Elasticsearch V/s Relational Database

Elasticsearch V/s Relational Databases

Agenda
● Basic Difference Between Elasticsearch And Relational Database
● Use Cases where Relational Db are not suitable
● Basic Terminology Of Elasticsearch
● Elasticsearch – CRUD operations

Basic Difference
● Elasticsearch is a No sql Database.
● It has no relations, no constraints, no joins, no transactional
behaviour.
● Easier to scale as compared to a relational Database.
Relational DB Elasticsearch
DataBase Index
Table Type
Row/Record Document
Column Name Field

Usecases where Relational Databases
are not suitable
● Relevance based searching
● Searching when entered spelling of search term is wrong
● Full text search
● Synonym search
● Phonetic search
● Log analysis

Relevance Based searcching
● By default, results are returned sorted by relevance—with the most
relevant docs first.
● The relevance score of each document is represented by a positive
floating-point number called the _score. The higher the _score, the
more relevant the document.
● A query clause generates a _score for each document. How that
score is calculated depends on the type of query clause.

Relevance Representation in ES
{
"_index": "test",
"_type": "product",
"_id": "AV0iKK_ZJJfvpLB9dSHl",
"_score": 0.51623213, ====> Relevance Score calculated by ES
"_source": {
"id": 2,
"name": "Red Shirt"
}
}

Wrong Spelling searching
Query
● {
"query": {
"match": {
"name": {
"query": "shrt",
"fuzziness": 2,
"prefix_length": 0
}
}
}
}
Result
{
"_index": "test",
"_type": "product",
"_id": "AV0iKKplJJfvpLB9dSHk",
"_score": 0.21576157,
"_source": {
"id": 1,
"name": "Shirt"
}
}

Full Text Search
● Whenever a full-text is given to Elasticsearch, special analyzers are
applied in order to simplify it and make it searchable.
● It does not store the text as it is visible. This means that the original
text would be modified following special rules before being stored in
the Inverted index.
● This process is called the “analysis phase,” and it is applied to all full-
text fields.

Synonym search
● Synonyms are used to broaden the scope of what is considered a
matching document.
● Perhaps no documents match a query for “Top Doctor's College,” but
documents that contain “Top Medical Institutions” would probably be
considered a good match.

Phonetic Searching
● Elasticsearch can search for words that sound similar, even if their
spelling differs.
● The Phonetic Analysis plugin provides token filters which convert
tokens to their phonetic representation using Soundex, Metaphone,
and a variety of other algorithms.
● Generally used while searching for names that sound similar.
Consider 'Smith', 'Smythe'. Elasticsearch analyser will produce same
tokens for both.

Log Analysis Using Elasticsearch
● Elasticsearch is vastly used as a centralized location for storing logs.
● For the purpose of indexing and searching logs, there is a bundled
solution offered at the Elasticsearch page - ELK stack, which stands
for elasticsearch, logstash and kibana.
●

Elasticsearch Terminology
● Elasticsearch: It is a horizontally distributed,data storage, search server,
aggregation engine, based on lucene library. It is written in java. Elasticsearch
5.5 is the latest one.
● Cluster: A cluster consists of one or more nodes which share the same cluster
name. Each cluster has a single master node which can be replaced if the
current master node fails.
● Node: A node is a running instance of elasticsearch which belongs to a cluster.
Multiple nodes can be started on a single server. At startup, a node will use
unicast to discover an existing cluster with the same cluster name and will try
to join that cluster.
● Primary Shard: Each document is stored in a single primary shard. When you
index a document, it is indexed first on the primary shard, then on all replicas
of the primary shard. By default, an index has 5 primary shards.

Elasticsearch Terminology Ctd.
● Replica Shard: Each primary shard can have zero or more replicas. A replica
is a copy of the primary shard. By Default there are 1 replica for each primary
shards.
● Document: A document is a JSON document which is stored in elasticsearch.
It is like a row in a table in a relational database. Each document is stored in
an index and has a type and an id. A document is a JSON object which
contains zero or more fields, or key-value pairs.
● ID: The ID of a document identifies a document. The index/type/id of a
document must be unique. If no ID is provided, then it will be auto-generated.
● Mapping: A mapping is like a schema definition in a relational database. Each
index has a mapping, which defines each type within the index, plus a number
of index-wide settings.

Create Index/Document
● Index Creation:
PUT employee
● Document Creation
POST employee/employee/1
{
"name" : "John"
}

Delete Document
● Delete By Id
DELETE employee/employee/1
● Delete By query
POST employee/employee/_delete_by_query
{
"query": {
"match": {
"name": "John"
}
}
}

Update Document
● Update By Id:
POST employee/employee/1/_update
{
"doc": {
"name": "Johny"
}
}
● Update By Query:
POST employee/_update_by_query
{
"script": {
"inline": "ctx._source.age++",
"lang": "painless"
},
"query": {
"match": {
"name": "john"
}
}
}

Read/Query Document
● Read By Id
GET employee/employee/1
● Read By query
GET employee/_search
{
"query": {
"match": {
"name": "John"
}
}
}

Elasticsearch V/s Relational Database

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