the limit.
-
-The exact retention that can be achieved by each tier depends on the number of metrics collected. The more 
-the metrics, the smaller the retention that will fit in a given size. The general rule is that Netdata needs 
-about **1 byte per data point on disk for tier 0**, and **4 bytes per data point on disk for tier 1 and above**.
-
-So, for 1000 metrics collected per second and 256 MB for tier 0, Netdata will store about:
-
-```
-256MB on disk / 1 byte per point / 1000 metrics => 256k points per metric / 86400 sec per day ~= 3 days
-```
-
-At tier 1 (per minute):
-
-```
-128MB on disk / 4 bytes per point / 1000 metrics => 32k points per metric / (24 hr * 60 min) ~= 22 days
-```
-
-At tier 2 (per hour):
-
-```
-64MB on disk / 4 bytes per point / 1000 metrics => 16k points per metric / 24 hr per day ~= 2 years 
-```
-
-Of course double the metrics, half the retention. There are more factors that affect retention. The number 
-of ephemeral metrics (i.e. metrics that are collected for part of the time). The number of metrics that are 
-usually constant over time (affecting compression efficiency). The number of restarts a Netdata Agents gets 
-through time (because it has to break pages prematurely, increasing the metadata overhead). But the actual 
-numbers should not deviate significantly from the above. 
-
-To see the number of metrics stored and the retention in days per tier, use the `/api/v1/dbengine_stats` endpoint. 
-
-### Effect of storage tiers and retention on memory usage
-
-The total memory Netdata uses is heavily influenced by the memory consumed by the DBENGINE.
-The DBENGINE memory is related to the number of metrics concurrently being collected, the retention of the metrics 
-on disk in relation with the queries running, and the number of metrics for which retention is maintained.
-
-The precise analysis of how much memory will be used by the DBENGINE itself is described in 
-[DBENGINE memory requirements](https://github.com/netdata/netdata/blob/master/database/engine/README.md#memory-requirements).
-
-In addition to the DBENGINE, Netdata uses memory for contexts, metric labels (e.g. in a Kubernetes setup), 
-other Netdata structures/processes (e.g. Health) and system overhead.
-
-The quick rule of thumb, for a high level estimation is
-
-```
-DBENGINE memory in MiB = METRICS x (TIERS - 1) x 8 / 1024 MiB
-Total Netdata memory in MiB = Metric ephemerality factor x DBENGINE memory in MiB + "dbengine page cache size MB" from netdata.conf
-```
-
-You can get the currently collected **METRICS** from the "dbengine metrics" chart of the Netdata dashboard. You just need to divide the 
-value of the "collected" dimension with the number of tiers. For example, at the specific point highlighted in the chart below, 608k metrics 
-were being collected across all 3 tiers, which means that `METRICS = 608k / 3 = 203667`. 
-
-<img width="988" alt="image" src="https://user-images.githubusercontent.com/43294513/225335899-a9216ba7-a09e-469e-89f6-4690aada69a4.png" />
-
-
-The **ephemerality factor** is usually between 3 or 4 and depends on how frequently the identifiers of the collected metrics change, increasing their
-cardinality. The more ephemeral the infrastructure, the more short-lived metrics you have, increasing the ephemerality factor. If the metric cardinality is 
-extremely high due for example to a lot of extremely short lived containers (hundreds started every minute), the ephemerality factor can be much higher than 4.
-In such cases, we recommend splitting the load across multiple Netdata parents, until we can provide a way to lower the metric cardinality, 
-by aggregating similar metrics.
-
-#### Small agent RAM usage
-
-For 2000 metrics (dimensions) in 3 storage tiers and the default cache size:
-
-```
-DBENGINE memory for 2k metrics = 2000 x (3 - 1) x 8 / 1024 MiB = 32 MiB
-dbengine page cache size MB = 32 MiB 
-Total Netdata memory in MiB = 3*32 + 32 = 128 MiB (low ephemerality)
-```
-
-#### Large parent RAM usage
-
-The Netdata parent in our production infrastructure at the time of writing:
- - Collects 206k metrics per second, most from children streaming data
- - The metrics include moderately ephemeral Kubernetes containers, leading to an ephemerality factor of about 4
- - 3 tiers are used for retention
- - The `dbengine page cache size MB` in `netdata.conf` is configured to be 4GB
-
-Netdata parents can end up collecting millions of metrics per second. See also [scaling dedicated parent nodes](#scaling-dedicated-parent-nodes).
