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Storage

This article describes the detailed design of Celeborn Worker's storage management.

PartitionLocation Physical Storage

Logically, PartitionLocation contains all data with the same partition id. Physically, Celeborn stores PartitionLocation in multiple files, each file corresponds to one PartitionLocation object with a unique epoch for the partition. All PartitionLocations with the same partition id but different epochs aggregate to the complete data for the partition. The file can be in memory, local disks, or DFS/OSS, see Multi-layered Storage below.

A PartitionLocation file can be read only after it is committed, trigger by CommitFiles RPC.

File Layout

Celeborn supports two kinds of partitions:

  • ReducePartition, where each PartitionLocation file stores a portion of data with the same partition id, currently used for Apache Spark.
  • MapPartition, where each PartitionLocation file stores a portion of data from the same map id, currently used for Apache Flink.

ReducePartition

The layout of ReducePartition is as follows:

ReducePartition

ReducePartition data file consists of several chunks (defaults to 8 MiB). Each data file has an in-memory index which points to start positions of each chunk. Upon requesting data from some partition, Worker first returns the index, then sequentially reads and returns a chunk upon each ChunkFetchRequest, which is very efficient.

Notice that chunk boundaries is simply decided by the current chunk's size. In case of replication, since the order of data batch arrival is not guaranteed to be the same for primary and replica, chunks with the same chunk index will probably contain different data in primary and replica. Nevertheless, the whole files in primary and replica contain the same data batches in normal cases.

MapPartition

The layout of MapPartition is as follows:

MapPartition

MapPartition data file consists of several regions (defaults to 64MiB), each region is sorted by partition id. Each region has an in-memory index which points to start positions of each partition. Upon requesting data from some partition, Worker reads the partition data from every region.

Local Disk and Memory Buffer

To the time this article is written, the most common case is local disk only. Users specify directories and capacity that Celeborn can use to store data. It is recommended to specify one directory per disk. If users specify more directories on one disk, Celeborn will try to figure it out and manage in the disk-level granularity.

Worker periodically checks disk health, isolates unhealthy or spaceless disks, and reports to Master through heartbeat.

Upon receiving ReserveSlots, Worker will first try to create a FileWriter on the hinted disk. If that disk is unavailable, Worker will choose a healthy one.

Upon receiving PushData or PushMergedData, Worker unpacks the data (for PushMergedData) and logically appends to the buffered data for each PartitionLocation (no physical memory copy). If the buffer exceeds the threshold (defaults to 256KiB), data will be flushed to the file asynchronously.

If data replication is turned on, Worker will send the data to replica asynchronously. Only after Worker receives ACK from replica will it return ACK to ShuffleClient. Notice that it's not required that data is flushed to file before sending ACK.

Upon receiving CommitFiles, Worker will flush all buffered data for PartitionLocations specified in the RPC and close files, then responds the succeeded and failed PartitionLocation lists.

Trigger Split

Upon receiving PushData or PushMergedData, Worker will check whether disk usage exceeds disk reservation (defaults to 5GiB). If so, Worker will respond Split to ShuffleClient.

Celeborn supports two configurable kinds of split:

  • HARD_SPLIT, meaning old PartitionLocation epoch refuses to accept any data, and future data of the PartitionLocation will only be pushed after new PartitionLocation epoch is ready
  • SOFT_SPLIT, meaning old PartitionLocation epoch continues to accept data, when new epoch is ready, ShuffleClient switches to the new location transparently

The detailed design of split can be found Here.

Self Check

In additional to health and space check on each disk, Worker also collects perf statistics to feed Master for better slots allocation:

  • Average flush time of the last time window
  • Average fetch time of the last time window

Multi-layered Storage

Celeborn aims to store data in multiple layers, i.e. memory, local disks and distributed file systems(or object store like S3, OSS). To the time this article is written, Celeborn supports local disks and HDFS.

The principles of data placement are:

  • Try to cache small data in memory
  • Always prefer faster storage
  • Trade off between faster storage's space and cost of data movement

The high-level design of multi-layered storage is:

storage

Worker's memory is divided into two logical regions: Push Region and Cache Region. ShuffleClient pushes data into Push Region, as ① indicates. Whenever the buffered data in PushRegion for a PartitionLocation exceeds the threshold (defaults to 256KiB), Worker flushes it to some storage layer. The policy of data movement is as follows:

  • If the PartitionLocation is not in Cache Region and Cache Region has enough space, logically move the data to Cache Region. Notice this just counts the data in Cache Region and does not physically do memory copy. As ② indicates.
  • If the PartitionLocation is in Cache Region, logically append the current data, as ③ indicates.
  • If the PartitionLocation is not in Cache Region and Cache Region does not have enough memory, flush the data into local disk, as ④ indicates.
  • If the PartitionLocation is not in Cache Region and both Cache Region and local disk do not have enough memory, flush the data into DFS/OSS, as ⑤ indicates.
  • If the Cache Region exceeds the threshold, choose the largest PartitionLocation and flush it to local disk, as ⑥ indicates.
  • Optionally, if local disk does not have enough memory, choose a PartitionLocation split and evict to HDFS/OSS.