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IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
A data stream lets you store append-only time series
data across multiple indices while giving you a single named resource for
requests. Data streams are well-suited for logs, events, metrics, and other
continuously generated data.
You can submit indexing and search requests directly to a data stream. The
stream automatically routes the request to backing indices that store the
stream s data. You can use index lifecycle management (ILM) to
automate the management of these backing indices. For example, you can use
ILM to automatically move older backing indices to less expensive
hardware and delete unneeded indices. ILM can help you reduce costs and
overhead as your data grows.
Backing indicesedit
A data stream consists of one or more hidden, auto-generated
backing indices.
Each data stream requires a matching index template. The
template contains the mappings and settings used to configure the stream s
backing indices.
Every document indexed to a data stream must contain a @timestamp field,
mapped as a date or date_nanos field type. If the
index template doesn t specify a mapping for the @timestamp field, Elasticsearch maps
@timestamp as a date field with default options.
The same index template can be used for multiple data streams. You cannot
delete an index template in use by a data stream.
Read requestsedit
When you submit a read request to a data stream, the stream routes the request
to all its backing indices.
Write indexedit
The most recently created backing index is the data stream’s write index.
The stream adds new documents to this index only.
You cannot add new documents to other backing indices, even by sending requests
directly to the index.
You also cannot perform operations on a write index that may hinder indexing,
such as:
Rolloveredit
When you create a data stream, Elasticsearch automatically creates a backing index for
the stream. This index also acts as the stream s first write index. A
rollover creates a new backing index that becomes the
stream s new write index.
We recommend using ILM to automatically
roll over data streams when the write index reaches a specified age or size.
If needed, you can also manually roll over
a data stream.
Generationedit
Each data stream tracks its generation: a six-digit, zero-padded integer that
acts as a cumulative count of the stream s rollovers, starting at 000001.
When a backing index is created, the index is named using the following
convention:
.ds- data-stream - generation
Backing indices with a higher generation contain more recent data. For example,
the web-server-logs data stream has a generation of 34. The stream s most
recent backing index is named .ds-web-server-logs-000034.
Some operations, such as a shrink or
restore, can change a backing index s name.
These name changes do not remove a backing index from its data stream.
Append-onlyedit
Data streams are designed for use cases where existing data is rarely,
if ever, updated. You cannot send update or deletion requests for existing
documents directly to a data stream. Instead, use the
update by query and
delete by query APIs.
If needed, you can update or deletedocuments by submitting requests directly to the document s backing index.
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