This document provides guidelines to right-sizing the storage capacity of a Pravega cluster (tier-1 and long-term storage).
Right-Sizing Long-Term Storage¶
Pravega's design enables users to store stream data virtually without bounds. However, in practical terms, we need to understand what will be the storage capacity required in Long-Term Storage, either to provision such a system or to estimate storage costs. Even more, users may have a legacy Long-Term Storage and want to devote part of its capacity to store Pravega streams. In either case, we need guidelines to estimate the Long-Term Storage space requirements for Pravega.
There are two ways to reduce the amount of data stored in a Pravega Stream: deleting or truncating the Stream. In this guide, we focus on Stream truncation, and more concretely, on how to provision Long-Term Storage capacity when Stream truncation is performed automatically in Pravega via Stream auto-retention. In this regard, the main configuration parameters involved are:
controller.retention.frequency.minutes: The Controller service periodically triggers the enforcement of Stream auto-retention (i.e., truncation) based on this parameter. Type:
controller.retention.bucket.count: Controllers distribute the Streams that require periodic auto-retention across the number of (Stream) "buckets" defined in this parameter. Multiple Controller instances will share the load related to auto-retention by owning a subset of buckets. Type:
controller.retention.thread.count: Number of threads in the Controller devoted to execute Stream auto-retention tasks. Type:
writer.rollover.size.bytes.max: The maximum size of a single Segment Chunk in Storage. While this can be configured on a per-segment basis via the Segment Store API, this setting establishes a ceiling to that value. No Segment Chunk may exceed this length regardless of how it was configured via the API (if at all). Use this setting if you need to meet some hardware limitation with the underlying Storage implementation (i.e., maximum file size). Valid values: Positive number.
We can enforce two types of auto-retention policies on our Streams: size-based and time-based. There is also a new consumption-based retention model, but it can be combined with the previous policies as well. Also, in this section we assume that all the Streams in the system are configured with some type of auto-retention.
The Long-Term Storage requirements in a production deployment are dictated by: - Rate of data ingestion: This is the main factor that dictates the Long-Term Storage requirements. Clearly, having a high data ingestion rate unavoidably leads to higher Long-Term Storage requirements. - Stream retention policy used: The retention configuration of our Streams is almost as important as the ingestion rate. That is, we can have a data high ingestion rate on Streams configured with frequent/small retention period/size, which would still keep the Long-Term Storage requirements low. - Efficiency of serializers: Pravega only understands bytes, not events. This means that applications writing and reading events from Pravega are required to provide serializers in order to convert events into bytes and the other way round. Depending on the type of data being stored and the serializer implementation, the write amplification of user events into bytes may be significant. - System metadata storage of Pravega: Internally, Pravega stores most of its metadata in Key/Value Tables, which are backed by regular segments. Thus, the metadata of Pravega is also going to consume some space in Long-Term Storage. While this storage cost is minor, depending on how metadata-heavy the workload is it may represent 1% to 5% of the storage space in most cases (i.e., storing some data in 1 Stream has lower metadata cost than storing the same data in 1 million Streams).
With the above points in mind, let's go for a practical example. Let's assume that our workload consists on an average ingestion rate of 1GBps and we have only 1 Stream in the system. Also, the serializers we use in our application have a write amplification of 1.3x and we foresee an additional 5% of storage cost related to Pravega's metadata. Given that, let's exercise how much Long-Term Storage capacity we would need with the following auto-retention policies:
Size-based auto-retention of 1TB: This policy attempts to keep at least 1TB of data related to our Stream. Therefore, the Long-Term Storage space needed would be at least:
1TB · 1.3 + 1TB · 0.05 = 1.35TB.
Time-based retention of 1 day: With this policy, Pravega will keep data for our Stream for at least 1 day. This means that our Long-Term Storage may require to store at least:
86400s * 1GBps * 1.3 + 86400s * 1GBps * 0.05 = 116.7TB.
Note that the above storage estimations are lower bounds. They assume that the Pravega auto-retention service immediately truncates the Stream once the retention policy has been met. However, the Pravega auto-retention service is implemented as a background task and should be properly configured under stringent retention requirements. Next, we provide some guidelines to configure the Pravega auto-retention service:
Set a proper retention frequency: By default,
controller.retention.frequency.minutes=30. This means that Long-Term Storage should be provisioned to accommodate at least 30 minutes of data beyond what is configured in the auto-retention policy, until a Controller instance truncates the Stream. If your cluster should minimize the usage of Long-Term Storage resources, then you may want to set this parameter to a lower value (e.g., 1-5 minutes).
