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new 5d7be0877b0 [MINOR][DOC] updated some grammar and a missed period in
the tuning doc
5d7be0877b0 is described below
commit 5d7be0877b0bf36098e63297a4125807a933625b
Author: Dustin William Smith <dustin.sm...@deliveryhero.com>
AuthorDate: Sun Nov 6 09:05:44 2022 -0600
[MINOR][DOC] updated some grammar and a missed period in the tuning doc
### What changes were proposed in this pull request?
I changed some grammatical issues in the documentation. One potential
change could be debatable though so please take a look.
https://spark.apache.org/docs/latest/tuning.html
### Why are the changes needed?
Some grammatical mistakes in the documentation.
### Does this PR introduce _any_ user-facing change?
Yes, this corrects some issues in documentation related to Tuning Spark.
The following changes were made
Check if there are too many garbage collections by collecting GC stats. If
a full GC is invoked multiple times ~~for~~
before a task completes, it means that there isn't enough memory
available for executing tasks.
with `-XX:G1HeapRegionSize`. (added missing period).
we can estimate the size of Eden to be `4*3*128MiB`. (added the to estimate
the size--this one I guess debatable)
### How was this patch tested?
No tests added as this was markdown documentation for the user facing page.
Closes #38499 from dwsmith1983/master.
Lead-authored-by: Dustin William Smith <dustin.sm...@deliveryhero.com>
Co-authored-by: dustin <dwsmith1...@users.noreply.github.com>
Signed-off-by: Sean Owen <sro...@gmail.com>
---
docs/tuning.md | 22 +++++++++++-----------
1 file changed, 11 insertions(+), 11 deletions(-)
diff --git a/docs/tuning.md b/docs/tuning.md
index 18d4a6205f4..550ffb0f357 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -217,7 +217,7 @@ The goal of GC tuning in Spark is to ensure that only
long-lived RDDs are stored
the Young generation is sufficiently sized to store short-lived objects. This
will help avoid full GCs to collect
temporary objects created during task execution. Some steps which may be
useful are:
-* Check if there are too many garbage collections by collecting GC stats. If a
full GC is invoked multiple times for
+* Check if there are too many garbage collections by collecting GC stats. If a
full GC is invoked multiple times
before a task completes, it means that there isn't enough memory available
for executing tasks.
* If there are too many minor collections but not many major GCs, allocating
more memory for Eden would help. You
@@ -235,12 +235,12 @@ temporary objects created during task execution. Some
steps which may be useful
* Try the G1GC garbage collector with `-XX:+UseG1GC`. It can improve
performance in some situations where
garbage collection is a bottleneck. Note that with large executor heap
sizes, it may be important to
increase the [G1 region
size](http://www.oracle.com/technetwork/articles/java/g1gc-1984535.html)
- with `-XX:G1HeapRegionSize`
+ with `-XX:G1HeapRegionSize`.
* As an example, if your task is reading data from HDFS, the amount of memory
used by the task can be estimated using
the size of the data block read from HDFS. Note that the size of a
decompressed block is often 2 or 3 times the
size of the block. So if we wish to have 3 or 4 tasks' worth of working
space, and the HDFS block size is 128 MiB,
- we can estimate size of Eden to be `4*3*128MiB`.
+ we can estimate the size of Eden to be `4*3*128MiB`.
* Monitor how the frequency and time taken by garbage collection changes with
the new settings.
@@ -293,14 +293,14 @@ available in `SparkContext` can greatly reduce the size
of each serialized task,
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.
Spark prints the serialized size of each task on the master, so you can look
at that to
-decide whether your tasks are too large; in general tasks larger than about 20
KiB are probably
+decide whether your tasks are too large; in general, tasks larger than about
20 KiB are probably
worth optimizing.
## Data Locality
Data locality can have a major impact on the performance of Spark jobs. If
data and the code that
-operates on it are together then computation tends to be fast. But if code
and data are separated,
-one must move to the other. Typically it is faster to ship serialized code
from place to place than
+operates on it are together, then computation tends to be fast. But if code
and data are separated,
+one must move to the other. Typically, it is faster to ship serialized code
from place to place than
a chunk of data because code size is much smaller than data. Spark builds its
scheduling around
this general principle of data locality.
@@ -308,14 +308,14 @@ Data locality is how close data is to the code processing
it. There are several
locality based on the data's current location. In order from closest to
farthest:
- `PROCESS_LOCAL` data is in the same JVM as the running code. This is the
best locality
- possible
+ possible.
- `NODE_LOCAL` data is on the same node. Examples might be in HDFS on the
same node, or in
another executor on the same node. This is a little slower than
`PROCESS_LOCAL` because the data
- has to travel between processes
-- `NO_PREF` data is accessed equally quickly from anywhere and has no locality
preference
+ has to travel between processes.
+- `NO_PREF` data is accessed equally quickly from anywhere and has no locality
preference.
- `RACK_LOCAL` data is on the same rack of servers. Data is on a different
server on the same rack
- so needs to be sent over the network, typically through a single switch
-- `ANY` data is elsewhere on the network and not in the same rack
+ so needs to be sent over the network, typically through a single switch.
+- `ANY` data is elsewhere on the network and not in the same rack.
Spark prefers to schedule all tasks at the best locality level, but this is
not always possible. In
situations where there is no unprocessed data on any idle executor, Spark
switches to lower locality
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