Data streams in Elasticsearch offer a powerful way to manage append-only time series data across multiple indices, providing a unified interface for indexing and querying while automating index lifecycle management. However, migrating existing data from an index to a data stream can be a daunting task, especially without losing any data. In this guide, we’ll explore what is a data stream, how to convert an index to a data stream seamlessly using Elasticsearch’s reindex API, ensuring data integrity throughout the process.
What is a Data Stream?
At its core, a data stream in Elasticsearch is a mechanism for storing append-only time series data across multiple indices while providing a single named resource for requests. Think of it as a streamlined way of organizing and accessing your time-based data, making it ideal for scenarios where new data is continuously being added, such as server logs, sensor readings, or application metrics.
How do Data Streams Work?
When you submit indexing and search requests to a data stream, Elasticsearch automatically routes these requests to the backing indices that store the data. These backing indices are managed by Elasticsearch’s index lifecycle management (ILM), which automates tasks like data retention, rollover, and deletion based on predefined policies. This automation reduces the operational overhead and ensures efficient resource utilization as your data grows over time.
Key Benefits of Data Streams
- Unified Interface: With a data stream, you interact with your time series data through a single named resource, simplifying indexing, querying, and management tasks.
- Automated Index Management: Index lifecycle management (ILM) automates the management of backing indices, allowing you to define policies for tasks like rollover, retention, and deletion based on your data lifecycle requirements.
- Optimized Performance: Data streams are optimized for append-only workloads, making them ideal for scenarios where new data is constantly being ingested. Elasticsearch efficiently handles indexing requests and queries, ensuring optimal performance even as your data grows.
- Cost Efficiency: By automating index lifecycle management and optimizing resource utilization, data streams help reduce infrastructure costs associated with managing time series data.
Why Convert to a Data Stream?
Before diving into the conversion process, let’s quickly recap the benefits of using a data stream:
- Unified Resource: Data streams provide a single named resource for requests, simplifying indexing and search operations.
- Ideal for Time Series Data: Data streams are well-suited for time series data such as logs, events, and metrics.
Prerequisites
Before proceeding with the conversion, ensure that:
- Your data contains a timestamp field, or one could be automatically generated.
- You mostly perform indexing requests, with occasional updates and deletes.
- You have a basic understanding of Elasticsearch concepts such as index templates and ILM.
Step-by-Step Conversion Process
1. Create Component Templates
Before creating the index template, set up component templates that define the mappings and settings for the data stream’s backing indices. These templates will ensure consistency across all indices within the data stream.
PUT _component_template/my-mappings
{
"template": {
"mappings": {
"properties": {
"@timestamp": {
"type": "date",
"format": "date_optional_time||epoch_millis"
},
"message": {
"type": "wildcard"
}
}
}
},
"_meta": {
"description": "Mappings for @timestamp and message fields",
"my-custom-meta-field": "More arbitrary metadata"
}
}
PUT _component_template/my-settings
{
"template": {
"settings": {
"index.lifecycle.name": "my-lifecycle-policy"
}
},
"_meta": {
"description": "Settings for ILM",
"my-custom-meta-field": "More arbitrary metadata"
}
}
2. Create an Index Template
Now, use the component templates to create an index template that defines the structure and settings for the data stream. This template will ensure consistency and automate the creation of backing indices.
PUT _index_template/my-index-template
{
"index_patterns": ["my-data-stream*"],
"data_stream": { },
"composed_of": [ "my-mappings", "my-settings" ],
"priority": 500,
"_meta": {
"description": "Template for my time series data",
"my-custom-meta-field": "More arbitrary metadata"
}
}
3. Create a Data Stream
Now, create a new data stream that will replace the existing index. Use the create data stream API or submit an indexing request targeting the stream’s name.
PUT _data_stream/my-data-stream
4. Reindex Data
Use the reindex API to copy documents from the existing index to the newly created data stream. Ensure that you include the timestamp field in each document and use op_type: create to ensure that existing documents are not updated.
POST _reindex
{
"source": {
"index": "existing-index"
},
"dest": {
"index": "my-data-stream",
"op_type": "create" // Ensure new documents are created
}
}
5. Verify Data Integrity
After reindexing, verify that all data has been successfully migrated to the data stream. Perform spot checks to ensure timestamps and document structures are intact. Check if the number of documents in the original index matches the number of documents in the new data stream.
GET existing-index/_count
GET my-data-stream/_count
6. Cleanup
Once you’ve confirmed the successful migration and verified data integrity, you can proceed with cleanup by deleting the old index to free up resources.
Conclusion
Converting an index to a data stream in Elasticsearch is a straightforward process with the reindex API. By following the steps outlined in this guide, you can seamlessly transition your existing data to a data stream without any loss or disruption. Leveraging the power of data streams, you can efficiently manage and query time series data while automating index lifecycle management for improved scalability and cost-effectiveness.