I want to transfer data of certain conditions from elasticsearch to kafka.
Is there any way to do that?
I use logstash for transferring data from elasticsearch to kafka finally.
logstash is alse a common framework for ingesting transforming and stashing data, which offers a variety of input and output plugins including elasticsearch input and kafka output.
Besides, elasticsearch input plugins also provide a schedule mechanism， which is very convenient for ingesting incremental data.
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I am recieving a very complex json inside a topic message, so i want to do some computations with it using SMTs and send to different elasticsearch indice documents. is it possible?
I am not able to find a solution for this.
The Elasticsearch sink connector only writes to one index, per record, based on the topic name. It's explicitly written in the Confluent documentation that topic altering transforms such as RegexRouter will not work as expected.
I'd suggest looking at logstash Kafka input and Elasticsearch output as an alternative, however, I'm still not sure how you'd "split" a record into multiple documents there either.
You may need an intermediate Kafka consumer such as Kafka Streams or ksqlDB to extract your nested JSON and emit multiple records that you expect in Elasticsearch.
I do not have the ability to do much but receive unstructured syslogs from Kafka which have been produced with logstash.
When I attach logstash as a consumer, these syslogs are all over the place and contain half a dozen patterns or more which very wildly. This is something more fitting to be run somehow streamed with an awk filter since the programmatic approach to passing incoming messages is actually quite sttisghtforward with such a tool.
Does anyone have any input on how one could attach a consumer to a Kafka topic and procure incoming logs and ship these logs in am intelligent way towards an elasticsearch clister?
Try to use grok expressions in your LOGSTASH config to parse the logs https://logz.io/blog/logstash-grok/ . This should allow you to filter, transform or drop data.
Or use something like CRIBL in between KAFKA and ELASTIC https://docs.cribl.io/stream/about/
Note on the CRIBL page how under sources KAFKA is one of the supported sources and ELASTIC is one of the supported destinations. This should allow to transform your data before ingesting it into ELASTIC.
Elastic docs mentions that Elastic data stream supports the following types: logs, metrics and synthetics. What are the differences between these types?
I tested storing some data as logs and metrics types separately and I don't see any difference when querying the data. Are both types interchangeable or are they stored differently?
Those are different types of data sets collected by the new Elastic Agent and Fleet integration:
The logs type is for logs data, i.e. what Filebeat used to send to Elasticsearch.
The metrics type is for metric data, i.e. what Metricbeat used to send to Elasticsearch
The synthetics type is for uptime and status check data, i.e. what Heartbeat used to send to Elasticsearch.
Now, with Fleet, all the Beats have been refactored into a single agent called Elastic Agent which can do all of that, so instead of having to install all the *Beats, you just need to install that agent and enable/disable/configure whatever type of data you want to gather and index into Elasticsearch. All of that through a nice, powerful and centralized Kibana UI.
Beats are now simply Elastic Agent modules that you can enable/disable and they will all write their data into indexes that follow a new taxonomy and naming scheme, which is based on those types, which are nothing more than a generic way describing the nature of data they contain, i.e. logs, metrics, synthetics, etc.
When I transfer or stream two and three tables then I can easily map in Elasticsearch but can I map automatically map topics to index
I have streamed data from PostgreSQL to ES by mapping manually topic.index.map=topic1:index1,topic2:index2, etc.
Can I map automatically whatever topics send by producer then consumer consume in ES connector automatically?
By default, the topics map directly to an index of the same name.
If you want "better" control, you can use RegexRouter in a transforms property
To quote the docs
topic.index.map
This option is now deprecated. A future version may remove it completely. Please use single message transforms, such as RegexRouter, to map topic names to index names
If you cannot capture a single regex for each topic in the connector, then run more connectors with a different pattern
How are events indexed and stored by Elasticsearch when using ELK (Elastichsearch, Logstash, Kibana)
How does Elasticsearch work in ELK
Looks like you got downvoted for not just reading up at elastic.co, but...
logstash picks up unstructured data from log files and other sources, transforms it into structured data, and inserts it into elasticsearch.
elasticsearch is the document repository. While it's not useful for log information, it's a text engine at heart and can analyze the data (tokenization, stop words, stemming, etc).
kibana reads from elasticsearch and allows you to explore the data and make dashboards.
That's the 30,000-ft overview.
Elasticsearch have the function of database on ELK Stack.
You can read more information about Elasticsearch and ELK Stack here: https://www.elastic.co/guide/en/elasticsearch/guide/current/index.html.
first of all you will have logs file that you used to write system logs on it
for example when you add new record to database you will write the record in any form you need to log file like
date,"name":"system","serial":"1234" .....
after that you will add your configuration in logstash to parse the data from the logs
and it will be like
name : system
.....
and the data will saved in elastic search
kibana is used to preview the elastic search data
and you can use send a request to elasticsearch with the required query and get your data from it