* [How to migrate data from Prometheus](https://medium.com/@romanhavronenko/victoriametrics-how-to-migrate-data-from-prometheus-d44a6728f043)
* [How to migrate data from Prometheus. Filtering and modifying time series](https://medium.com/@romanhavronenko/victoriametrics-how-to-migrate-data-from-prometheus-filtering-and-modifying-time-series-6d40cea4bf21)
It is recommended using [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases) - `vmctl` is located in `vmutils-*` archives there.
It builds `vmctl-prod` binary and puts it into the `bin` folder.
### Building docker images
Run `make package-vmctl`. It builds `victoriametrics/vmctl:<PKG_TAG>` docker image locally.
`<PKG_TAG>` is auto-generated image tag, which depends on source code in the repository.
The `<PKG_TAG>` may be manually set via `PKG_TAG=foobar make package-vmctl`.
The base docker image is [alpine](https://hub.docker.com/_/alpine) but it is possible to use any other base image
by setting it via `<ROOT_IMAGE>` environment variable. For example, the following command builds the image on top of [scratch](https://hub.docker.com/_/scratch) image:
```bash
ROOT_IMAGE=scratch make package-vmctl
```
### ARM build
ARM build may run on Raspberry Pi or on [energy-efficient ARM servers](https://blog.cloudflare.com/arm-takes-wing/).
`vmctl` supports the `opentsdb` mode to migrate data from OpenTSDB to VictoriaMetrics time-series database.
See `./vmctl opentsdb --help` for details and full list of flags.
*OpenTSDB migration is not possible without a functioning [meta](http://opentsdb.net/docs/build/html/user_guide/metadata.html) table to search for metrics/series.*
OpenTSDB migration works like so:
1. Find metrics based on selected filters (or the default filter set ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'])
* e.g. `curl -Ss "http://opentsdb:4242/api/suggest?type=metrics&q=sys"`
2. Find series associated with each returned metric
* e.g. `curl -Ss "http://opentsdb:4242/api/search/lookup?m=system.load5&limit=1000000"`
3. Download data for each series in chunks defined in the CLI switches
This means that we must stream data from OpenTSDB to VictoriaMetrics in chunks. This is where concurrency for OpenTSDB comes in. We can query multiple chunks at once, but we shouldn't perform too many chunks at a time to avoid overloading the OpenTSDB cluster.
Starting with a relatively simple retention string (`sum-1m-avg:1h:30d`), let's describe how this is converted into actual queries.
There are two essential parts of a retention string:
1. [aggregation](#aggregation)
2. [windows/time ranges](#windows)
#### Aggregation
Retention strings essentially define the two levels of aggregation for our collected series.
`sum-1m-avg` would become:
* First order: `sum`
* Second order: `1m-avg-none`
##### First Order Aggregations
First-order aggregation addresses how to aggregate any un-mentioned tags.
This is, conceptually, directly opposite to how PromQL deals with tags. In OpenTSDB, if a tag isn't explicitly mentioned, all values assocaited with that tag will be aggregated.
It is recommended to use `sum` for the first aggregation because it is relatively quick and should not cause any changes to the incoming data (because we collect each individual series).
##### Second Order Aggregations
Second-order aggregation (`1m-avg` in our example) defines any windowing that should occur before returning the data
It is recommended to match the stat collection interval so we again avoid transforming incoming data.
We do not allow for defining the "null value" portion of the rollup window (e.g. in the aggreagtion, `1m-avg-none`, the user cannot change `none`), as the goal of this tool is to avoid modifying incoming data.
#### Windows
There are two important windows we define in a retention string:
1. the "chunk" range of each query
2. The time range we will be querying on with that "chunk"
From our example, our windows are `1h:30d`.
##### Window "chunks"
The window `1h` means that each individual query to OpenTSDB should only span 1 hour of time (e.g. `start=2h-ago&end=1h-ago`).
It is important to ensure this window somewhat matches the row size in HBase to help improve query times.
