We have discussed indexing, searching, and aggregations for parent-child and grandparent-grandchildren relationships in elasticsearch. The parent-child functionality allows us to associate one document type with another, in a one-to-many relationship or one parent to many children.
We have already discussed the “The Authoritative Guide to Elasticsearch Performance Tuning” in a three part tutorial series to introduce some general tips and methods for performance tuning, explaining at each step the most relevant system configuration settings and metrics. The tutorials already covered in the same context are as follows:
The Authoritative Guide to Elasticsearch Performance Tuning (Part 1)
The Authoritative Guide to Elasticsearch Performance Tuning (Part 2)
The Authoritative Guide to Elasticsearch Performance Tuning (Part 3)
The aim of this tutorial is to recommend some Performance Tuning techniques, strategies and recommendations specific to Elasticsearch 5.0 or onwards.
This post is part 3 of a 3-part series about tuning Elasticsearch Indexing. Part 1 can be found here and Part 2 can be found here.
This tutorial series focuses specifically on tuning elasticsearch to achieve maximum indexing throughput and reduce monitoring and management load.
Elasticsearch provides sharding and replication as the recommended way for scaling and increasing availability of an index. A little over allocation is good but a bazillion shards is bad. It is difficult to define what constitutes too many shards, as it depends on their size and how they are being used. A hundred shards that are seldom used may be fine, while two shards experiencing very heavy usage could be too many. Monitor your nodes to ensure that they have enough spare capacity to deal with exceptional conditions.
This post is part 2 of a 3-part series about tuning Elasticsearch Indexing. Part 1 can be found here.
The tutorial series focuses specifically on tuning elasticsearch to achieve maximum indexing throughput and reduce monitoring and management load. Elasticsearch is near-realtime, in the sense that when you index a document, you need to wait for the next refresh for that document to appear in search.
Refreshing is an expensive operation and that is why it’s made at a regular interval (default), instead of after each indexing operation. If you are planning to index a lot of documents and you don’t need the new information to be immediately available for search, you can optimize for indexing performance over search performance by decreasing refresh frequency until you are done indexing.
This post is part 1 of a 3-part series about tuning Elasticsearch Indexing. This series focuses specifically on tuning Elasticsearch to achieve maximum indexing throughput and reduce monitoring and management load.
As a starting point, assume that you start Elasticsearch, create an index, and feed it with JSON documents without incorporating schemas. Elasticsearch will then iterate over each indexed field of the JSON document, estimate its field, and create a respective mapping. While this may seem ideal, Elasticsearch mappings are not always accurate. If, for example, the wrong field type is chosen, indexing errors will occur.
Editors Note: This post is part 3 of a 3-part series on tuning Elasticsearch performance. Part 1 can be found here and Part 2 can be found here.
Shard Allocation, Rebalancing and Awareness are very crucial and important from the perspective of preventing any data loss or to prevent the painful Cluster Status: RED (a sign alerting that the cluster is missing some primary shards). Apart from shard allocation, everyone loves to tweak threadpools. For whatever reason, it seems people cannot resist increasing thread counts.
The default threadpool settings in Elasticsearch are very sensible. For all threadpools (except search) the threadcount is set to the number of CPU cores. If we have eight cores, we can be running only eight threads simultaneously. It makes sense to assign only eight threads to any particular threadpool.
In this tutorial, we’ll be focussing on Shard Allocation and Threadpool Configuration settings to keep our cluster’s health green and improve overall performance.