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author | Matei Zaharia <matei@databricks.com> | 2014-05-30 00:34:33 -0700 |
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committer | Patrick Wendell <pwendell@gmail.com> | 2014-05-30 00:34:33 -0700 |
commit | c8bf4131bc2a2e147e977159fc90e94b85738830 (patch) | |
tree | a2f885df8fb6654bd7750bb344b97a6cb6889bf3 /docs/tuning.md | |
parent | eeee978a348ec2a35cc27865cea6357f9db75b74 (diff) | |
download | spark-c8bf4131bc2a2e147e977159fc90e94b85738830.tar.gz spark-c8bf4131bc2a2e147e977159fc90e94b85738830.tar.bz2 spark-c8bf4131bc2a2e147e977159fc90e94b85738830.zip |
[SPARK-1566] consolidate programming guide, and general doc updates
This is a fairly large PR to clean up and update the docs for 1.0. The major changes are:
* A unified programming guide for all languages replaces language-specific ones and shows language-specific info in tabs
* New programming guide sections on key-value pairs, unit testing, input formats beyond text, migrating from 0.9, and passing functions to Spark
* Spark-submit guide moved to a separate page and expanded slightly
* Various cleanups of the menu system, security docs, and others
* Updated look of title bar to differentiate the docs from previous Spark versions
You can find the updated docs at http://people.apache.org/~matei/1.0-docs/_site/ and in particular http://people.apache.org/~matei/1.0-docs/_site/programming-guide.html.
Author: Matei Zaharia <matei@databricks.com>
Closes #896 from mateiz/1.0-docs and squashes the following commits:
03e6853 [Matei Zaharia] Some tweaks to configuration and YARN docs
0779508 [Matei Zaharia] tweak
ef671d4 [Matei Zaharia] Keep frames in JavaDoc links, and other small tweaks
1bf4112 [Matei Zaharia] Review comments
4414f88 [Matei Zaharia] tweaks
d04e979 [Matei Zaharia] Fix some old links to Java guide
a34ed33 [Matei Zaharia] tweak
541bb3b [Matei Zaharia] miscellaneous changes
fcefdec [Matei Zaharia] Moved submitting apps to separate doc
61d72b4 [Matei Zaharia] stuff
181f217 [Matei Zaharia] migration guide, remove old language guides
e11a0da [Matei Zaharia] Add more API functions
6a030a9 [Matei Zaharia] tweaks
8db0ae3 [Matei Zaharia] Added key-value pairs section
318d2c9 [Matei Zaharia] tweaks
1c81477 [Matei Zaharia] New section on basics and function syntax
e38f559 [Matei Zaharia] Actually added programming guide to Git
a33d6fe [Matei Zaharia] First pass at updating programming guide to support all languages, plus other tweaks throughout
3b6a876 [Matei Zaharia] More CSS tweaks
01ec8bf [Matei Zaharia] More CSS tweaks
e6d252e [Matei Zaharia] Change color of doc title bar to differentiate from 0.9.0
Diffstat (limited to 'docs/tuning.md')
-rw-r--r-- | docs/tuning.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/tuning.md b/docs/tuning.md index 78e10770a8..c4230416e1 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -10,7 +10,7 @@ Because of the in-memory nature of most Spark computations, Spark programs can b by any resource in the cluster: CPU, network bandwidth, or memory. Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you also need to do some tuning, such as -[storing RDDs in serialized form](scala-programming-guide.html#rdd-persistence), to +[storing RDDs in serialized form](programming-guide.html#rdd-persistence), to decrease memory usage. This guide will cover two main topics: data serialization, which is crucial for good network performance and can also reduce memory use, and memory tuning. We also sketch several smaller topics. @@ -130,7 +130,7 @@ pointer-based data structures and wrapper objects. There are several ways to do When your objects are still too large to efficiently store despite this tuning, a much simpler way to reduce memory usage is to store them in *serialized* form, using the serialized StorageLevels in -the [RDD persistence API](scala-programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`. +the [RDD persistence API](programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`. Spark will then store each RDD partition as one large byte array. The only downside of storing data in serialized form is slower access times, due to having to deserialize each object on the fly. @@ -239,7 +239,7 @@ number of cores in your clusters. ## Broadcasting Large Variables -Using the [broadcast functionality](scala-programming-guide.html#broadcast-variables) +Using the [broadcast functionality](programming-guide.html#broadcast-variables) available in `SparkContext` can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g. a static lookup table), consider turning it into a broadcast variable. |