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Light edits to data streams

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aetter 2021-08-31 10:50:48 -07:00
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_opensearch/data-streams.md
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If you're ingesting continuously generated time-series data such as logs, events, and metrics into OpenSearch, you're likely in a scenario where the number of documents grows rapidly and you don't need to update older documents.
A typical workflow to manage time-series data involves multiple steps such as creating a rollover index alias, defining a write index, and defining common mappings and settings for the backing indices.
A typical workflow to manage time-series data involves multiple steps, such as creating a rollover index alias, defining a write index, and defining common mappings and settings for the backing indices.
Data streams simplify this bootstrapping process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data, and ensuring that each document has a timestamp field.
Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field.
A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. You can use [ISM]({{site.url}}{{site.baseurl}}/im-plugin/ism/index/) policies to automatically handle rollovers or deletion of indices in a data stream, based on your use case.
A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. [ISM]({{site.url}}{{site.baseurl}}/im-plugin/ism/index/) policies let you automatically handle index rollovers or deletions.
## Get started with data streams