hello guys welcome to the lecture basic concept there are few concepts that are core to elasticsearch understanding these concepts is very important so that it is very easy to learn elasticsearch process so the first one is near-real-time sometimes it is called as NRT near-real-time what this means is there is a slight latency or some delay from the time you index a document until the time it will be searchable and the latency are the delay it is not much it’s normally one second but there is some delay between the real data and the actual searchable data so that is the reason elastic search is Carles near real time data next one is cluster a cluster is nothing but it is a collection of one or more servers are nodes that together holds our entire data and provides searching capabilities across all the nodes so a cluster is identified by a unique name by default if you install elastic search the name will be elastic search so remember that default name for cluster is elastic search this is really important because we can till a node so which part of a cluster it belongs to so consider in our system there are two set of clusters are available then we should explicitly tell one of the node are one of the server it belongs to which cluster so in such scenarios the we have to explicitly mention the name of the cluster so usually then we are in real time so when we are dealing with stage bill a QA and production you can give names like elasticsearch do elastics first stage so that it will be different differentiable and unique and the next one is node a node is nothing but it’s a single server that is a part of our cluster and this node R this server stores our data and participates in the clusters indexing and search capabilities so and just now I mentioned a cluster have a unique name similarly a node is also identify its name and a node can be configured to join a specific cluster by using the cluster name in a single cluster we can have as many nodes as we want so the next one is index an index is a collection of documents that has somewhat similar characteristics for example so can have in index for all customer data and we can have another index for all product data so it is useful to differentiate the data so once again index is nothing but a collection of similar documents so in the next sections we will see what the correlation between the elasticsearch and database so that if you already work with the database it will be very easy to understand so and here the next one is type so I already told you what is an index so within the index we can define one or more types a type is nothing but it’s a logical category our partition in general a type is defined for documents that have set of common fields for example let’s assume we are running a blogging platform and store all our data in a single index in this index we may define a type for user data another type for block data and completely different type for our comments data so this type can be anything it’s like in a company you can have a type like Associates another type like vendors so completely other type contractors so just it will differentiate the type of the documents so finally the document the document is a basic unit of information that can be indexed for example we can have a document for a single customer another document for a single product and yet another for a single order usually this document is expressed in JSON JSON is nothing but JavaScript object notation so people chose this because it is lightweight so the other concept is charts and replicas so an index can potentially store a large amount of data sometimes that can exceed our the hardware limits so to solve these problems elastic switch provides the ability to subdivide our index into multiple pieces it’s like if you have any important data or if you have a huge data you should have keeping entire data at one point we are taking a sub parts and putting in each nodes so these each node is called as shards means the subdivide I mean the subdivision of our index is called shard and then we create an index we can simply define the number of shares we want so this chart can be defined at the time of creating index so why charting is important because it allows two important features the first one is horizontally scalable and next one is it allows us to distribute and paralyze operations across charts so the another thing is the replica a replica is nothing but just a backup so each index can be split into multiple shard and each index also be replicated one or more times zero or more times so a replica is nothing but just having a backup another copy in the next lecture we will see how can we install and cleb it elasticsearch thank you

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