Data Caching - andrew-nguyen/titan GitHub Wiki

Titan employs multiple layers of data caching to facilitate fast graph traversals. The caching layers are listed here in the order they are accessed from within a Titan transaction. The closer the cache is to the transaction, the faster the cache access and the higher the memory footprint and maintenance overhead.

Within an open transaction, Titan maintains two caches:

• Vertex Cache: Caches accessed vertices and their adjacency list (or subsets thereof) so that subsequent access is significantly faster within the same transaction. Hence, this cache speeds up iterative traversals.
• Index Cache: Caches the results for index queries so that subsequent index calls can be served from the cache and do not requires invoking the indexing backend.

Read more about transaction level caching and how to tune it.

Caches retrieved adjacency lists (or subsets thereof) across multiple transactions and beyond the duration of a single transaction. The database level cache is shared by all transactions across a database. It is more space efficient than the transaction level caches but also slightly slower to access.

Read more about database level caching and how to configure it.

Each storage backend maintains its own data caching layer. These caches benefit from compression, data compactness, coordinated expiration and are often maintained off heap which means that large caches can be used without running in garbage collection issues. While these caches can be significantly larger than the database level cache, they are also slower to access.

The exact type of caching and its properties depends on the particular storage backend. Please refer to the respective documentation for more information about the caching infrastructure and how to optimize it.