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Diffstat (limited to 'docs')
| -rw-r--r-- | docs/configuration.md | 7 | ||||
| -rw-r--r-- | docs/programming-guide.md | 10 |
2 files changed, 10 insertions, 7 deletions
diff --git a/docs/configuration.md b/docs/configuration.md index 38d3d059f9..85e7d1202d 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -687,9 +687,10 @@ Apart from these, the following properties are also available, and may be useful <td><code>spark.rdd.compress</code></td> <td>false</td> <td> - Whether to compress serialized RDD partitions (e.g. for - <code>StorageLevel.MEMORY_ONLY_SER</code>). Can save substantial space at the cost of some - extra CPU time. + Whether to compress serialized RDD partitions (e.g. for + <code>StorageLevel.MEMORY_ONLY_SER</code> in Java + and Scala or <code>StorageLevel.MEMORY_ONLY</code> in Python). + Can save substantial space at the cost of some extra CPU time. </td> </tr> <tr> diff --git a/docs/programming-guide.md b/docs/programming-guide.md index f823b89a4b..c5e2a1cd7b 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -1196,14 +1196,14 @@ storage levels is: partitions that don't fit on disk, and read them from there when they're needed. </td> </tr> <tr> - <td> MEMORY_ONLY_SER </td> + <td> MEMORY_ONLY_SER <br /> (Java and Scala) </td> <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition). This is generally more space-efficient than deserialized objects, especially when using a <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read. </td> </tr> <tr> - <td> MEMORY_AND_DISK_SER </td> + <td> MEMORY_AND_DISK_SER <br /> (Java and Scala) </td> <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them on the fly each time they're needed. </td> </tr> @@ -1230,7 +1230,9 @@ storage levels is: </tr> </table> -**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level.* +**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, +so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, +`MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`.* Spark also automatically persists some intermediate data in shuffle operations (e.g. `reduceByKey`), even without users calling `persist`. This is done to avoid recomputing the entire input if a node fails during the shuffle. We still recommend users call `persist` on the resulting RDD if they plan to reuse it. @@ -1243,7 +1245,7 @@ efficiency. We recommend going through the following process to select one: This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible. * If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to -make the objects much more space-efficient, but still reasonably fast to access. +make the objects much more space-efficient, but still reasonably fast to access. (Java and Scala) * Don't spill to disk unless the functions that computed your datasets are expensive, or they filter a large amount of the data. Otherwise, recomputing a partition may be as fast as reading it from |