Elastic Stack Interview Questions
- We can create alerts for certain threshold values so that we can get alerts without doing a continuous monitoring
- We can configure different Beats to monitor system metrics, database metrics, and log metrics, etc.
- We can configure the APM to monitor the application metrics and any application related issues.
- We can Configure the JDBC plugin of Logstash to pull RDBMS, CSV, or NoSQL data into Elasticsearch to make it available in Kibana for creating visualizations on different KPIs
- We can use different third-party plugins that help us to get data from different sources. For example, we can use the Twitter plugin to get the Twitter feeds
- We can apply machine learning on our data set to find any data anomaly or future trends by analyzing the current dataset
4) How to create the index pattern in Kibana?
- Data collection pipeline tool.
- Gathers data from different sources.
- A tool to collect, process, and forward events and log messages.
- Data collection, enrichment, and transformation pipeline.
- Configurable input and output plugins
- Java
- PHP
- Perl
- Python
- .NET
- Ruby
- JavaScript
- Groovy
- Check our cluster, node, and index health, status, and statistics, etc.
- Administer our cluster, node, and index data and metadata.
- Perform CRUD (Create, Read, Update, and Delete) and search operations against our indexes.
- Execute advanced search operations such as paging, sorting, filtering, scripting, aggregations, etc.
If we want to create an index. For example, create an index named blogs
PUT /blogs?pretty
In the above expression, we have provided pretty which displays the output in a pretty format.
To delete the index, we can execute the following command:
DELETE /blogs?pretty
PUT /blogs/_doc/1?pretty
{
"topic": "introduction to Elasticsearch"
}
PUT /blogs/_doc/1?pretty
{
"topic": "introduction to Kibana"
}
We can delete a document from the index:
DELETE /blogs/_doc/1?pretty
In the above expression, we are deleting the document with id = 1
18) How can we search for any data in Elasticsearch?
We have basically two types of search APIs in Elasticsearch: 'request URI based' and 'request body-based'. In REST request URI we use to pass the search criteria in the URL itself like:
GET /blogs/technical/_search?q=topic:kibana
In the REST request body, we use to construct the search block and write the search query inside the query block of Elasticsearch like:
GET /blogs/technical/_search
{
"query": {
"term": {
"topic":"kibana"
}
}
}
So the URI based search is quite a basic search where we just want to search a keyword whereas in the request body we can construct the complex queries. So we have the query language to work with request body based searches.
19) Does Elasticsearch support the schema?
By nature Elasticseach is schema-less and we can create any document without explicitly creating the schema. But Elasticsearch supports the schema definition and it is good to create the schema before indexing the documents. Elasticsearch automatically guesses the data type for the indexed document but the guess can always not be right so it is good to define the schema.
20) What is Aggregation in Elasticsearch?
Aggregations provide us the option to group and extract statistics from our data. aggregations give the insight of our data and can be used for a wide range of problems like we can use Elasticsearch aggregations for creating a recommendation engine through which we can implement the recommendation system on any website.
Following is the syntax of aggregations:
"aggregationss|aggs" {
"<name of aggregations>" : {
"<type of aggregations>" : {
<body of aggregations>
}
}
}
This is the simplest representation of Elasticsearch aggregations. Now let us see what is the meaning of each line of the example.
- The first line denotes the aggregation keyword where we can use "aggregations" or "
- In the second line, we need to specify a name for the aggregation.
- In the third line, we need to specify the type of aggregation like terms, etc.
- Then we need to specify the actual aggregation body.
For more detail please refer to the blog: Introduction to in Elasticsearch Aggregation
21) What is Metrics Aggregation?
Metrics aggregation is those aggregation where we apply different types of metrics on fields of Elasticsearch documents like min, max, avg, top, and stats,
For more detail please refer to the blog: Metrics Aggregation in Elasticsearch
22) What is Bucket Aggregation?
Bucket aggregation is like a group by the result of the RDBMS query where we group the result with a certain field. In the case of Elasticsearch, we use to bucket data on the basis of certain criteria. In metrics aggregations, we can calculate metrics on a field while in the bucket we don't perform calculations but just create buckets with the documents which can be clubbed on the basis of certain criteria. In bucket aggregations, we can create sub aggregations.
For more detail please refer to the blog: Bucket Aggregation in Elasticsearch
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