We use Elastic search for full text search use cases. The data is metadata collected across different objects and stored as ES document. We also update the document in ES whenever the master data gets updated. So, basically it is not a logging use case.
We create one ES index (one primary and 1 replica shard) as soon as we have a tenant who gets onboard for our application. This is to ensure that the ES index is ready when the first object gets created.
We do not anticipate volume of data in the index. The data could range between few hundred of MBs per index. So this is a relatively empty index.
Also, full text search is an optional add-in feature in application, so not all tenants may opt for the same, however our technical team suggested creating index upfront.
What is the overhead of such indices on the ES performance? Are we doing anything different from best practices of ES?
Any input is appreciated.
Empty Elasticsearch index don't have much overhead, as there is actually no data in them, only places where empty indices data is present in the cluster state(index mapping, setting etc) which every node in the cluster has and any change in the index mapping or settings ie index metadata updates the cluster state and gets updated on all the nodes in ES cluster.
If you have sufficient memory and ES heap size, you don't have to worry at all about these empty indices which IMO makes sense considering your use-case.
Related
I am very new to elastic search and its applications, I found that elastic search saves data(indexes) onto disk. Then I wondered: Are there any limitations on number of indexes that can be created or can I create as many as I can since I have a very large disk space?
Currently I have elastic search deployed using a single node cluster with Docker. I have read something about shards and its limitation etc., but I was not able to understand it properly.
Is there anyone on SO, who can shed some light onto these questions for a newbie in layman terms?
What is a single node cluster and how does my data get saved onto disk? Also what are shards and how is it related to elastic search?
I guess the best answer is "it depends ". Generally there is no limitation for having many indexes , Every index has its own mapping and irrelevant to other indexes by default, Actually indexes are instance of Elasticsearch servers and please note that they are not data rather you may think about as entire database alone. There are many variables for answering this question for example if are planning to have replication of your shards in one index then you may found limitation due to the size of document you are planning to ingest inside the index.
As an other note you may need to ask first why I need many indexes ? for enhancing search operation or queries throughput? if it is the case then perhaps its better to use replica shards beside your primary shards in the single index because the queries are executed parallel to each other in replica shards and you can think of shards as an stand alone index inside of your main index so in conclusion I can say there is no limitation as long as you have enough free space to save new data (expanding inverted indexes table created for on field) but regarding to you needs it may be better to have primary and replica shards inside an index .
Is there any limit on how many indexes we can create in elastic search?
Can 100 000 indexes be created in Elasticsearch?
I have read that, maximum of 600-1000 indices can be created. Can it be scaled?
eg: I have a number of stores, and the store has items. Each store will have its own index where its items will be indexed.
There is no limit as such, but obviously, you don't want to create too many indices(too many depends on your cluster, nodes, size of indices etc), but in general, it's not advisable as it can have a server impact on cluster functioning and performance.
Please check loggly's blog and their first point is about proper provisioning and below is important relevant text from the same blog.
ES makes it very easy to create a lot of indices and lots and lots of
shards, but it’s important to understand that each index and shard
comes at a cost. If you have too many indices or shards, the
management load alone can degrade your ES cluster performance,
potentially to the point of making it unusable. We’re focusing on
management load here, but running too many indices/shards can also
have pretty significant impacts on your indexing and search
performance.
The biggest factor we’ve found to impact management overhead is the
size of the Cluster State, which contains all of the mappings for
every index in the cluster. At one point, we had a single cluster with
a Cluster State size of over 900MB! The cluster was alive but not
usable.
Edit: Thanks #Silas, who pointed that from ES 2.X, cluster state updates are not that much costly(As the only diff is sent in update call). More info on this change can be found on this ES issue
I am learning ElasticSearch and in their documentation it's written this line
Performing full SQL-style joins in a distributed system like
Elasticsearch is prohibitively expensive. Instead, Elasticsearch
offers two forms of join which are designed to scale horizontally.
Please someone explain me in layman term what does the 2nd sentence means.
As a preamble you might want to go through another thread on SO that explains horizontal vs vertical scaling.
Most of the time, an ES cluster is designed to grow horizontally, meaning that whenever your cluster starts to show some signs of weaknesses (slow queries, slow indexing, etc), all you need to do is add one or more nodes to your cluster and ES will spread the load on more hardware, and thus, lighten the burden on existing nodes. That's what horizontal scaling is all about and ES is perfectly designed for this given the way it partitions the indexes into shards that get assigned to the nodes in your cluster.
As you know, ES has no JOIN feature and they did it on purpose for the reason mentioned above (i.e. "prohibitively expensive"). There are four ways to model relationships in ES:
by denormalizing your data (preferred)
by using nested types
by using parent/child documents
by using application-side joins
The link you referred to, which introduces the nested, has_parent and has_child queries, is about the second and third bullet point above. Nested and parent/child documents have been designed in such a way as to take advantage as much as possible from the index/shard partitioning model that ES supports.
When using a nested field (1-N relationship), each element inside of the nested array is just another hidden document under the hood and is stored in a shard somewhere in your cluster. When using a join field (1-N relationship), parent and child documents are also documents stored in your index within a shard located somewhere in your cluster. When your index grows (i.e. when you have more and more parent and child and/or nested data), you add nodes and the shards containing your documents will get spread within the cluster transparently. This means that wherever your documents are stored, you can retrieve them as well as their related documents without having to perform expensive joins.
So you will get more information about scaling horizontal here
In Elasticsearch terms when you start two or more instances on ES in same network with same cluster configs then they will connect to each other and create a distributed network.So if you add one more computer or node and started one ES instance there and keep the cluster config same that node will automatically will get attached to the previous cluster and the data and the request load will be shared .When you make any request to ES may be its a read or write request each request can be processed parallel and you get the speed according to the no of node and shards in them of each index.
Get more information here
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.
I'm new to ElasticSearch.
Lets suppose I've 10000 documents. The relevant field in the documents are such that after getting indexed most of them would end up in a single shard.
Would ElasticSearch rebalance this "skewed" distribution for, may be better load balancing?
If I got you question right, the short answer - no, the documents will not be relocated. Choosing shard is based on modulo-like distribution, and its used for index as well as for retrieval.
So, if (theoretically) ES will rebalance such docs, you'll be unable to retrieve them with you routing key, as it will leads to original shard (which is empty in such theoretical case).
The "distribution" part of docs if nice place for further reading
I don't exactly understand what you mean by this "the relevant field in the documents are such that after getting indexed most of them would end up in a single shard".
From what I understand, ElasticSearch will automatically balances the shards between all the nodes started on your setup to be the most effective possible.
The document are indexed on a shard with the field. The same document cannot have some fields on node 1 and some other fields on node 2.