Here we try to explain how RocksDB uses memory. There are a couple of components in RocksDB that contribute to memory usage:

1. Block cache
2. Indexes and bloom filters
3. Memtables
4. Blocks pinned by iterators

We will describe each of them in turn.

Block cache

Block cache is where RocksDB caches uncompressed data blocks. You can configure block cache’s size by setting block_cache property of BlockBasedTableOptions:

rocksdb::BlockBasedTableOptions table_options;table_options.block_cache = rocksdb::NewLRUCache(1 * 1024 * 1024 * 1024LL);rocksdb::Options options;options.table_factory.reset(new rocksdb::BlockBasedTableFactory(table_options));

If the data block is not found in block cache, RocksDB reads it from file using buffered IO. That means it also uses the OS’s page cache for raw file blocks, usually containing compressed data. In a way, RocksDB’s cache is two-tiered: block cache and page cache. Counter-intuitively, decreasing block cache size will not increase IO. The memory saved will likely be used for page cache, so even more data will be cached. However, CPU usage might grow because RocksDB needs to decompress pages it reads from page cache.

To learn how much memory block cache is using, you can call a function GetUsage() on block cache object:

table_options.block_cache->GetUsage();

In MongoRocks, you can get the size of block cache by calling

> db.serverStatus()["rocksdb"]["block-cache-usage"]

Indexes and filter blocks

Indexes and filter blocks can be big memory users and by default they don’t count in memory you allocate for block cache. This can sometimes cause confusion for users: you allocate 10GB for block cache, but RocksDB is using 15GB of memory. The difference is usually explained by index and bloom filter blocks.

Here’s how you can roughly calculate and manage sizes of index and filter blocks:

1. For each data block we store three information in the index: a key, a offset and size. Therefore, there are two ways you can reduce the size of the index. If you increase block size, the number of blocks will decrease, so the index size will also reduce linearly. By default our block size is 4KB, although we usually run with 16-32KB in production. The second way to reduce the index size is the reduce key size, although that might not be an option for some use-cases.
2. Calculating the size of filter blocks is easy. If you configure bloom filters with 10 bits per key (default, which gives 1% of false positives), the bloom filter size is number_of_keys * 10 bits. There’s one trick you can play here, though. If you’re certain that Get() will mostly find a key you’re looking for, you can set options.optimize_filters_for_hits = true. With this option turned on, we will not build bloom filters on the last level, which contains 90% of the database. Thus, the memory usage for bloom filters will be 10X less. You will pay one IO for each Get() that doesn’t find data in the database, though.

There are two options that configure how much index and filter blocks we fit in memory:

1. If you set cache_index_and_filter_blocks to true, index and filter blocks will be stored in block cache, together with all other data blocks. This also means they can be paged out. If your access pattern is very local (i.e. you have some very cold key ranges), this setting might make sense. However, in most cases it will hurt your performance, since you need to have index and filter to access a certain file. An exception to cache_index_and_filter_blocks=true is for L0 when setting pin_l0_filter_and_index_blocks_in_cache=true, which can be a good compromise setting.
2. If cache_index_and_filter_blocks is false (which is default), the number of index/filter blocks is controlled by option max_open_files. If you are certain that your ulimit will always be bigger than number of files in the database, we recommend setting max_open_files to -1, which means infinity. This option will preload all filter and index blocks and will not need to maintain LRU of files. Setting max_open_files to -1 will get you the best possible performance.

However, regardless of options, by default each column family in each database instance will have its own block cache instance, with its own memory limit. To share a single block cache, set block_cache in your various BlockBasedTableOptions to use the same shared_ptr, or share the same BlockBasedTableOptions for your various factories, or share the same BlockBasedTableFactory for table_factory in your various Options or ColumnFamilyOptions, etc.

To learn how much memory is being used by index and filter blocks, you can use RocksDB’s GetProperty() API:

std::string out;db->GetProperty("rocksdb.estimate-table-readers-mem", &out);

In MongoRocks, just call this API from the mongo shell:

> db.serverStatus()["rocksdb"]["estimate-table-readers-mem"]

In partitioned index/filters the indexes and filters for each partition are always stored in block cache. The top-level index can be configured to be stored in heap or block cache via cache_index_and_filter_blocks.

Memtable

You can think of memtables as in-memory write buffers. Each new key-value pair is first written to the memtable. Memtable size is controlled by the option write_buffer_size. It’s usually not a big memory consumer unless using many column families and/or database instances. However, memtable size inversely affects write amplification: more memory to the memtable yields less write amplification. If you increase your memtable size, be sure to also increase your L1 size! L1 size is controlled by the option max_bytes_for_level_base.

To get the current memtable size, you can use:

std::string out;db->GetProperty("rocksdb.cur-size-all-mem-tables", &out);

In MongoRocks, the equivalent call is

> db.serverStatus()["rocksdb"]["cur-size-all-mem-tables"]

Since version 5.6, you can cost the memory budget of memtables as a part of block cache. Check Write Buffer Manager for the information.

Similar to block cache, by default memtable sizes are per column family, per database instance. Use a Write Buffer Manager to cap memtable memory across column families and/or database instances.

Blocks pinned by iterators

Blocks pinned by iterators usually don’t contribute much to the overall memory usage. However, in some cases, when you have 100k read transactions happening simultaneously, it might put a strain on memory. Memory usage for pinned blocks is easy to calculate. Each iterator pins exactly one data block for each L0 file plus one data block for each L1+ level. So the total memory usage from pinned blocks is approximately num_iterators * block_size * ((num_levels-1) + num_l0_files). To get the statistics about this memory usage, call GetPinnedUsage() on block cache object:

    table_options.block_cache->GetPinnedUsage();