-
-The rule of thumb calculation for this set up gives us
-```
-DBENGINE memory = 206,000 x 16 / 1024 MiB = 3,217 MiB = about 3 GiB
-Extra cache = 4 GiB
-Metric ephemerality factor = 4
-Estimated total Netdata memory = 3 * 4 + 4 = 16 GiB
-```
-
-The actual measurement during a low usage time was the following:
-
-Purpose|RAM|Note
-:--- | ---: | :--- 
-DBENGINE usage | 5.9 GiB | Out of 7GB max 
-Cardinality/ephemerality related memory (k8s contexts, labels, strings) | 3.4 GiB
-Buffer for queries | 0 GiB | Out of 0.5 GiB max, when heavily queried
-Other | 0.5 GiB | 
-System overhead | 4.4 GiB | Calculated by subtracting all of the above from the total 
-**Total Netdata memory usage** | 14.2 GiB | 
-
-All the figures above except for the system memory management overhead were retrieved from Netdata itself. 
-The overhead can't be directly calculated, so we subtracted all the other figures from the total Netdata memory usage to get it. 
-This overhead is usually around 50% of the memory actually useable by Netdata, but could range from 20% in small 
-setups, all the way to 100% in some edge cases. 
-
-## Configure metric retention
-
-Once you have decided how to size each tier, open `netdata.conf` with
-[`edit-config`](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#use-edit-config-to-edit-configuration-files) 
-and make your changes in the `[db]` subsection. 
-
-Save the file and restart the Agent with `sudo systemctl restart netdata`, or
-the [appropriate method](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md) 
-for your system, to change the database engine's size.
-
-## Scaling dedicated parent nodes
-
-When you use streaming in medium to large infrastructures, you can have potentially millions of metrics per second reaching each parent node.
-In the lab we have reliably collected 1 million metrics/sec with 16cores and 32GB RAM.
-
-Our suggestion for scaling parents is to have them running on dedicated VMs, using a maximum of 50% of cpu, and ensuring you have enough RAM 
-for the desired retention. When your infrastructure can lead a parent to exceed these characteristics, split the load to multiple parents that
-do not communicate with each other. With each child sending data to only one of the parents, you can still have replication, high availability,
-and infrastructure level observability via the Netdata Cloud UI. 
-
-## Legacy configuration
-
-### v1.35.1 and prior
-
-These versions of the Agent do not support tiers. You could change the metric retention for the parent and
-all of its children only with the `dbengine multihost disk space MB` setting. This setting accounts the space allocation
-for the parent node and all of its children.
-
-To configure the database engine, look for the `page cache size MB` and `dbengine multihost disk space MB` settings in
-the `[db]` section of your `netdata.conf`.
-
-```conf
-[db]
-    dbengine page cache size MB = 32
-    dbengine multihost disk space MB = 256
-```
-
-### v1.23.2 and prior
-
-_For Netdata Agents earlier than v1.23.2_, the Agent on the parent node uses one dbengine instance for itself, and another instance for every child node it receives metrics from. If you had four streaming nodes, you would have five instances in total (`1 parent + 4 child nodes = 5 instances`).
-
-The Agent allocates resources for each instance separately using the `dbengine disk space MB` (**deprecated**) setting. If `dbengine disk space MB`(**deprecated**) is set to the default `256`, each instance is given 256 MiB in disk space, which means the total disk space required to store all instances is, roughly, `256 MiB * 1 parent * 4 child nodes = 1280 MiB`.
-
-#### Backward compatibility
-
-All existing metrics belonging to child nodes are automatically converted to legacy dbengine instances and the localhost
-metrics are transferred to the multihost dbengine instance.
