How can we combine Bloom Filter Offloading and Storage Indexes on MyRocks?

MyRocks is a storage engine for the MySQL database that uses RocksDB, a high-performance key-value store, as its underlying storage engine. By combining Bloom filter offloading and storage indexes, it is possible to further improve query performance in a MyRocks environment.

Bloom filter offloading in MyRocks involves moving the Bloom filters from the main memory to disk-based storage. This allows for more memory to be used for other purposes, such as caching the hot data, while still providing the benefits of Bloom filters in terms of query optimization.

Storage indexes, on the other hand, are used in MyRocks to efficiently store and retrieve index data. Storage indexes allow MyRocks to access the data more efficiently, reducing the number of disk I/O operations and improving query performance.

By combining Bloom filter offloading and storage indexes, MyRocks is able to provide a more efficient and optimized query execution environment. The Bloom filters can be offloaded to disk-based storage, freeing up memory for other purposes, while the storage indexes can be used to efficiently access the data, reducing the number of disk I/O operations and improving query performance.

It is important to note that the benefits of combining Bloom filter offloading and storage indexes will vary depending on the workload and the specific use case. It is recommended to thoroughly test and benchmark the performance of the system to determine the optimal configuration for your use case.

Implementing Bloom filter offloading and storage indexes in MyRocks

Here are the steps to implement Bloom filter offloading and storage indexes in MyRocks:

  1. Enable Bloom filter offloading: To enable Bloom filter offloading in MyRocks, you need to set the rocksdb_bloom_filter_bits_per_key configuration option to a value greater than 0. This option controls the size of the Bloom filter and the trade-off between accuracy and memory usage. For example, to enable Bloom filter offloading with a size of 10 bits per key, you would set rocksdb_bloom_filter_bits_per_key=10.
  2. Create storage indexes: To create storage indexes in MyRocks, you need to create an index on the table using the CREATE INDEX statement. MyRocks supports several types of indexes, including B-tree, hash, and bloom filters. For example, to create a B-tree index on the column1 of the table1 table, you would run the following SQL statement: CREATE INDEX index_column1 ON table1 (column1)
  3. Monitor Bloom filter offloading and storage index performance: After enabling Bloom filter offloading and creating storage indexes, it is important to monitor the performance of the system to ensure that the configuration is optimal for your use case. You can use the rocksdb_bloom_filter_offloaded_hit_count and rocksdb_bloom_filter_offloaded_miss_count status variables to monitor the performance of the Bloom filter offloading, and use the SHOW INDEXES statement to monitor the performance of the storage indexes.

Note: The optimal configuration for Bloom filter offloading and storage indexes will depend on the specific use case and workload. It is recommended to thoroughly test and benchmark the performance of the system to determine the optimal configuration for your use case.

In summary, to implement Bloom filter offloading and storage indexes in MyRocks, you need to enable Bloom filter offloading by setting the rocksdb_bloom_filter_bits_per_key configuration option, create storage indexes using the CREATE INDEX statement, and monitor the performance of the system to ensure that the configuration is optimal for your use case.

Conclusion

Combining Bloom filter offloading and storage indexes in MyRocks enhances query performance by optimizing memory and reducing disk I/O. To implement, enable Bloom filter offloading with rocksdb_bloom_filter_bits_per_key and create storage indexes using CREATE INDEX. Monitor with rocksdb_bloom_filter_offloaded_hit_count and rocksdb_bloom_filter_offloaded_miss_count, and test to optimize for specific workloads.

About Shiv Iyer 452 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.