What is the maximum number of nodes per cluster in elasticsearch Basic license?
I cannot configure a cluster bigger than 3 nodes, and I can't find information if it is possible on the web.
Is there any kind of limits on the regard.
Oficial Licensing information
AFAIK, the Basic license doesn't impose any limitation on the maximum number of nodes per cluster
Is there a list of hard limits for Elasticsearch?
I am particularly interested if there is a theoretical limit on the number of indices one can create, and the number of records an index can have.
There are limits to the number of docs per shard of 2 billion, which is a hard Lucene limit.
The actual value is Integer.MAX_VALUE - 128 which is 2147483519 documents per shard.
While asking about the number of documents can be a realistic question, looking at the maximum number of indices is the wrong question. Probably there is a JVM limit, assuming some kind of array or ArrayList that holds these indices (or indices mappings - the cluster state) then the limit would be the size of the array or the size of the ArrayList/HashSet/Map etc.
Way before reaching that theoretical limit your cluster is probably dead or not even able to start. Each shard is using resources, the cluster state would be huge, even with one primary shard only. The correct question would be to ask about performance issues with your cluster and not thinking about the maximum number of indices in a cluster.
We are having a setup of 1 master and 2 slave nodes. The data is setup in postgres and in hbase and its a similar dataset (same number of rows) - 65 million rows. Yet, we dont find a measurable increase in performance from HBase for the same query.
My first thought is - does HBase use the compute capacity of all nodes to fork the query out? Perhaps this is why the performance is not measurably better.
Any other reasons for why the performance between Postgres and HBase would be about the same? Any specific configuration items to look for?
EDIT : Something I found while researching this : http://www.flurry.com/2012/06/12/137492485#.VaQP_5QpBpg
This is kind of a yes and no answer. Depending on what you are doing for your 'query' and your region distribution, you may or may not be using all the nodes. For example, if you are running a scan across the table it will run against each region (assuming more then one) in sequence. However if you are using a multi-get for keys that are in different regions, this will run in parallel.
The real benefit is going to come as the number of regions increase and you start parallelizing requests (multiple clients). Regions will be distributed across region servers by the Master as regions are split.
I would appreciate if someone could suggest the optimal number of shards per ES node for optimal performance or provide any recommended way to arrive at the number of shards one should use, given the number of cores and memory foot print.
I'm late to the party, but I just wanted to point out a couple of things:
The optimal number of shards per index is always 1. However, that provides no possibility of horizontal scale.
The optimal number of shards per node is always 1. However, then you cannot scale horizontally more than your current number of nodes.
The main point is that shards have an inherent cost to both indexing and querying. Each shard is actually a separate Lucene index. When you run a query, Elasticsearch must run that query against each shard, and then compile the individual shard results together to come up with a final result to send back. The benefit to sharding is that the index can be distributed across the nodes in a cluster for higher availability. In other words, it's a trade-off.
Finally, it should be noted that any more than 1 shard per node will introduce I/O considerations. Since each shard must be indexed and queried individually, a node with 2 or more shards would require 2 or more separate I/O operations, which can't be run at the same time. If you have SSDs on your nodes then the actual cost of this can be reduced, since all the I/O happens much quicker. Still, it's something to be aware of.
That, then, begs the question of why would you want to have more than one shard per node? The answer to that is planned scalability. The number of shards in an index is fixed. The only way to add more shards later is to recreate the index and reindex all the data. Depending on the size of your index that may or may not be a big deal. At the time of writing, Stack Overflow's index is 203GB (see: https://stackexchange.com/performance). That's kind of a big deal to recreate all that data, so resharding would be a nightmare. If you have 3 nodes and a total of 6 shards, that means that you can scale out to up to 6 nodes at a later point easily without resharding.
There are three condition you consider before sharding..
Situation 1) You want to use elasticsearch with failover and high availability. Then you go for sharding.
In this case, you need to select number of shards according to number of nodes[ES instance] you want to use in production.
Consider you wanna give 3 nodes in production. Then you need to choose 1 primary shard and 2 replicas for every index. If you choose more shards than you need.
Situation 2) Your current server will hold the current data. But due to dynamic data increase future you may end up with no space on disk or your server cannot handle much data means, then you need to configure more no of shards like 2 or 3 shards (its up to your requirements) for each index. But there shouldn't any replica.
Situation 3) In this situation you the combined situation of situation 1 & 2. then you need to combine both configuration. Consider your data increased dynamically and also you need high availability and failover. Then you configure a index with 2 shards and 1 replica. Then you can share data among nodes and get an optimal performance..!
Note: Then query will be processed in each shard and perform mapreduce on results from all shards and return the result to us. So the map reduce process is expensive process. Minimum shards gives us optimal performance
If you are using only one node in production then, only one primary shards is optimal no of shards for each index.
Hope it helps..!
Just got back from configuring some log storage for 10 TB so let's talk sharding :D
Node limitations
Main source: The definitive guide to elasticsearch
HEAP: 32 GB at most:
If the heap is less than 32 GB, the JVM can use compressed pointers, which saves a lot of memory: 4 bytes per pointer instead of 8 bytes.
