I have few read heavy indices(started seeing performance issues on these indices) in my ES cluster which has ~50 million docs and noticed most of them have around 25% of total documents as deleted, I know that these deleted document count decrease over time when background merge operation happens, But in my case these count is always around ~25% of total documents and I have below questions/concerns:
Will these huge no of deleted count affects the search performance as they are still part of lucene immutable segments and search happens to all the segments and latest version of document is returned, so size of immutable segments would be high as they contains huge number of deleted docs and then another operation to figure out the latest version of doc.
Will periodic merge operation would take lot of time and inefficient if huge number of deleted documents are there?
is there is any way to delete these huge number of deleted docs in one shot as looks like background merge operation is not able to keep up with huge number?
Thanks
your deleted documents are still part of the index so they impact the search performance ( but I can't tell you if its a huge impact ).
For the periodic merge, Lucene is "reluctant" to merge heavy segments as it requires some disk space and generates a lot of IO.
You can get some precious insight on your segments thanks to the Index Segments API
If you have segments close to the 5GB limit, it is probable that they won't be merged automatically until they are mostly constituted with deleted docs.
You can force a merge on your index with the force merge API
Remember a force merge can generate some stress on a cluster for huge indices. An option exists to only delete documents, that should reduce the burden.
only_expunge_deletes (Optional, boolean) If true, only expunge
segments containing document deletions. Defaults to false.
In Lucene, a document is not deleted from a segment; just marked as
deleted. During a merge, a new segment is created that does not
contain those document deletions.
Regards
we have an index which default max_result_window was set up to 10000, but our data is increasing and we expect we have more than 1 Million Docs there, on of our requirements is scroll all data from the start to end with 1000 in each epic , our documents are not very big and I'll write down one example on following :
{
"serp_query": "c=44444&ct=333333",
"uid": "5815697",
"notify_status": 0,
"created_at": "2018-02-04 10:00:00"
}
I've set max_result_window to 10,000,000 but at this time we have almost 50K docs in our Index, I've read the some texts about consequences of this increasing
Values higher than can consume significant chunks of heap memory per
search and per shard executing the search. It’s safest to leave this
value as it is an use the scroll api for any deep scrolling
https://www.elastic.co/guide/en/elasticsearch/reference/2.x/breaking_21_search_changes.html#_from_size_limits
But we our Documents are not too big and our Elastic Server has 16GB dedicated RAM and guess there is not problem,
I'm writing to ask two questions,
according to the sample Doc ( all our docs should have the same fields) how much it could be Big for one Million Docs,I mean how much heap memory will needed for handle this?
is it very bad solution and will faced us with big problem in future ? are we use scrolling instead of offset and start?
our query is not very complicated, loop on all data ordered by "created_at" descending and get 1000 Docs in each epic.
FYI: our elastic search engine version in 2.7
Just to share the Result with others,
If the Document is not very big and your queries are not very complicated increasing max_result_window has not big effect on performance.
The Usecase
I have an index of potentially millions of documents. I want to make around 20'0000 searches on a subset of these documents (around 25'000 documents). These 25'000 documents could take up around 100 MB stored in Solr (consisting of stored and indexes text fields).
The Problem
As the number of indexed documents increases, the performance of the queries decreases a lot. For example running 20'000 searches that hit 25'000 documents on 100'000 document index takes around 4 minutes. Running the same searches on 200'000 document index takes around 20 minutes.
So is there any way to cache these 25'000 documents in RAM before hitting them with searches?
UPDATE
Some things that really helped:
reducing returned row count (In almost all cases I had to iterate through returned results and in almost all cases where were no more than 100 matching results, but I had set rows to a very large value. Reducing the row count improved the performance around 2x. This seemed counter intuitive. If there are only 79 matches and I set returned row count to 100 it performs better than in a case when where are 79 matches and I set the row count to 1000. In the first case Solr already returns found item count and does it fast. Why should there be a performance difference?)
reducing multithreading (I had added multiple threads for querying because on the development box there were more resources available. On the resource constrained production box it was slowing things down. Using only one or two threads got me around 2x speed improvement.)
