I am planning to segregate Elasticsearch index and search requests as much as possible to avoid any unnecessary delay in the indexing process. There is no such a thing as an Elasticsearch dedicated search node or index node. However, I was wondering if the following scenario is suitable. As far as I understood, I cannot segregate search requests from index requests completely because at the end both hit ES data nodes, but it is what I think can help a little:
Few Elasticsearch Coordinator nodes (No master/data) to deal with search requests and route them to the corresponding data node. Hence, for creating search client to deal with search requests, coordinator node URL will be used only.
Use Elasticsearch data nodes directly for the index path and ignore coordinator nodes for indexing.
In this case, the receiving data node will act as a coordinator node for indexing path and dedicated coordinator nodes will be used to route to a replica on data nodes. Data node unnecessary load due to search routing can be minimised.
I was wondering if there is another way to provide segregation at a higher level or I am insane to not use coordinator nodes for the indexing path as well.
P.S: My use case is heavy indexing and light/medium search
You cant separate indexing and search operations, indexing will write on the primary shard, then on the replica shard, whereas search can be done only on primary shards.
If you care about write performance:
no replica
refresh_interval > 30s, keep analyzer simple
lot of shards (across data nodes)
send insert/update queries on data nodes directly
try to have a hot/cold data architecture (hot/cold indices)
Coordinator nodes can not improve search performance at all, this depends on your workload (aggs etc...).
As usually, all tuning stuff depend on your data and usage, you must find the good balance between indexation and searching performance, use the _node/stats endpoint to see whats going on.
Related
I have 2 nodes Elasticsearch cluster with IP addresses of xx.xx.xx.17(master) and xx.xx.xx.18(data). I know this is the documented way of searching on preferred replica/node.
The question is, If I send my request targeting xx.xx.xx.18(data) node (as an example- http://xx.xx.xx.18:9200/product/_count) will the request be querying that specific node?
OR is the only way of querying a preferred node is sending it with the 'preferred' parameter as in the above link?
when you send a query to an Elasticsearch node, it will talk to any and all other nodes that hold data for indices that need to be queried. if you have replicas assigned to indices, Elasticsearch will randomly pick between the primary and (n) replica shards
assuming each node of yours holds a full copy of every shard, either primary or replica, this means you might get your response from all shards on that node or not, which is what LeBigCat hints on above
however you can use preference here, yes. but it's not clear what problem you are trying to solve with this
Im new to elasticsearch and would like someone to help me clarify a few concepts
Im designing a small cluster with the following requirements
everything should still work when restarting one of the machines, one at a time (eg: OS updates)
a single disk failure is ok
heavy indexing should not impact query performance
How many master, data, ingest nodes should I have?
or do I need 2 clusters?
the indexing workload is purely indexing structured text documents, no processing/rules... do I even need an ingest node?
Also, does each node have a complete copy of the all the data? or only a cluster has the complete copy?
Be sure to read the documentation about Elasticsearch terminology at the very least.
With the default of 1 replica (primary shard and one replica shard) you can survive the failure of 1 Elasticsearch node (failed disk, restart, upgrade,...).
"heavy indexing should not impact query performance": You'll need to size your cluster correctly to handle both the indexing and searching. If you want to read current data and you do heavy updates, that will take up resources and you won't be able to fully decouple it.
By default every node is a data, ingest, and master-eligible node. The minimum HA setting needs 3 nodes. If you don't use ingest that's fine; it won't take up resources when you're not using it.
To understand which node has which data, you need to read up on the concept of shards. Basically every index is broken up into 1 to N shards (current default is 5) and there is one primary and one replica copy of each one of them (by default).
If I have an ES cluster and an application indexing data into ES.
EDIT: The application creates indices in a dynamic way based on some business rules.
For example, if the application listen to tweets from Twitter API based on some hashtags it creates an index in ES for each hashtag.
This way, each time a new hashtag comes, a new index is created in ES.
Sometimes, shard reallocation happen and at this stage, the cluster behaves poorly as the amount of data moved between nodes is huge.
From ES cluster API, we can disable shard reallocation and balancing.
What will be the effects (positive and negative) of disabling the reallocation and balancing?
This sounds like a quite unorthodox way of organizing documents in Elasticsearch, wouldn't it be simpler to have a string not_analyzed field which would be an array of hashtabs (as a single tweet can have zero, one two or more hashtags).
If there was only one hashtag / tweet you could use it for routing them to a specific shard, if search performance is a concern for you.
Anyway, if you disable shard balancing then some machines would have increasingly disproportionate amount of documents on some machines and too few on others, this could hamper indexing and searching performance.
Also if you don't have any replicas of shards then in the event of a node shutdown part of you data would become inaccessible. I'm sure in the long run there are other downsides as well.
Suppose at the time of index creation I didn't set any replica for that if I update using update settings API and changed replica status to 1.If I have 2 node the replica should be create on second node because on primary node side replica will not create due to that cluster status is showing yellow the shards not allocating to node2 even though we enabled the replicas to 1.
please share me why replica shard not allocating to node2?
but on cluster startup nodes are showing they detected and join each other.
Here are the Basic concepts of the Elastic search
Installation »
Basic Concepts
There are a few concepts that are core to Elasticsearch. Understanding these concepts from the outset will tremendously help ease the learning process.
