Thank you Erick. Right now, we have our autoCommit time set to 1800000
(30 minutes), and our autoSoftCommit set to 120000. The thought was
that with HDFS we want less frequent, but larger operations, since HDFS
has such a large block size. Is that incorrect thinking?
As to why we are using HDFS. For our use case, we already have a large
cluster that runs HBase, and we want to index data within it. Adding
another layer of storage that we would need to manage would add
complexity. With HDFS, we just add another box that has disk, and boom
- more storage for all players involved.
-Joe
On 11/22/2017 8:17 PM, Erick Erickson wrote:
Hmm. This is quite possible. Any time things take "too long" it can be
a problem. For instance, if the leader sends docs to a replica and
the request times out, the leader throws the follower into "Leader
Initiated Recovery". The smoking gun here is that there are no errors
on the follower, just the notification that the leader put it into
recovery.
There are other variations on the theme, it all boils down to when
communications fall apart replicas go into recovery.....
Best,
Erick
On Wed, Nov 22, 2017 at 11:02 AM, Joe Obernberger
<joseph.obernber...@gmail.com> wrote:
Hi Shawn - thank you for your reply. The index is 29.9TBytes as reported
by:
hadoop fs -du -s -h /solr6.6.0
29.9 T 89.9 T /solr6.6.0
The 89.9TBytes is due to HDFS having 3x replication. There are about 1.1
billion documents indexed and we index about 2.5 million documents per day.
Assuming an even distribution, each node is handling about 680GBytes of
index. So our cache size is 1.4%. Perhaps 'relatively small block cache'
was an understatement! This is why we split the largest collection into two,
where one is data going back 30 days, and the other is all the data. Most
of our searches are not longer than 30 days back. The 30 day index is
2.6TBytes total. I don't know how the HDFS block cache splits between
collections, but the 30 day index performs acceptable for our specific
application.
If we wanted to cache 50% of the index, each of our 45 nodes would need a
block cache of about 350GBytes. I'm accepting offers of DIMMs!
What I believe caused our 'recovery, fail, retry loop' was one of our
servers died. This caused HDFS to start to replicate blocks across the
cluster and produced a lot of network activity. When this happened, I
believe there was high network contention for specific nodes in the cluster
and their network interfaces became pegged and requests for HDFS blocks
timed out. When that happened, SolrCloud went into recovery which caused
more network traffic. Fun stuff.
-Joe
On 11/22/2017 11:44 AM, Shawn Heisey wrote:
On 11/22/2017 6:44 AM, Joe Obernberger wrote:
Right now, we have a relatively small block cache due to the
requirements that the servers run other software. We tried to find
the best balance between block cache size, and RAM for programs, while
still giving enough for local FS cache. This came out to be 84 128M
blocks - or about 10G for the cache per node (45 nodes total).
How much data is being handled on a server with 10GB allocated for
caching HDFS data?
The first message in this thread says the index size is 31TB, which is
*enormous*. You have also said that the index takes 93TB of disk
space. If the data is distributed somewhat evenly, then the answer to
my question would be that each of those 45 Solr servers would be
handling over 2TB of data. A 10GB cache is *nothing* compared to 2TB.
When index data that Solr needs to access for an operation is not in the
cache and Solr must actually wait for disk and/or network I/O, the
resulting performance usually isn't very good. In most cases you don't
need to have enough memory to fully cache the index data ... but less
than half a percent is not going to be enough.
Thanks,
Shawn
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