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https://issues.apache.org/jira/browse/DATAFU-63?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16249870#comment-16249870
]
OlgaK commented on DATAFU-63:
-----------------------------
I'm on Linux/Fedora. I've not modified gradlew file manually just, as it
pointed in the docs `gradle -b bootstrap.gradle`. I've done it with my 3.1
gradle. To remove a file from the repo: move the file somewhere else, then
commit, ten move it back and add to gitignore. Especially if it's
platform/versions dependent and by the docs should be generated locally.
The changes in the file are substantial about 1/3 of the file. For example
{quote}git diff gradlew
diff --git a/gradlew b/gradlew
index 16bbbbf..9aa616c 100755
--- a/gradlew
+++ b/gradlew
@@ -6,12 +6,30 @@
##
##############################################################################
-# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to p
-DEFAULT_JVM_OPTS="-XX:MaxPermSize=512m"
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+ ls=`ls -ld "$PRG"`
+ link=`expr "$ls" : '.*-> \(.*\)$'`
+ if expr "$link" : '/.*' > /dev/null; then
+ PRG="$link"
+ else
+ PRG=`dirname "$PRG"`"/$link"
+ fi
+done
.....
{quote}
> SimpleRandomSample by a fixed number
> ------------------------------------
>
> Key: DATAFU-63
> URL: https://issues.apache.org/jira/browse/DATAFU-63
> Project: DataFu
> Issue Type: New Feature
> Reporter: jian wang
> Assignee: jian wang
>
> SimpleRandomSample currently supports random sampling by probability, it does
> not support random sample a fixed number of items. ReserviorSample may do the
> work but since it relies on an in-memory priority queue, memory issue may
> happen if we are going to sample a huge number of items, eg: sample 100M from
> 100G data.
> Suggested approach is to create a new class "SimpleRandomSampleByCount" that
> uses Manuver's rejection threshold to reject items whose weight exceeds the
> threshold as we go from mapper to combiner to reducer. The majority part of
> the algorithm will be very similar to SimpleRandomSample, except that we do
> not use Berstein's theory to accept items and replace probability p = k / n,
> k is the number of items to sample, n is the total number of items local in
> mapper, combiner and reducer.
> Quote this requirement from others:
> "Hi folks,
> Question: does anybody know if there is a quicker way to randomly sample a
> specified number of rows from grouped data? I’m currently doing this, since
> it appears that the SAMPLE operator doesn’t work inside FOREACH statements:
> photosGrouped = GROUP photos BY farm;
> agg = FOREACH photosGrouped {
> rnds = FOREACH photos GENERATE *, RANDOM() as rnd;
> ordered_rnds = ORDER rnds BY rnd;
> limitSet = LIMIT ordered_rnds 5000;
> GENERATE group AS farm,
> FLATTEN(limitSet.(photo_id, server, secret)) AS (photo_id, server,
> secret);
> };
> This approach seems clumsy, and appears to run quite slowly (I’m assuming the
> ORDER/LIMIT isn’t great for performance). Is there a less awkward way to do
> this?
> Thanks,
> "
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