`mmapfs` is really good for random access but can have sideeffects if
memory maps are large depending on the operating system etc. A hybrid
solution where only selected files are actually memory mapped but others
mostly consumed sequentially brings the best of both worlds and
minimizes the memory map impact.
This commit mmaps only the `dvd` and `tim` file for fast random access
on docvalues and term dictionaries.
Closes#6636
This commit upgrades to the latest Lucene 4.8.1 release including the
following bugfixes:
* An IndexThrottle now kicks in when merges start falling behind
limiting index threads to 1 until merges caught up. Closes#6066
* RateLimiter now kicks in at the configured rate where previously
the limiter was limiting at ~8MB/sec almost all the time. Closes#6018
It's dangerous to expose SerialMergeScheduler as an option: since it only allows one merge at a time, it can easily cause merging to fall behind.
Closes#6120
The current setting of 20MB/sec seems to be too conservative given
the capabilities of modern hardware. Even on cloud infrastructure this
seems to be too lowish. A 50MB default should provide better out of the box
performance
Currently we use 5k operations as a flush threshold. Indexing 5k documents
per second is rather common which would cause the index to be committed on
the lucene level each time the flush logic runs which is 5 seconds by default.
We should rather use a size based threshold similar to the lucene index writer
that doesn't cause such agressive commits which can slow down indexing significantly
especially since they cause the underlying devices to fsync their data.
Load tests showed that SerialMS has problems to keep up with
the merges under high load. We should switch back to CMS
until we have a better story to balance merge
threads / efforts across shards on a single node.
Closes#5817
Today, we use ConcurrentMergeScheduler, and this can be painful since it is concurrent on a shard level, with a max of 3 threads doing concurrent merges. If there are several shards being indexed, then there will be a minor explosion of threads trying to do merges, all being throttled by our merge throttling.
Moving to serial merge scheduler will still maintain concurrency of merges across shards, as we have the merge thread pool that schedules those merges. It will just be a serial one on a specific shard.
Also, on serial merge scheduler, we now have a limit of how many merges it will do at one go, so it will let other shards get their fair chance of merging. We use the pending merges on IW to check if merges are needed or not for it.
Note, that if a merge is happening, it will not block due to a sync on the maybeMerge call at indexing (flush) time, since we wrap our merge scheduler with the EnabledMergeScheduler, where maybeMerge is not activated during indexing, only with explicit calls to IW#maybeMerge (see Merges).
closes#5447
Removed unused misc.asciidoc file
Added plugins directory to directory layout
Fixed transport.tcp.connect_timeout value to match the code found in NetworkService.TcpSettings
Clarified that phrase query does not preserve order of terms
Clarified merge page
Added instructions on how to build documentation to docs/README
* Clean up s/ElasticSearch/Elasticsearch on docs/*
* Clean up s/ElasticSearch/Elasticsearch on src/* bin/* & pom.xml
* Clean up s/ElasticSearch/Elasticsearch on NOTICE.txt and README.textile
Closes#4634
This adds the field data circuit breaker, which is used to estimate
the amount of memory required to load field data before loading it. It
then raises a CircuitBreakingException if the limit is exceeded.
It is configured with two parameters:
`indices.fielddata.cache.breaker.limit` - the maximum number of bytes
of field data to be loaded before circuit breaking. Defaults to
`indices.fielddata.cache.size` if set, unbounded otherwise.
`indices.fielddata.cache.breaker.overhead` - a contast for all field
data estimations to be multiplied with before aggregation. Defaults to
1.03.
Both settings can be configured dynamically using the cluster update
settings API.
This commits add doc values support to geo point using the exact same approach
as for numeric data: geo points for a given document are stored uncompressed
and sequentially in a single binary doc values field.
Close#4207
This commit changes field data configuration updates so that they are
immediately taken into account for loading new segments. The way it works
is that field data configuration is now cached separately from the field
data cache, meaning that it is now possible to clear the field data
configuration from IndexFieldDataService while the cache will stay around. On
the next time that Elasticsearch will reload field data configuration, it will
check if there is already a cache entry, and reuse it if it exists.
To disable field data loading, all that is required is to change the field
data format to "none" (supported by all field data types) using the update
mapping API. Elasticsearch will then refuse to load field data on any new
segment, but field data which has been loaded on the previous segments will
remain available. So you need to clear the field data cache in order to
reclaim memory (otherwise memory will be reclaimed slower, as segments get
merged).
Close#4430Close#4431
Simon Willnauer
Clinton Gormley
Ian Babrou
Adrien Grand
Lee Hinman
Israel Tsadok
mikemccand
Ivan Brusic
Christoph Frick
Kouhei Sutou
Kevin Wang
Martijn van Groningen
Shay Banon
Konrad Feldmeier
Oleg Anashkin