[SPARK-4916][SQL][DOCS]Update SQL programming guide about cache section #3759
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Can one of the admins verify this patch? |
| @@ -835,8 +835,7 @@ Spark SQL can cache tables using an in-memory columnar format by calling `sqlCon | |||
| Then Spark SQL will scan only required columns and will automatically tune compression to minimize | |||
| memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory. | |||
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| Note that if you call `schemaRDD.cache()` rather than `sqlContext.cacheTable(...)`, tables will _not_ be cached using | |||
| the in-memory columnar format, and therefore `sqlContext.cacheTable(...)` is strongly recommended for this use case. | |||
| Note that you call `schemaRDD.cache()` alike `sqlContext.cacheTable(...)` in 1.2 release of Spark, tables will be cached using the in-memory columnar format. | |||
There was a problem hiding this comment.
Maybe a bit rewording:
Note that starting from Spark 1.2, both
schemaRDD.cache()andsqlContext.cacheTable(...)leverage in-memory columnar format.
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Thanks! Merged to master. |
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SchemeRDD.cache()now uses in-memory columnar storage.