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Unlike Hive, Shark allows users to exploit this temporallocality by caching their working set of data, or in database terms, to createin-memory materialized views. Common data types can be cached in a columnarformat (as Java primitives arrays), which is very efficient for storage andgarbage collection, yet provides maximum performance (orders of magnitudefaster than reading data from disk).
To create a cached table from the rows (or subset ofrows) of an existing table, set the shark.cache table property:
CREATE TABLE ... TBLPROPERTIES ("shark.cache" ="true") AS SELECT ...
We also extend HiveQL to provide a shortcut for thissyntax. Simply append _cached to the table name when using CREATE TABLE AS SELECT, andthat table will be cached in memory. To disable this shortcut, see theconfiguration options section. Below is an example:
CREATE TABLE logs_last_month_cached AS
SELECT * FROM logs WHERE time > date(...);
以上，建表即可，表名加後綴_cached。同時設置這個參數shark.cache.flag.checkTableName爲true。
說明：shark.cache.flag.checkTableName# 'true' or 'false', whether to cache tables ending in "_cached"
Once this table has been created, we can query it likeany other Hive table.
SELECT count(*) from logs_last_month_cached;
Note that queries which shuffle data require you to setthe number of reducers. This will be automatically determined in the nextversion of Shark:
set mapred.reduce.tasks=[num_tasks];
SELECT page, count(*) c FROM logs_last_month_cached
GROUP BY page ORDER BY c DESC LIMIT 10;
In addition to caching, Shark employs a number ofoptimization techniques such as limit push downs and hash-based shuffle, whichcan provide significant speedups in query processing. You can directly inspectthe execution plan for a given query using the explain statement:
explain SELECT * FROM logs_last_month ORDER BY timestamp LIMIT 10;