Some things that did not really help:
splitting up field queries (I was already using field queries everywhere it was possible, but I was combining them in one fq for each query fq=name:a AND type:b. Splitting them up with fq=name:a&fq=type:b caches them separately (see Apache Solr documentation) and could improve performance. But it did not make a huge difference in this case.
changing caching settings in this case filterCache seemed to have the most potential. However, increasing it or changing its settings did not make a huge difference.
A few things that are recommended for performance:
Have enough spare RAM on the box so index files can be in OS cache
Try to play around with solr caching settings in SolrConfig
Play around with autowarming after commits
Try to develop your queries to limit the result set. Large result sets, specifically if using grouping and faceting will kill performance. Now 200,000 document index is really quite small, so you should not have any problems, but I thought I'd mention this for when you scale.
Try to use Filter query (FQ) whenever possible. They are much faster than doing field:val in q, plus they are cached.
I am deploying an approach which uses from parameter a lot of times. I wish to understand how 'skip' works in elasticsearch or other such systems in general to judge what performance lost does it incur.
It depends on search type. If you use the default, i.e. query then fetch, then to fetch page 20 with size 10 (from: 190, size: 10), elasticsearch will:
ask each primary shard for ids and relevance scores of top 200 documents (which are selected from all docs matching the query, so this means searching the whole index, but this is the same as with fetching only the first page)
merge the results, sorting by relevance, and skip 190 top hits of such merged list, taking those 10 that follow
fetch actual docs (i.e. 10 of them) from relevant shards
It means that if you have e.g. 3 primary replicas, then elasticsearch nodes need to exchange information about 3 * 200 = 600 docs. There are some optimizations to make obtaining particularly 'distant' pages more efficient, but in a nutshell, you need to process more and more documents each time you fetch next page.
If your use case involves going through a result set sequentially, consider scrolling.
We are using elastic search almost as a cache, storing documents found in a time window. We continuously insert a lot of documents of different sizes and then we search in the ES using text queries combined with a date filter so the current thread does not get documents it has already seen. Something like this:
"((word1 AND word 2) OR (word3 AND word4)) AND insertedDate > 1389000"
We maintain the data in the elastic search for 30 minutes, using the TTL feature. Today we have at least 3 machines inserting new documents in bulk requests every minute for each machine and searching using queries like the one above pratically continuously.
We are having a lot of trouble indexing and retrieving these documents, we are not getting a good throughput volume of documents being indexed and returned by ES. We can't get even 200 documents indexed per second.
We believe the problem lies in the simultaneous queries, inserts and TTL deletes. We don't need to keep old data in elastic, we just need a small time window of documents indexed in elastic at a given time.
What should we do to improve our performance?
Thanks in advance
Machine type:
An Amazon EC2 medium instance (3.7 GB of RAM)
Additional information:
The code used to build the index is something like this:
https://gist.github.com/dggc/6523411
Our elasticsearch.json configuration file:
https://gist.github.com/dggc/6523421
EDIT
Sorry about the long delay to give you guys some feedback. Things were kind of hectic here at our company, and I chose to wait for calmer times to give a more detailed account of how we solved our issue. We still have to do some benchmarks to measure the actual improvements, but the point is that we solved the issue :)
First of all, I believe the indexing performance issues were caused by a usage error on out part. As I told before, we used Elasticsearch as a sort of a cache, to look for documents inside a 30 minutes time window. We looked for documents in elasticsearch whose content matched some query, and whose insert date was within some range. Elastic would then return us the full document json (which had a whole lot of data, besides the indexed content). Our configuration had elastic indexing the document json field by mistake (besides the content and insertDate fields), which we believe was the main cause of the indexing performance issues.
However, we also did a number of modifications, as suggested by the answers here, which we believe also improved the performance:
We now do not use the TTL feature, and instead use two "rolling indexes" under a common alias. When an index gets old, we create a new one, assign the alias to it, and delete the old one.