HEAP: 50% of the server memory at most. The rest is left to filesystem caches (thus 64 GB servers are a common sweet spot):
Lucene makes good use of the filesystem caches, which are managed by the kernel. Without enough filesystem cache space, performance will suffer. Furthermore, the more memory dedicated to the heap means less available for all your other fields using doc values.
[An index split in] N shards can spread the load over N servers:
1 shard can use all the processing power from 1 node (it's like an independent index). Operations on sharded indices are run concurrently on all shards and the result is aggregated.
Less shards is better (the ideal is 1 shard):
The overhead of sharding is significant. See this benchmark for numbers https://blog.trifork.com/2014/01/07/elasticsearch-how-many-shards/
Less servers is better (the ideal is 1 server (with 1 shard)]):
The load on an index can only be split across nodes by sharding (A shard is enough to use all resources on a node). More shards allow to use more servers but more servers bring more overhead for data aggregation... There is no free lunch.
Configuration
Usage: A single big index
We put everything in a single big index and let elasticsearch do all the hard work relating to sharding data. There is no logic whatsoever in the application so it's easier to dev and maintain.
Let's suppose that we plan for the index to be at most 111 GB in the future and we've got 50 GB servers (25 GB heap) from our cloud provider.
That means we should have 5 shards.
Note: Most people tend to overestimate their growth, try to be realistic. For instance, this 111GB example is already a BIG index. For comparison the stackoverflow index is 430 GB (2016) and it's a top 50 site worldwide, made entirely of written texts by millions of people.
Usage: Index by time
When there're too much data for a single index or it's getting too annoying to manage, the next thing is to split the index by time period.
The most extreme example is logging applications (logstach and graylog) which are using a new index every day.
The ideal configuration of 1-single-shard-per-index makes perfect sense in scenario. The index rotation period can be adjusted, if necessary, to keep the index smaller than the heap.
Special case: Let's imagine a popular internet forum with monthly indices. 99% of requests are hitting the last index. We have to set multiple shards (e.g. 3) to spread the load over multiple nodes. (Note: It's probably unnecessary optimization. A 99% hitrate is unlikely in the real world and the shard replica could distribute part of the read-only load anyway).
Usage: Going Exascale (just for the record)
ElasticSearch is magic. It's the easiest database to setup in cluster and it's one of the very few able to scale to many nodes (excluding Spanner ).
It's possible to go exascale with hundreds of elasticsearch nodes. There must be many indices and shards to spread the load on that many machines and that takes an appropriate sharding configuration (eventually adjusted per index).
The final bit of magic is to tune elasticsearch routing to target specific nodes for specific operations.
It might be also a good idea to have more than one primary shard per node, depends on use case. I have found out that bulk indexing was pretty slow, only one CPU core was used - so we had idle CPU power and very low IO, definitely hardware was not a bottleneck. Thread pool stats shown, that during indexing only one bulk thread was active. We have a lot of analyzers and complex tokenizer (decomposed analysis of German words). Increasing number of shards per node has resulted in more bulk threads being active (one per shard on node) and it has dramatically improved speed of indexing.
Number of primary shards and replicas depend upon following parameters:
No of Data Nodes: The replica shards for the given primary shard meant to be present on different data nodes, which means if there are 3 data Nodes: DN1, DN2, DN3 then if primary shard is in DN1 then the replica shard should be present in DN2 and/or DN3. Hence no of replicas should be less than total no of Data Nodes.
Capacity of each of the Data Nodes: Size of the shard cannot be more than the size of the data nodes hard disk and hence depending upon the expected size for the given index, no of primary shards should be defined.
Recovering mechanism in case of failure: If the data on the given index has quick recovering mechanism then 1 replica should be enough.
Performance requirement from the given index: As sharding helps in directing the client node to appropriate shard to improve the performance and hence depending upon the query parameter and size of the data belonging to that query parameter should be considered in defining the no of primary shards.
These are the ideal and basic guidelines to be followed, it should be optimized depending upon the actual use cases.
I have not tested this yet, but aws has a good articale about ES best practises. Look at Choosing Instance Types and Testing part.
Elastic.co recommends to:
[…] keep the number of shards per node below 20 per GB heap it has configured
I'm looking for an answer to this question that comes from a class on data structures and algorithms. I learned about the merge sort but don't remember clusters and buffers. I'm not quite sure I understand the question. Can someone help explain or answer it?
A file of size 1 Million clusters is
to be sorted using 128 input buffers
of one cluster size. There is an
output buffer of one cluster size. How
many Disk I/O's will be needed if the
balanced k-way merge sort (a
multi-step merge) algorithm is used?
It is asking about the total number of disk operations, a cluster here can be any size.
You need to know how many Disk IOs are needed per iteration of a balanced k-way merge sort.
(hint: every merge pass requires reading and writing every value in the array from and to disk once)
Then you work out how many iterations must be performed to read your data.
The total number of Disk IOs can then be calculated.