I have a logstash pipeline ingesting data from Kafka. Currently it's searching multiple Kafka topics based on a pattern and outputting to indexes dynamically named with the topic. I don't know how to dynamically create a data view for each index pattern or if this is thee right approach. I'd like each pattern to be separate when developers try to query them. Traditionally we've used data views but maybe there's a better method.
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I have recently started to use OpenSearch and having a few newbie questions.
What is the difference between Index, Index Pattern and Index template? (Some examples would be really helpful to visualize and differentiate these terminologies).
I have seen some indexes with data streams and some without data streams. What exactly are data streams and why some indexes have them and the others do not.
Tried reading a few docs, watching a few youTube videos. But it's getting a little confusing as I do not have much hands on experience with OpenSearch.
(1)
An index is a collection of JSON documents that you want to make searchable. To maximise your ability to search and analyse documents, you can define how documents and their fields are stored and indexed (i.e., mappings and settings).
An index template is a way to initialize with predefined mappings and settings new indices that match a given name pattern - e.g., any new index with a name starting with "java-" (docs).
An index pattern is a concept associated with Dashboards, the OpenSearch UI. It provides Dashboards with a way to identify which indices you want to analyse, based on their name (again, usually based on prefixes).
(2)
Data streams are managed indices highly optimised for time-series and append-only data, typically, observability data. Under the hood, they work like any other index, but OpenSearch simplifies some management operations (e.g., rollovers) and stores in a more efficient way the continuous stream of data that characterises this scenario.
In general, if you have a continuous stream of data that is append-only and has a timestamp attached (e.g., logs, metrics, traces, ...), then data streams are advertised as the most efficient way to model this data in OpenSearch.
I had just started learning about ElasticSearch and Kibana. I created a Winlogbeat dashboard where the logs are working fine. I want to import additional data (CSV data) which I created using Python. I tried uploading the CSV file but I am only allowed to create a separate index and not merge it with the Winlogbeat data. Does anyone know how to do this?
Thanks in advance
In many use cases, you don't need to actually combine into a single index. Here's a few ways you can show combined data, in approximate order of complexity:
Straightforward methods, using separate indices:
Use multiple charts on a dashboard
Use multiple indices in a single chart
More complex methods that combine data into a single index:
Pivot indices using Data Transforms
Combine at ingest-time
Roll your own
Use multiple charts on a dashboard
This is the simplest way: ingest your data into separate indices, make separate visualizations for them, then add those visualisations to one dashboard. If the events are time-based, this simple approach could be all you need.
Use multiple indices in a single chart
Lens, TSVB and Timelion can all use multiple data sources. (Vega can too, but that's playing on hard mode)
Here's an official Elastic video about how to do it in Lens: youtube
Create pivot indices using Data Transforms
You can use Elasticsearch's Data Transforms functionality to fetch, combine and aggregate your disparate data sources into a combined data structure which is then available for querying with Kibana. The official tutorial on Transforming the eCommerce sample data is a good place to learn more.
Combine at ingest-time
If you have (or can add) Logstash in the mix, you have several options for combining datasets during the filter phase of your pipelines:
Using a file-based lookup table and the translate filter plugin
By waiting for related documents to come in then outputting a combined document to Elasticsearch with the aggregate filter plugin
Using external lookups with filter plugins like elasticsearch or http
Executing arbitrary ruby code using the ruby filter plugin
Roll your own
If you're generating the CSV file with a Python program, you might want to think about incorporating the python Elasticsearch DSL lib to run queries on the winlogbeat data, then ingest it in its combined state (whether via a CSV or other means).
Basically, Winlogbeat is a data shipper to Elasticsearch. Which ships windows specific data to an index named winlogbeat with a specific schema and document structure.
You can't merge another document with a different schema into winlogbeat index.
If your goal is to correlate different data points. Please use Time-series visual builder to overlay two different datasets to visualize.
Hi I am new to Elastic stack. This is basically a design based question. We have lot of Kafka Topics (>500) and each of them store json as data exchange format. Now we are planning to build a Kafka Consumer and dump all the records/jsons into a Single Index. We have some requirements but to begin with the most important one being, able to search through all the relevant jsons based on few important field values. For example if I have multiple jsons having field correlation id with a value XYZ, then if I enter XYZ then it should be able to search through all the topics.
Also as an additional question, since we are using Kibana do we have some inbuilt visualization for this search thing so that we dont need to build our own UI? This is simply for management searching specific values and need not be very fancy UI.
What should be the best thing to do, is having a single index the best design? What all things we need to consider? I read about the standard Analyzer and am wondering if that is enough for our purpose.
Assumption- All Kafka topics will store jsons and each json can be of different formats. Some might have lots of nesting, some might have nested objects. Some might be simple.
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
Consider the following use case:
I want the information from one particular log line to be indexed into Elasticsearch, as a document X.
I want the information from some log line further down the log file to be indexed into the same document X (not overriding the original, just adding more data).
The first part, I can obviously achieve with filebeat.
For the second, does anyone have any idea about how to approach it? Could I still use filebeat + some pipeline on an ingest node for example?
Clearly, I can use the ES API to update the said document, but I was looking for some solution that doesn't require changes to my application - rather, it is all possible to achieve using the log files.
Thanks in advance!
No, this is not something that Beats were intended to accomplish. Enrichment like you describe is one of the things that Logstash can help with.
Logstash has an Elasticsearch input that would allow you to retrieve data from ES and use it in the pipeline for enrichment. And the Elasticsearch output supports upsert operations (update if exists, insert new if not). Using both those features you can enrich and update documents as new data comes in.
You might want to consider ingesting the log lines as is to Elasticearch. Then using Logstash, build a separate index that is entity specific and driven based on data from the logs.