Our application does a huge number of queries per second. We believe this hits elastic hard, and degrades the indexing performance (since we only use one node for elastic search). We were using 10 shards for the node, which caused each query we fired to elastic to be translated into 10 queries, one for each shard. Since we can discard the data in elastic at any moment (thus making changes in the number of shards not a problem to us), we just changed the number of shards to 1, greatly reducing the number of queries in our elastic node.
We had 9 mappings in our index, and each query would be fired to a specific mapping. Of those 9 mappings, about 90% of the documents inserted went to two of those mappings. We created a separate rolling index for each of those mappings, and left the other 7 in the same index.
Not really a modification, but we installed SPM (Scalable Performance Monitoring) from Sematext, which allowed us to closely monitor elastic search and learn important metrics, such as the number of queries fired -> sematext.com/spm/index.html
Our usage numbers are relatively small. We have about 100 documents/second arriving which have to be indexed, with peaks of 400 documents/second. As for searches, we have about 1500 searches per minute (15000 before changing the number of shards). Before those modifications, we were hitting those performance issues, but not anymore.
TTL to time-series based indexes
You should consider using time-series-based indexes rather than the TTL feature. Given that you only care about the most recent 30 minute window of documents, create a new index for every 30 minutes using a date/time based naming convention: ie. docs-201309120000, docs-201309120030, docs-201309120100, docs-201309120130, etc. (Note the 30 minute increments in the naming convention.)
Using Elasticsearch's index aliasing feature (http://www.elasticsearch.org/guide/reference/api/admin-indices-aliases/), you can alias docs to the most recently created index so that when you are bulk indexing, you always use the alias docs, but they'll get written to docs-201309120130, for example.
When querying, you would filter on a datetime field to ensure only the most recent 30 mins of documents are returned, and you'd need to query against the 2 most recently created indexes to ensure you get your full 30 minutes of documents - you could create another alias here to point to the two indexes, or just query against the two index names directly.
With this model, you don't have the overhead of TTL usage, and you can just delete the old, unused indexes from over an hour in the past.
There are other ways to improve bulk indexing and querying speed as well, but I think removal of TTL is going to be the biggest win - plus, your indexes only have a limited amount of data to filter/query against, which should provide a nice speed boost.
Elasticsearch settings (eg. memory, etc.)
Here are some setting that I commonly adjust for servers running ES - http://pastebin.com/mNUGQCLY, note that it's only for a 1GB VPS, so you'll need to adjust.
Node roles
Looking into master vs data vs 'client' ES node types might help you as well - http://www.elasticsearch.org/guide/reference/modules/node/
Indexing settings
When doing bulk inserts, consider modifying the values of both index.refresh_interval index.merge.policy.merge_factor - I see that you've modified refresh_interval to 5s, but consider setting it to -1 before the bulk indexing operation, and then back to your desired interval. Or, consider just doing a manual _refresh API hit after your bulk operation is done, particularly if you're only doing bulk inserts every minute - it's a controlled environment in that case.
With index.merge.policy.merge_factor, setting it to a higher value reduces the amount of segment merging ES does in the background, then back to its default after the bulk operation restores normal behaviour. A setting of 30 is commonly recommended for bulk inserts and the default value is 10.
Some other ways to improve Elasticsearch performance:
increase index refresh interval. Going from 1 second to 10 or 30 seconds can make a big difference in performance.
throttle merging if it's being overly aggressive. You can also reduce the number of concurrent merges by lowering index.merge.policy.max_merge_at_once and index.merge.policy.max_merge_at_once_explicit. Lowering the index.merge.scheduler.max_thread_count can help as well
It's good to see you are using SPM. Its URL in your EDIT was not hyperlink - it's at http://sematext.com/spm . "Indexing" graphs will show how changing of the merge-related settings affects performance.
I would fire up an additional ES instance and have it form a cluster with your current node. Then I would split the work between the two machines, use one for indexing and the other for querying. See how that works out for you. You might need to scale out even more for your specific usage patterns.