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1. #Compress data and recompress it to its original state how to
2. #Compress data and recompress it to its original state code
3. #Compress data and recompress it to its original state windows

We could read the input more than once and introduce I/O overhead, or we could buffer the uncompressed output file data blocks until a dictionary is trained, introducing memory overhead. The solutions we considered both involved a new overhead. With this change in approach, we had to face the problem we had hoped to avoid: how can we compress all of an SST file’s data blocks with the same preset dictionary while that dictionary can only be trained after many data blocks have been sampled? In response to this finding, we changed the preset dictionary scope to per SST file. In particular, the approach of training a dictionary on an adjacent file yielded substantially worse ratios than training the dictionary on the same file it would be used to compress.

However, we found a large use case where the proximity of data in the keyspace was more correlated with its similarity than we had predicted. The dictionary could then be trained and applied to subsequent SST files in the same subcompaction. This enabled an approach with minimal buffering overhead because we could collect samples while generating the first output SST file. The original choice was subcompaction scope. Over time we have considered a few possibilities for the scope of a dictionary. We have also measured a use case that can save both CPU and space by reducing data block size and turning on dictionary presetting at the same time. We have measured meaningful benefit to compression ratio in use cases with data block size up to 16KB. In production, we have deployed dictionary presetting to save space in multiple RocksDB use cases with data block size 8KB or smaller.

#Compress data and recompress it to its original state code

Third, preset dictionaries need to be persisted since they are needed at decompression time.įourth, overhead in accessing the preset dictionary must be minimized to prevent regression in critical code paths.įifth, we need easy-to-use measurement to evaluate candidate use cases and production impact. Second, preset dictionaries need to be trained from data samples, which need to be gathered. The challenges in integrating this feature into the storage engine were more substantial than apparent on the surface.įirst, we need to target a preset dictionary to the relevant data. RocksDB now optionally takes advantage of these dictionary presetting APIs. However, as explained above, smaller data block size comes with the downside of worse compression ratio when using the basic compress API.įortunately, zstd and other libraries offer advanced compress APIs that preset the dictionary.Ī preset dictionary makes it possible for the compressor to start from a useful state instead of from an empty one, making compression immediately effective. RocksDB groups key-value pairs into data blocks before storing them in files.įor use cases that are heavy on random accesses, smaller data block size is sometimes desirable for reducing I/O and CPU spent reading blocks. With small inputs, not much content gets added to the dictionary during the compression.Ĭombined, these factors suggest the dictionary will never have enough contents to achieve great compression ratios. With the basic compress API, the compressor starts with an empty dictionary.

#Compress data and recompress it to its original state how to

How to persist in-memory RocksDB database?Īndrew Kryczka Preset Dictionary CompressionĬompression algorithms relying on an adaptive dictionary, such as LZ4, zstd, and zlib, struggle to achieve good compression ratios on small inputs when using the basic compress API.The 1st RocksDB Local Meetup Held on March 27, 2014.Indexing SST Files for Better Lookup Performance.WriteBatchWithIndex: Utility for Implementing Read-Your-Own-Writes.

#Compress data and recompress it to its original state windows