Prometheus includes a local on-disk time series database, but also optionally integrates with remote storage systems.
Prometheus's local time series database stores time series data in a custom format on disk.
Ingested samples are grouped into blocks of two hours. Each two-hour block consists of a directory containing one or more chunk files that contain all time series samples for that window of time, as well as a metadata file and index file (which indexes metric names and labels to series in the chunk files). The block for currently incoming samples is kept in memory and not fully persisted yet. It is secured against crashes by a write-ahead-log (WAL) that can be replayed when the Prometheus server restarts after a crash. When series are deleted via the API, deletion records are stored in separate tombstone files (instead of deleting the data immediately from the chunk files).
The directory structure of a Prometheus server's data directory will look something like this:
./data/01BKGV7JBM69T2G1BGBGM6KB12 ./data/01BKGV7JBM69T2G1BGBGM6KB12/meta.json ./data/01BKGV7JBM69T2G1BGBGM6KB12/wal ./data/01BKGV7JBM69T2G1BGBGM6KB12/wal/000002 ./data/01BKGV7JBM69T2G1BGBGM6KB12/wal/000001 ./data/01BKGTZQ1SYQJTR4PB43C8PD98 ./data/01BKGTZQ1SYQJTR4PB43C8PD98/meta.json ./data/01BKGTZQ1SYQJTR4PB43C8PD98/index ./data/01BKGTZQ1SYQJTR4PB43C8PD98/chunks ./data/01BKGTZQ1SYQJTR4PB43C8PD98/chunks/000001 ./data/01BKGTZQ1SYQJTR4PB43C8PD98/tombstones ./data/01BKGTZQ1HHWHV8FBJXW1Y3W0K ./data/01BKGTZQ1HHWHV8FBJXW1Y3W0K/meta.json ./data/01BKGTZQ1HHWHV8FBJXW1Y3W0K/wal ./data/01BKGTZQ1HHWHV8FBJXW1Y3W0K/wal/000001 ./data/01BKGV7JC0RY8A6MACW02A2PJD ./data/01BKGV7JC0RY8A6MACW02A2PJD/meta.json ./data/01BKGV7JC0RY8A6MACW02A2PJD/index ./data/01BKGV7JC0RY8A6MACW02A2PJD/chunks ./data/01BKGV7JC0RY8A6MACW02A2PJD/chunks/000001 ./data/01BKGV7JC0RY8A6MACW02A2PJD/tombstones
The initial two-hour blocks are eventually compacted into longer blocks in the background.
Note that a limitation of the local storage is that it is not clustered or replicated. Thus, it is not arbitrarily scalable or durable in the face of disk or node outages and should thus be treated as more of an ephemeral sliding window of recent data. However, if your durability requirements are not strict, you may still succeed in storing up to years of data in the local storage.
Prometheus has several flags that allow configuring the local storage. The most important ones are:
--storage.tsdb.path: This determines where Prometheus writes its database. Defaults to
--storage.tsdb.retention: This determines when to remove old data. Defaults to
On average, Prometheus uses only around 1-2 bytes per sample. Thus, to plan the capacity of a Prometheus server, you can use the rough formula:
needed_disk_space = retention_time_seconds * ingested_samples_per_second * bytes_per_sample
To tune the rate of ingested samples per second, you can either reduce the number of time series you scrape (fewer targets or fewer series per target), or you can increase the scrape interval. However, reducing the number of series is likely more effective, due to compression of samples within a series.
If your local storage becomes corrupted for whatever reason, your best bet is to shut down Prometheus and remove the entire storage directory. However, you can also try removing individual block directories to resolve the problem. This means losing a time window of around two hours worth of data per block directory. Again, Prometheus's local storage is not meant as durable long-term storage.
Prometheus's local storage is limited by single nodes in its scalability and durability. Instead of trying to solve clustered storage in Prometheus itself, Prometheus has a set of interfaces that allow integrating with remote storage systems.
Prometheus integrates with remote storage systems in two ways:
The read and write protocols both use a snappy-compressed protocol buffer encoding over HTTP. The protocols are not considered as stable APIs yet and may change to use gRPC over HTTP/2 in the future, when all hops between Prometheus and the remote storage can safely be assumed to support HTTP/2.
For details on the request and response messages, see the remote storage protocol buffer definitions.
Note that on the read path, Prometheus only fetches raw series data for a set of label selectors and time ranges from the remote end. All PromQL evaluation on the raw data still happens in Prometheus itself. This means that remote read queries have some scalability limit, since all necessary data needs to be loaded into the querying Prometheus server first and then processed there. However, supporting fully distributed evaluation of PromQL was deemed infeasible for the time being.
To learn more about existing integrations with remote storage systems, see the Integrations documentation.
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