For example, if the query is hitting a rollup table with a 4 hour row size, we should set a chunk size of a multiple of 4 hours (e.g. `4h`, `8h`, etc.) to avoid requesting data across row boundaries. Landing on row boundaries allows for more consistent request times to HBase.
The default table created in HBase for OpenTSDB has a 1 hour row size, so if you aren't sure on a correct row size to use, `1h` is a reasonable choice.
##### Time range
The time range `30d` simply means we are asking for the last 30 days of data. This time range can be written using `h`, `d`, `w`, or `y`. (We can't use `m` for month because it already means `minute` in time parsing).
#### Results of retention string
The resultant queries that will be created, based on our example retention string of `sum-1m-avg:1h:30d` look like this:
One important note for OpenTSDB migration: Queries/HBase scans can "get stuck" within OpenTSDB itself. This can cause instability and performance issues within an OpenTSDB cluster, so stopping the migrator to deal with it may be necessary. Because of this, we provide the timstamp we started collecting data from at thebeginning of the run. You can stop and restart the importer using this "hard timestamp" to ensure you collect data from the same time range over multiple runs.
2020/01/26 14:23:29 fetching series: command: "show series on benchmark from cpu where hostname='host_1703'"; database: "benchmark"; retention: "autogen"
Found 10 timeseries to import. Continue? [Y/n]
```
The timeseries select query would be following:
`fetching series: command: "show series on benchmark from cpu where hostname='host_1703'"; database: "benchmark"; retention: "autogen"`
The second step of filtering is a time filter and it applies when fetching the datapoints from Influx.
Time filtering may be configured with two flags:
* --influx-filter-time-start
* --influx-filter-time-end
Here's an example of importing timeseries for one day only:
VictoriaMetrics has very similar data model to Prometheus and supports [RemoteWrite integration](https://prometheus.io/docs/operating/integrations/#remote-endpoints-and-storage).
So no data changes will be applied.
### Configuration
The configuration flags should contain self-explanatory descriptions.
### Filtering
The filtering consists of three parts: by timeseries and time.
Filtering by time may be configured via flags `--prom-filter-time-start` and `--prom-filter-time-end`
in in RFC3339 format. This filter applied twice: to drop blocks out of range and to filter timeseries in blocks with
Thanos uses the same storage engine as Prometheus and the data layout on-disk should be the same. That means
`vmctl` in mode `prometheus` may be used for Thanos historical data migration as well.
These instructions may vary based on the details of your Thanos configuration.
Please read carefully and verify as you go. We assume you're using Thanos Sidecar on your Prometheus pods,
and that you have a separate Thanos Store installation.
### Current data
1. For now, keep your Thanos Sidecar and Thanos-related Prometheus configuration, but add this to also stream
metrics to VictoriaMetrics:
```
remote_write:
- url: http://victoria-metrics:8428/api/v1/write
```
2. Make sure VM is running, of course. Now check the logs to make sure that Prometheus is sending and VM is receiving.
In Prometheus, make sure there are no errors. On the VM side, you should see messages like this:
```
2020-04-27T18:38:46.474Z info VictoriaMetrics/lib/storage/partition.go:207 creating a partition "2020_04" with smallPartsPath="/victoria-metrics-data/data/small/2020_04", bigPartsPath="/victoria-metrics-data/data/big/2020_04"
2020-04-27T18:38:46.506Z info VictoriaMetrics/lib/storage/partition.go:222 partition "2020_04" has been created
```
3. Now just wait. Within two hours, Prometheus should finish its current data file and hand it off to Thanos Store for long term
storage.
### Historical data
Let's assume your data is stored on S3 served by minio. You first need to copy that out to a local filesystem,
then import it into VM using `vmctl` in `prometheus` mode.
1. Copy data from minio.
1. Run the `minio/mc` Docker container.
1.`mc config host add minio http://minio:9000 accessKey secretKey`, substituting appropriate values for the last 3 items.
1.`mc cp -r minio/prometheus thanos-data`
1. Import using `vmctl`.
1. Follow the [instructions](#how-to-build) to compile `vmctl` on your machine.
1. Use [prometheus](#migrating-data-from-prometheus) mode to import data:
