I have 4 nodes,3 shards and 3 replicas in Elastic search cluster. I understands that data is split and stored into the shards. When query is requested, How data is fetched from all three shards?
Storage Structure :
Node 1: Shard1(Primary), Shard2(replica),Shard3(replica)
Node 2: Shard2(Primary), Shard1(replica)
Node 3: Shard3(Primary), Shard1(replica)
Node 4: Shard2(replica), Shard3(replica)
Thanks,
Kumar.
For search query, data can be searched in a primary shard or its replica shard, as Elastic never assigns the replica shard of a primary on the same data node, replica improves the high-availability also as replica contains the copy so data can be searched from replica shard as well.
But as your data is divided into 3 primary shards, so for search query at least it needs to search 3 shards(primary or primary's shard).
Do all shards (within index) have the same content?
If yes, more shards = longer propagation (save) time?
If no, when one of shards failed = data is incomplete when merging?
First, you need to understand what is sharding and why it's important in distributed systems like elasticsearch. You can read some good resources on shards here here and here.
Now Coming to your question,
Do all shards (within index) have the same content.
The answer, is no (assuming you are referring to primary shards here, of course, replica shard is just a copy of primary shard), let's take an example.
Your Index contains around 100 million docs and you have a 10 data nodes cluster, then you want to horizontally scale your index, so you started with the setting of 10 primary shards and 1 replica shards. In this case, elasticsearch will physically divide your data into 10 primary shards and each primary shard will be on a different node of a cluster as there are 10 data nodes and similarly every primary shards copy which is called replica of a shard which is on a different node of its primary shard.
Now coming to your follow-up question.
If yes, more shards = longer propagation (save) time? If no, when one
of shards failed = data is incomplete when merging?
As elasticsearch doesn't store the same data in all the primary shards, so more shards mean longer propagation or save time is invalid and also when one of the shards is failed then elasticsearch recover its data from its replica shard as it's present physically on a different data node server.
Bonus tip:- Shards are used to split your data and to make your application horizontal scalable, while the replica is to make your application is highly available as it contains the duplicated data, so the application can recover easily from the scenario you just asked in your follow-up question.
Let me know if you need any clarification or more details.
short answer:
Q-1: no
if-no: if index has not a replica, it affects the whole index but not other shards of the index .
please read this document:
https://www.elastic.co/guide/en/elasticsearch/reference/6.2/_basic_concepts.html
I have manually allocated 3 primary shards to a particular node in ElasticSearch. The replicas of these shards reside in different nodes. Now, let's say, primary shard number 2 goes down (for example, due to overflow of data) without the node on which it is residing going down. Then is it possible to retrieve the data residing on that particular shard, after I manually re-allocate it to a different node? If yes, how?
Yes.
Once the node with primary shard number 2 goes down then the replica shard on the other node will be upgraded to a primary shard - allowing you to retrieve the data. See here:
Coping with failure (ES Definitive Guide)
I am seeing the following index Unassigned which is very annoying. How do I get rid of it
Those unassigned shards are actually unassigned replicas of your actual shards from the master node.
The main purpose of replicas is for failover: if the node holding a primary shard dies, then a replica is promoted to the role of primary.
At index time, a replica shard does the same amount of work as the primary shard. New documents are first indexed on the primary and then on any replicas. Increasing the number of replicas does not change the capacity of the index.
However, replica shards can serve read requests. If, as is often the case, your index is search-heavy, you can increase search performance by increasing the number of replicas, but only if you also add extra hardware.
In order to assign these shards, you need to run a new instance of elasticsearch to create a secondary node to carry the data replicas. (The node can be master eligible or just a workhorse. Of course, you can set those configurations in the elasticsearch config files)
For more details about it you can refer to the official documentation and the Elasticsearch Definitive Guide (the work on it is still in progress but you will find what you are looking for here)
I don't understand the configuration of shards in ES.
I have few questions about sharding in ES:
The number of primary shards is configured through index.number_of_shards parameter, right?
So, it means that the number of shards are configured per index.
If so, if I have 2 indexes, then I will have 10 shards on the node ?
Assuming I have one node (Node 1) that configured with 3 shards and 1 replica.
Then, I create a new node (Node 2), in the same cluster, with 2 shards.
So, I assume I will have replica only to two shards, right?
In addition, what happens in case Node 1 is down, how the cluster "knows" that it should have 3 shards instead of 2? Since I have only 2 shards on Node 2, then it means that I lost the data of one of the shards in Node 1 ?
So first off I'd start reading about indexes, primary shards, replica shards and nodes to understand the differences:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/glossary.html
This is a pretty good description:
2.3 Index Basics
The largest single unit of data in elasticsearch is an index. Indexes
are logical and physical partitions of documents within elasticsearch.
Documents and document types are unique per-index. Indexes have no
knowledge of data contained in other indexes. From an operational
standpoint, many performance and durability related options are set
only at the per-index level. From a query perspective, while
elasticsearch supports cross-index searches, in practice it usually
makes more organizational sense to design for searches against
individual indexes.
Elasticsearch indexes are most similar to the ‘database’ abstraction
in the relational world. An elasticsearch index is a fully partitioned
universe within a single running server instance. Documents and type
mappings are scoped per index, making it safe to re-use names and ids
across indexes. Indexes also have their own settings for cluster
replication, sharding, custom text analysis, and many other concerns.
Indexes in elasticsearch are not 1:1 mappings to Lucene indexes, they
are in fact sharded across a configurable number of Lucene indexes, 5
by default, with 1 replica per shard. A single machine may have a
greater or lesser number of shards for a given index than other
machines in the cluster. Elasticsearch tries to keep the total data
across all indexes about equal on all machines, even if that means
that certain indexes may be disproportionately represented on a given
machine. Each shard has a configurable number of full replicas, which
are always stored on unique instances. If the cluster is not big
enough to support the specified number of replicas the cluster’s
health will be reported as a degraded ‘yellow’ state. The basic dev
setup for elasticsearch, consequently, always thinks that it’s
operating in a degraded state given that by default indexes, a single
running instance has no peers to replicate its data to. Note that this
has no practical effect on its operation for development purposes. It
is, however, recommended that elasticsearch always run on multiple
servers in production environments. As a clustered database, many of
data guarantees hinge on multiple nodes being available.
From here: http://exploringelasticsearch.com/modeling_data.html#sec-modeling-index-basics
When you create an index it you tell it how many primary and replica shards http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-create-index.html. ES defaults to 5 primary shard and 1 replica shard per primary for a total of 10 shards.
These shards will be balanced over how many nodes you have in the cluster, provided that a primary and it's replica(s) cannot reside on the same node. So if you start with 2 nodes and the default 5 primary shards and 1 replica per primary you will get 5 shards per node. Add more nodes and the number of shards per node drops. Add more indexes and the number of shards per node increases.
In all cases the number of nodes must be 1 greater than the configured number of replicas. So if you configure 1 replica you should have 2 nodes so that the primary can be on one and the single replica on the other, otherwise the replicas will not be assigned and your cluster status will be Yellow. If you have it configured for 2 replicas which means 1 primary shard and 2 replica shards you need at least 3 nodes to keep them all separate. And so on.
Your questions can't be answered directly because they are based on incorrect assumptions about how ES works. You don't add a node with shards - you add a node and then ES will re-balance the existing shards across the entire cluster. Yes, you do have some control over this if you want but I would not do so until you are much more familiar with ES operations. I'd read up on it here: http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index-modules-allocation.html and here: http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/cluster-reroute.html and here: http://exploringelasticsearch.com/advanced_techniques.html#advanced-routing
From the last link:
8.1.1 How Elasticsearch Routing Works
Understanding routing is important in large elasticsearch clusters. By
exercising fine-grained control over routing the quantity of cluster
resources used can be severely reduced, often by orders of magnitude.
The primary mechanism through which elasticsearch scales is sharding.
Sharding is a common technique for splitting data and computation
across multiple servers, where a property of a document has a function
returning a consistent value applied to it in order to determine which
server it will be stored on. The value used for this in elasticsearch
is the document’s _id field by default. The algorithm used to convert
a value to a shard id is what’s known as a consistent hashing
algorithm.
Maintaining good cluster performance is contingent upon even shard
balancing. If data is unevenly distributed across a cluster some
machines will be over-utilized while others will remain mostly idle.
To avoid this, we want as even a distribution of numbers coming out of
our consistent hashing algorithm as possible. Document ids hash well
generally because they are evenly distributed if they are either UUIDs
or monotonically increasing ids (1,2,3,4 …).
This is the default approach, and it generally works well as it solves
the problem of evening out data across the cluster. It also means that
fetches for a single document only need to be routed to the shard that
document hashes to. But what about routing queries? If, for instance,
we are storing user history in elasticsearch, and are using UUIDs for
each piece of user history data, user data will be stored evenly
across the cluster. There’s some waste here, however, in that this
means that our searches for that user’s data have poor data locality.
Queries must be run on all shards within the index, and run against
all possible data. Assuming that we have many users we can likely
improve query performance by consistently routing all of a given
user’s data to a single shard. Once the user’s data has been
so-segmented, we’ll only need to execute across a single shard when
performing operations on that user’s data.
Yes, the number of shards is per index. So if you had 2 indexes, each with 5 shards, then yes, you would have a total of 10 shards distributed across all your nodes.
The number of shards is unrelated to the number of nodes in the cluster. If you have 3 shards and one node, obviously all 3 shards will reside on that one node. However, if you then add an additional node, more shards are not magically created and you can't specify that a certain number of shards should reside on that new node. Rather, the existing shards are distributed as evenly as possible across all nodes resulting in one node with 2 shards and one node with 1 shard, for a total of 3. If you added a third node, then each node would house 1 shard for a total of 3. In other words, the number of shards is fixed and doesn't scale as you add more nodes.
As to your final question, it's based on a false premise, so it's difficult to answer. Rather, lets take the example of above of three shards and two nodes. In that setup, one node will house 2 shards and one node will house 1 shard. If either of those nodes go down, your cluster goes down, because neither has a complete set of shards. The first node is missing 1 shard and the second node is missing 2. This is where replicas come in. By adding replicas, you can ensure that each node ends up with a full set of shards. For example, if you added 1 replica in the above scenario, then the first node would have 2 active shards and 1 replica of the third that lives on the other node. The second node would have 1 active shard and 1 replica each of the two that live on the first. As a result, if either node went down, the cluster can merely activate the replicas and still continue working.
1) Yes, the number of shards is configured per index. It is a static operation and should be done while creating an index. If you want to change the number of shards at a later point of time, you have to reindex the document again and takes time.
2) The number of shards don't depend on number of nodes in you cluster. Lets say you are a book seller website. You have 100 books to sell. your website have an elastic cluster with 3 nodes. you create a book index with 5 shards. Each and very shard contains 20 books. 2 shards will reside on node1, 2 shards will reside in node2 and 1 shard will reside in node3. now let's say node 2 has gone down. But, still we have 2 shards in node 1 and 1 shard in node 3. Querying elastic search will still return the data on node 1 and node 3. i.e, 60 books data will still be available. 40 books data is lost.
But, the overall cluster status will be red meaning cluster is partially functioning, but somedata is not available.
To make the system fault tolerant you can configure replicas. By default elasticsearch makes one replica of each shard. So in this case if the default configuration is not over written the copy of 2 shards on node 2 will be replicated either on node 1 or node 3 and they become the primary shards when node 2 is not available. So all the data is available even when node 2 is down.
in this case the overall cluster health will be yellow, meaning cluster is still functional. But some nodes are lost.
Answer 1) yes you will have 10 shards fr 2 index with 5 shards.
Answer 2) I think you confused with shards and index.
Shards are split piece for index not for node.
If you create a index with 3 shards and 1 replica.
You will get 3 primary shard and 3 replica shards.
If you start new node the shards will be balanced with new node.So you will have 3 shard in old node and 3 shards in new node.
If old node fails you will survive with new node data.It will have exact copy of old node.
To understand basic concepts of elasticsearch refer
HOpe it helps..!