Please note that this is an experimental module and the development/testing still at early stage. Feel free to try it and give us your feedback.

Description

This Apache Druid module provides a per-lookup caching mechanism for JDBC data sources. The main goal of this cache is to speed up the access to a high latency lookup sources and to provide a caching isolation for every lookup source. Thus user can define various caching strategies or and implementation per lookup, even if the source is the same. This module can be used side to side with other lookup module like the global cached lookup module.

To use this Apache Druid extension, include druid-lookups-cached-single in the extensions load list.

If using JDBC, you will need to add your database’s client JAR files to the extension’s directory. For Postgres, the connector JAR is already included. See the MySQL extension documentation for instructions to obtain MySQL or MariaDB connector libraries. Copy or symlink the downloaded file to extensions/druid-lookups-cached-single under the distribution root directory.

Architecture

Generally speaking this module can be divided into two main component, namely, the data fetcher layer and caching layer.

Data Fetcher layer

First part is the data fetcher layer API DataFetcher, that exposes a set of fetch methods to fetch data from the actual Lookup dimension source. For instance JdbcDataFetcher provides an implementation of DataFetcher that can be used to fetch key/value from a RDBMS via JDBC driver. If you need new type of data fetcher, all you need to do, is to implement the interface DataFetcher and load it via another druid module.

Caching layer

This extension comes with two different caching strategies. First strategy is a poll based and the second is a load based.

Poll lookup cache

The poll strategy cache strategy will fetch and swap all the pair of key/values periodically from the lookup source. Hence, user should make sure that the cache can fit all the data. The current implementation provides 2 type of poll cache, the first is on-heap (uses immutable map), while the second uses MapDB based off-heap map. User can also implement a different lookup polling cache by implementing PollingCacheFactory and PollingCache interfaces.

Loading lookup

Loading cache strategy will load the key/value pair upon request on the key it self, the general algorithm is load key if absent. Once the key/value pair is loaded eviction will occur according to the cache eviction policy. This module comes with two loading lookup implementation, the first is on-heap backed by a Guava cache implementation, the second is MapDB off-heap implementation. Both implementations offer various eviction strategies. Same for Loading cache, developer can implement a new type of loading cache by implementing LookupLoadingCache interface.

Configuration and Operation:

Polling Lookup

Note that the current implementation of offHeapPolling and onHeapPolling will create two caches one to lookup value based on key and the other to reverse lookup the key from value

Example of Polling On-heap Lookup

This example demonstrates a polling cache that will update its on-heap cache every 10 minutes

{
    "type":"pollingLookup",
   "pollPeriod":"PT10M",
   "dataFetcher":{ "type":"jdbcDataFetcher", "connectorConfig":"jdbc://mysql://localhost:3306/my_data_base", "table":"lookup_table_name", "keyColumn":"key_column_name", "valueColumn": "value_column_name"},
   "cacheFactory":{"type":"onHeapPolling"}
}

Example Polling Off-heap Lookup

This example demonstrates an off-heap lookup that will be cached once and never swapped (pollPeriod == null)

{
    "type":"pollingLookup",
   "dataFetcher":{ "type":"jdbcDataFetcher", "connectorConfig":"jdbc://mysql://localhost:3306/my_data_base", "table":"lookup_table_name", "keyColumn":"key_column_name", "valueColumn": "value_column_name"},
   "cacheFactory":{"type":"offHeapPolling"}
}

Loading lookup

Example Loading On-heap Guava

Guava cache configuration spec.

{
   "type":"loadingLookup",
   "dataFetcher":{ "type":"jdbcDataFetcher", "connectorConfig":"jdbc://mysql://localhost:3306/my_data_base", "table":"lookup_table_name", "keyColumn":"key_column_name", "valueColumn": "value_column_name"},
   "loadingCacheSpec":{"type":"guava"},
   "reverseLoadingCacheSpec":{"type":"guava", "maximumSize":500000, "expireAfterAccess":100000, "expireAfterWrite":10000}
}

Example Loading Off-heap MapDB

Off heap cache is backed by MapDB implementation. MapDB is using direct memory as memory pool, please take that into account when limiting the JVM direct memory setup.

{
   "type":"loadingLookup",
   "dataFetcher":{ "type":"jdbcDataFetcher", "connectorConfig":"jdbc://mysql://localhost:3306/my_data_base", "table":"lookup_table_name", "keyColumn":"key_column_name", "valueColumn": "value_column_name"},
   "loadingCacheSpec":{"type":"mapDb", "maxEntriesSize":100000},
   "reverseLoadingCacheSpec":{"type":"mapDb", "maxStoreSize":5, "expireAfterAccess":100000, "expireAfterWrite":10000}
}

JDBC Data Fetcher