Define the right number of buckets: Similarly to what happens to Segment Containers, we need to think twice before setting the number of buckets. That is, if we leave the default value of 1, there would be only one Controller instance handling all the work related to auto-retention. For this reason, we recommend to estimate the initial number of Controller instances required in your Pravega Cluster: as a rule of thumb, having 1 to 2 Controllers every 3 Segment Stores is enough, depending on the metadata load expected (e.g., working with small Transactions or with many Streams induces more metadata workload). Once the number of Controllers for a deployment is estimated, we can define a number of 4 to 8 times the number of Controllers. That is, if we have 3 Controller instances, we will configure
controller.retention.bucket.count=24. With this, we could scale up the number of Controllers and the new instances will share the load along with with the previous ones.
Controller retention thread pool size: In general, having 1 thread (
controller.retention.thread.count=1, default) per Controller devoted to auto-retention should be enough. However, if you expect your workload to be very intensive in terms of auto-retention (i.e., thousands of Streams, stringent retention requirements) consider to increase the thread pool size to 2-4 threads.
_Maximum size of single chunk in LTS: In production, it may be required to revisit the value of
writer.rollover.size.bytes.max. Limiting the size of chunks in LTS poses a trade-off: if the value is low, the system will achieve fine retention granularity (good) but extra metadata overhead (not so good). On the contrary, a high value in this parameter induces coarse retention granularity with less metadata overhead. Limiting chunk sizes between 1GB-16GB seems reasonable in many scenarios.
Right-Sizing Tier-1 Storage (Bookkeeper)¶
Pravega uses tier-1 storage for durable, low-latency persistence until data can be moved to long-term storage. Thus, we need guidelines to right-sizing the storage capacity required in tier-1 (i.e., Bookkeeper).
To this end, there are three variables that we need to take into account:
- Max ledger size (
bookkeeper.ledger.size.max): Maximum Ledger size (bytes) in BookKeeper. Once a Ledger reaches this size,
it will be closed and another one opened. Type:
1073741824 (1 GB). Update-mode:
Number of Segment Containers (
controller.container.count): Number of Segment Containers in the system. This value must be the same across all Segment Store instances in this cluster. This value cannot be changed dynamically. See documentation for details. Type:
Replication degree (
bookkeeper.write.quorum.size): Write Quorum size for BookKeeper ledgers. Type:
Each Segment Container has a Durable Log (default implementation is
BookkeeperLog). For context, a Segment
Container multiplexes and writes all the operations of active Segments to its Durable Log. In the Bookkeeper implementation
of the Durable Log, Segment operations are grouped into
DataFrames and written as individual entries into a Ledger.
If the Ledger reaches the maximum size specified in
bookkeeper.ledger.size.max, then the current Ledger is closed and
a new one is created (i.e., Ledger rollover). In parallel, as soon as data for a Segment Container is correctly persisted
in long-term storage, the Durable Log can be truncated. When working with Bookkeeper, truncating a Durable Log means
deleting the Ledgers whose last entry is lower than the last truncation point. Once these Ledgers are marked as deleted,
Bookkeeper can delete them when running garbage collection, thus freeing up space to continue writing data.
With the above context, the question to answer is what is the minimum amount of storage space that is needed for preventing Pravega from exhausting storage space in Bookkeeper during normal operation? We suggest provisioning storage space for at least 2 full-length ledgers per container (so one Ledger can be garbage collected by Bookkeeper while the Segment Container continues to write to the new one). Therefore, we could estimate the storage space in Bookkeeper as follows:
Bookie journal/ledger volume: 2 *
pravegaservice.container.count * (
That is, we need a storage space on Bookies that can allocate 2 max size ledgers per container. Nonetheless, we have also to take
into account the replication factor of Pravega writing to Bookkeeper, as well as the number of Bookies. For example, assuming the
default values, we would need
2 * 4 Segment Containers * 1GB = 8GB of storage space in Bookkeeper. This is in the
case of having 3 Bookies (as
bookkeeper.write.quorum.size = 3). If we had 6 Bookies instead, the minimum storage space
to be allocated in Bookie volumes would be 4GB.
As an important point, note that this is the storage space to be allocated individually to each journal and ledger volumes of Bookies. Also, this is a lower bound estimation: there might be situations in which Pravega could still hit Bookies getting rid of storage space (e.g., if long-term storage is unavailable for very long while Pravega keeps ingesting data). For this reason, we recommend using this calculation as a bare minimum estimation and always provision Bookie volumes with higher storage capacity.