Thanks Imran for very detailed explanations and options. I think for now
T-Digest is what I want.
From: iras...@cloudera.com
Date: Tue, 17 Feb 2015 08:39:48 -0600
Subject: Re: Percentile example
To: myl...@hotmail.com
CC: user@spark.apache.org
(trying to repost to the list w/out URLs -- rejected as spam earlier)
Hi,
Using take() is not a good idea, as you have noted it will pull a lot of data
down to the driver so its not scalable. Here are some more scalable
alternatives:
1. Approximate solutions
1a. Sample the data. Just sample some of the data to the driver, sort that
data in memory, and take the 66th percentile of that sample.
1b. Make a histogram with pre-determined buckets. Eg., if you know your data
ranges from 0 to 1 and is uniform-ish, you could make buckets every 0.01. Then
count how many data points go into each bucket. Or if you only care about
relative error and you have integers (often the case if your data is counts),
then you can span the full range of integers with a relatively small number of
buckets. Eg., you only need 200 buckets for 5% error. See the Histogram class
in twitter's Ostrich library
The problem is, if you have no idea what the distribution of your data is, its
very hard to come up with good buckets; you could have an arbitrary amount of
data going to one bucket, and thus tons of error.
1c. Use a TDigest , a compact & scalable data structure for approximating
distributions, and performs reasonably across a wide range of distributions.
You would make one TDigest for each partition (with mapPartitions), and then
merge all of the TDigests together. I wrote up a little more detail on this
earlier, you can search the spark-user on nabble for "tdigest"
2. Exact solutions. There are also a few options here, but I'll give one that
is a variant of what you suggested. Start out by doing a sortByKey. Then
figure out how many records you have in each partitions (with mapPartitions).
Figure out which partition the 66th percentile would be in. Then just read the
one partition you want, and go down to the Nth record in that partition.
To read the one partition you want, you can either (a) use
mapPartitionsWithIndex, and just ignore every partition that isnt' the one you
want or (b) use PartitionPruningRDD. PartitionPruningRDD will avoid launching
empty tasks on the other partitions, so it will be slightly more efficient, but
its also a developer api, so perhaps not worth going to that level of detail.
Note that internally, sortByKey will sample your data to get an approximate
distribution, to figure out what data to put in each partition. However, your
still getting an exact answer this way -- the approximation is only important
for distributing work among all executors. Even if the approximation is
inaccurate, you'll still correct for it, you will just have unequal partitions.
Imran On Sun, Feb 15, 2015 at 9:37 AM, SiMaYunRui <myl...@hotmail.com> wrote:
hello,
I am a newbie to spark and trying to figure out how to get percentile against a
big data set. Actually, I googled this topic but not find any very useful code
example and explanation. Seems that I can use transformer SortBykey to get my
data set in order, but not pretty sure how can I get value of , for example,
percentile 66.
Should I use take() to pick up the value of percentile 66? I don't believe any
machine can load my data set in memory. I believe there must be more efficient
approaches.
Can anyone shed some light on this problem?
Regards
