How We Use RocksDB at Rockset

On this weblog put up, I will describe how we use RocksDB at Rockset and the way we tuned it to get probably the most efficiency out of it. I assume that the reader is mostly conversant in how Log-Structured Merge tree primarily based storage engines like RocksDB work.

At Rockset, we would like our customers to have the ability to repeatedly ingest their information into Rockset with sub-second write latency and question it in 10s of milliseconds. For this, we’d like a storage engine that may help each quick on-line writes and quick reads. RocksDB is a high-performance storage engine that’s constructed to help such workloads. RocksDB is utilized in manufacturing at Fb, LinkedIn, Uber and plenty of different firms. Initiatives like MongoRocks, Rocksandra, MyRocks and so on. used RocksDB as a storage engine for current standard databases and have been profitable at considerably lowering house amplification and/or write latencies. RocksDB’s key-value mannequin can also be best suited for implementing converged indexing. So we determined to make use of RocksDB as our storage engine. We’re fortunate to have important experience on RocksDB in our workforce within the type of our CTO Dhruba Borthakur who based RocksDB at Fb. For every enter discipline in an enter doc, we generate a set of key-value pairs and write them to RocksDB.

Let me shortly describe the place the RocksDB storage nodes fall within the total system structure.

When a consumer creates a group, we internally create N shards for the gathering. Every shard is replicated k-ways (normally ok=2) to attain excessive learn availability and every shard duplicate is assigned to a leaf node. Every leaf node is assigned many shard replicas of many collections. In our manufacturing surroundings every leaf node has round 100 shard replicas assigned to it. Leaf nodes create 1 RocksDB occasion for every shard duplicate assigned to them. For every shard duplicate, leaf nodes repeatedly pull updates from a DistributedLogStore and apply the updates to the RocksDB occasion. When a question is acquired, leaf nodes are assigned question plan fragments to serve information from among the RocksDB situations assigned to them. For extra particulars on leaf nodes, please check with Aggregator Leaf Tailer weblog put up or Rockset white paper.

To attain question latency of milliseconds beneath 1000s of qps of sustained question load per leaf node whereas repeatedly making use of incoming updates, we spent numerous time tuning our RocksDB situations. Under, we describe how we tuned RocksDB for our use case.

RocksDB-Cloud

RocksDB is an embedded key-value retailer. The info in 1 RocksDB occasion is just not replicated to different machines. RocksDB can’t get better from machine failures. To attain sturdiness, we constructed RocksDB-Cloud. RocksDB-Cloud replicates all the info and metadata for a RocksDB occasion to S3. Thus, all SST information written by leaf nodes get replicated to S3. When a leaf node machine fails, all shard replicas on that machine get assigned to different leaf nodes. For every new shard duplicate project, a leaf node reads the RocksDB information for that shard from corresponding S3 bucket and picks up the place the failed leaf node left off.

Disable Write Forward Log

RocksDB writes all its updates to a write forward log and to the energetic in-memory memtable. The write forward log is used to get better information within the memtables within the occasion of course of restart. In our case, all of the incoming updates for a group are first written to a DistributedLogStore. The DistributedLogStore itself acts as a write forward log for the incoming updates. Additionally, we don’t want to ensure information consistency throughout queries. It’s okay to lose the info within the memtables and re-fetch it from the DistributedLogStore on restarts. Because of this, we disable RocksDB’s write forward log. Which means all our RocksDB writes occur in-memory.

Author Charge Restrict

As talked about above, leaf nodes are answerable for each making use of incoming updates and serving information for queries. We will tolerate comparatively a lot greater latency for writes than for queries. As a lot as potential, we all the time wish to use a fraction of accessible compute capability for processing writes and most of compute capability for serving queries. We restrict the variety of bytes that may be written per second to all RocksDB situations assigned to a leaf node. We additionally restrict the variety of threads used to use writes to RocksDB situations. This helps decrease the affect RocksDB writes may have on question latency. Additionally, by throttling writes on this method, we by no means find yourself with imbalanced LSM tree or set off RocksDB’s built-in unpredictable back-pressure/stall mechanism. Notice that each of those options should not obtainable in RocksDB, however we applied them on prime of RocksDB. RocksDB helps a price limiter to throttle writes to the storage machine, however we’d like a mechanism to throttle writes from the applying to RocksDB.

Sorted Write Batch

RocksDB can obtain greater write throughput if particular person updates are batched in a WriteBatch and additional if consecutive keys in a write batch are in a sorted order. We benefit from each of those. We batch incoming updates into micro-batches of ~100KB measurement and type them earlier than writing them to RocksDB.