-
-All new child nodes are automatically transferred to the multihost dbengine instance and share its page cache and disk
-space. If you want to migrate a child node from its legacy dbengine instance to the multihost dbengine instance, you
-must delete the instance's directory, which is located in `/var/cache/netdata/MACHINE_GUID/dbengine`, after stopping the
-Agent.
diff --git a/docs/store/distributed-data-architecture.md b/docs/store/distributed-data-architecture.md
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-# Distributed data architecture
-
-Learn how Netdata's distributed data architecture enables us to store metrics on the edge nodes for security, high performance and scalability.
-
-This way, it helps you collect and store per-second metrics from any number of nodes.
-Every node in your infrastructure, whether it's one or a thousand, stores the metrics it collects.
-
-Netdata Cloud bridges the gap between many distributed databases by _centralizing the interface_ you use to query and
-visualize your nodes' metrics. When you [look at charts in Netdata Cloud](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/interact-new-charts.md)
-, the metrics values are queried directly from that node's database and securely streamed to Netdata Cloud, which
-proxies them to your browser.
-
-Netdata's distributed data architecture has a number of benefits:
-
-- **Performance**: Every query to a node's database takes only a few milliseconds to complete for responsiveness when
-  viewing dashboards or using features
-  like [Metric Correlations](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md).
-- **Scalability**: As your infrastructure scales, install the Netdata Agent on every new node to immediately add it to
-  your monitoring solution without adding cost or complexity.
-- **1-second granularity**: Without an expensive centralized data lake, you can store all of your nodes' per-second
-  metrics, for any period of time, while keeping costs down.
-- **No filtering or selecting of metrics**: Because Netdata's distributed data architecture allows you to store all
-  metrics, you don't have to configure which metrics you retain. Keep everything for full visibility during
-  troubleshooting and root cause analysis.
-- **Easy maintenance**: There is no centralized data lake to purchase, allocate, monitor, and update, removing
-  complexity from your monitoring infrastructure.
-
-## Ephemerality of metrics
-
-The ephemerality of metrics plays an important role in retention. In environments where metrics collection is dynamic and
-new metrics are constantly being generated, we are interested about 2 parameters:
-
-1. The **expected concurrent number of metrics** as an average for the lifetime of the database. This affects mainly the
-   storage requirements.
-
-2. The **expected total number of unique metrics** for the lifetime of the database. This affects mainly the memory
-   requirements for having all these metrics indexed and available to be queried.
-
-## Granularity of metrics
-
-The granularity of metrics (the frequency they are collected and stored, i.e. their resolution) is significantly
-affecting retention.
-
-Lowering the granularity from per second to every two seconds, will double their retention and half the CPU requirements
-of the Netdata Agent, without affecting disk space or memory requirements.
-
-## Long-term metrics storage with Netdata
-
-Any node running the Netdata Agent can store long-term metrics for any retention period, given you allocate the
-appropriate amount of RAM and disk space.
-
-Read our document on changing [how long Netdata stores metrics](https://github.com/netdata/netdata/blob/master/docs/store/change-metrics-storage.md) on your nodes for
-details.
-
-You can also stream between nodes using [streaming](https://github.com/netdata/netdata/blob/master/streaming/README.md), allowing to replicate databases and create
-your own centralized data lake of metrics, if you choose to do so.
-
-While a distributed data architecture is the default when monitoring infrastructure with Netdata, you can also configure
-its behavior based on your needs or the type of infrastructure you manage.
-
-To archive metrics to an external time-series database, such as InfluxDB, Graphite, OpenTSDB, Elasticsearch,
-TimescaleDB, and many others, see details on [integrating Netdata via exporting](https://github.com/netdata/netdata/blob/master/docs/export/external-databases.md).
-
-When you use the database engine to store your metrics, you can always perform a quick backup of a node's
-`/var/cache/netdata/dbengine/` folder using the tool of your choice.
-
-## Does Netdata Cloud store my metrics?
-
-Netdata Cloud does not store metric values.
-
-To enable certain features, such as [viewing active alerts](https://github.com/netdata/netdata/blob/master/docs/monitor/view-active-alerts.md)
-or [filtering by hostname](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/node-filter.md), Netdata Cloud does
-store configured alerts, their status, and a list of active collectors.
-
-Netdata does not and never will sell your personal data or data about your deployment.