Near Realtime (NRT)
Elasticsearch is a near real time search platform. What this means is there is a slight latency (normally one second) from the time you index a document until the time it becomes searchable.
Cluster
A cluster is a collection of one or more nodes (servers) that together holds your entire data and provides federated indexing and search capabilities across all nodes. A cluster is identified by a unique name which by default is "elasticsearch". This name is important because a node can only be part of a cluster if the node is set up to join the cluster by its name.
Make sure that you don’t reuse the same cluster names in different environments, otherwise you might end up with nodes joining the wrong cluster. For instance you could use logging-dev, logging-stage, and logging-prod for the development, staging, and production clusters.
Note that it is valid and perfectly fine to have a cluster with only a single node in it. Furthermore, you may also have multiple independent clusters each with its own unique cluster name.
Node
A node is a single server that is part of your cluster, stores your data, and participates in the cluster’s indexing and search capabilities. Just like a cluster, a node is identified by a name which by default is a random Universally Unique IDentifier (UUID) that is assigned to the node at startup. You can define any node name you want if you do not want the default. This name is important for administration purposes where you want to identify which servers in your network correspond to which nodes in your Elasticsearch cluster.
A node can be configured to join a specific cluster by the cluster name. By default, each node is set up to join a cluster named elasticsearch which means that if you start up a number of nodes on your network and—assuming they can discover each other—they will all automatically form and join a single cluster named elasticsearch.
In a single cluster, you can have as many nodes as you want. Furthermore, if there are no other Elasticsearch nodes currently running on your network, starting a single node will by default form a new single-node cluster named elasticsearch.
Index
An index is a collection of documents that have somewhat similar characteristics. For example, you can have an index for customer data, another index for a product catalog, and yet another index for order data. An index is identified by a name (that must be all lowercase) and this name is used to refer to the index when performing indexing, search, update, and delete operations against the documents in it.
In a single cluster, you can define as many indexes as you want.
Type
Within an index, you can define one or more types. A type is a logical category/partition of your index whose semantics is completely up to you. In general, a type is defined for documents that have a set of common fields. For example, let’s assume you run a blogging platform and store all your data in a single index. In this index, you may define a type for user data, another type for blog data, and yet another type for comments data.
Document
A document is a basic unit of information that can be indexed. For example, you can have a document for a single customer, another document for a single product, and yet another for a single order. This document is expressed in JSON (JavaScript Object Notation) which is an ubiquitous internet data interchange format.
Within an index/type, you can store as many documents as you want. Note that although a document physically resides in an index, a document actually must be indexed/assigned to a type inside an index.
Shards & Replicas
An index can potentially store a large amount of data that can exceed the hardware limits of a single node. For example, a single index of a billion documents taking up 1TB of disk space may not fit on the disk of a single node or may be too slow to serve search requests from a single node alone.
To solve this problem, Elasticsearch provides the ability to subdivide your index into multiple pieces called shards. When you create an index, you can simply define the number of shards that you want. Each shard is in itself a fully-functional and independent "index" that can be hosted on any node in the cluster.
Sharding is important for two primary reasons:
It allows you to horizontally split/scale your content volume
It allows you to distribute and parallelize operations across shards (potentially on multiple nodes) thus increasing performance/throughput
The mechanics of how a shard is distributed and also how its documents are aggregated back into search requests are completely managed by Elasticsearch and is transparent to you as the user.
In a network/cloud environment where failures can be expected anytime, it is very useful and highly recommended to have a failover mechanism in case a shard/node somehow goes offline or disappears for whatever reason. To this end, Elasticsearch allows you to make one or more copies of your index’s shards into what are called replica shards, or replicas for short.
Replication is important for two primary reasons:
It provides high availability in case a shard/node fails. For this reason, it is important to note that a replica shard is never allocated on the same node as the original/primary shard that it was copied from.
It allows you to scale out your search volume/throughput since searches can be executed on all replicas in parallel.
To summarize, each index can be split into multiple shards. An index can also be replicated zero (meaning no replicas) or more times. Once replicated, each index will have primary shards (the original shards that were replicated from) and replica shards (the copies of the primary shards). The number of shards and replicas can be defined per index at the time the index is created. After the index is created, you may change the number of replicas dynamically anytime but you cannot change the number of shards after-the-fact.
By default, each index in Elasticsearch is allocated 5 primary shards and 1 replica which means that if you have at least two nodes in your cluster, your index will have 5 primary shards and another 5 replica shards (1 complete replica) for a total of 10 shards per index.
Note:
Each Elasticsearch shard is a Lucene index. There is a maximum number of documents you can have in a single Lucene index. As of LUCENE-5843, the limit is 2,147,483,519 (= Integer.MAX_VALUE - 128) documents. You can monitor shard sizes using the _cat/shards api.
We have an elastic search cluster set up with two nodes. We want the second node only for replication as load isn't enough to warrant a second node. All primary shards are on the master.
Now here's the problem, every other query gets forwarded to the secondary node. As a result, query times are doubled. I expect this is due to elasticsearch's load balancing.
Is there a way to prevent queries from being delegated?
If you specify preference=_local on the search request url, the request will be executed on the node that received the request (assuming that this node has required shards allocated on it). See http://www.elasticsearch.org/guide/reference/api/search/preference/ for more information.