Dynamic Degree Goal Sizes

In an LSM tree with leveled compaction coverage, information from a degree don’t get compacted with information from the following degree till the goal measurement of the present degree is exceeded. And the goal measurement for every degree is computed primarily based on the required L1 goal measurement and degree measurement multiplier (normally 10). This normally leads to greater house amplification than desired till the final degree has reached its goal measurement as described on RocksDB weblog. To alleviate this, RocksDB can dynamically set goal sizes for every degree primarily based on the present measurement of the final degree. We use this function to attain the anticipated 1.111 house amplification with RocksDB whatever the quantity of knowledge saved within the RocksDB occasion. It may be turned on by setting AdvancedColumnFamilyOptions::level_compaction_dynamic_level_bytes to true.

Shared Block Cache

As talked about above, leaf nodes are assigned many shard replicas of many collections and there may be one RocksDB occasion for every shard duplicate. As an alternative of utilizing a separate block cache for every RocksDB occasion, we use 1 world block cache for all RocksDB situations on the leaf node. This helps obtain higher reminiscence utilization by evicting unused blocks throughout all shard replicas out of leaf reminiscence. We give block cache about 25% of the reminiscence obtainable on a leaf pod. We deliberately don’t make block cache even larger even when there may be spare reminiscence obtainable that’s not used for processing queries. It is because we would like the working system web page cache to have that spare reminiscence. Web page cache shops compressed blocks whereas block cache shops uncompressed blocks, so web page cache can extra densely pack file blocks that aren’t so sizzling. As described in Optimizing House Amplification in RocksDB paper, web page cache helped scale back file system reads by 52% for 3 RocksDB deployments noticed at Fb. And web page cache is shared by all containers on a machine, so the shared web page cache serves all leaf containers working on a machine.

No Compression For L0 & L1

By design, L0 and L1 ranges in an LSM tree comprise little or no information in comparison with different ranges. There may be little to be gained by compressing the info in these ranges. However, we may avoid wasting cpu by not compressing information in these ranges. Each L0 to L1 compaction must entry all information in L1. Additionally, vary scans can’t use bloom filter and have to search for all information in L0. Each of those frequent cpu-intensive operations would use much less cpu if information in L0 and L1 doesn’t must be uncompressed when learn or compressed when written. That is why, and as really useful by RocksDB workforce, we don’t compress information in L0 and L1, and use LZ4 for all different ranges.

Bloom Filters On Key Prefixes

As described in our weblog put up, Converged Index™: The Secret Sauce Behind Rockset’s Quick Queries, we retailer each column of each doc in RocksDB a number of key ranges. For queries, we learn every of those key ranges otherwise. Particularly, we don’t ever search for a key in any of those key ranges utilizing the precise key. We normally merely search to a key utilizing a smaller, shared prefix of the important thing. Due to this fact, we set BlockBasedTableOptions::whole_key_filtering to false in order that entire keys should not used to populate and thereby pollute the bloom filters created for every SST. We additionally use a customized ColumnFamilyOptions::prefix_extractor in order that solely the helpful prefix of the secret’s used for setting up the bloom filters.

Iterator Freepool

When studying information from RocksDB for processing queries, we have to create 1 or extra rocksdb::Iterators. For queries that carry out vary scans or retrieve many fields, we have to create many iterators. Our cpu profile confirmed that creating these iterators is dear. We use a freepool of those iterators and attempt to reuse iterators inside a question. We can’t reuse iterators throughout queries as every iterator refers to a selected RocksDB snapshot and we use the identical RocksDB snapshot for a question.

Lastly, right here is the total record of configuration parameters we specify for our RocksDB situations.

Choices.max_background_flushes: 2
Choices.max_background_compactions: 8
Choices.avoid_flush_during_shutdown: 1
Choices.compaction_readahead_size: 16384
ColumnFamilyOptions.comparator: leveldb.BytewiseComparator
ColumnFamilyOptions.table_factory: BlockBasedTable
BlockBasedTableOptions.checksum: kxxHash
BlockBasedTableOptions.block_size: 16384
BlockBasedTableOptions.filter_policy: rocksdb.BuiltinBloomFilter
BlockBasedTableOptions.whole_key_filtering: 0
BlockBasedTableOptions.format_version: 4
LRUCacheOptionsOptions.capability : 8589934592
ColumnFamilyOptions.write_buffer_size: 134217728
ColumnFamilyOptions.compression[0]: NoCompression
ColumnFamilyOptions.compression[1]: NoCompression
ColumnFamilyOptions.compression[2]: LZ4
ColumnFamilyOptions.prefix_extractor: CustomPrefixExtractor
ColumnFamilyOptions.compression_opts.max_dict_bytes: 32768

Be taught extra about how Rockset makes use of RocksDB: