- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.10
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Setting JVM options
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- HTTP
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- License settings
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- Transforms settings
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- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
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- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
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- OnError and OnOutOfMemoryError checks
- Early-access check
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- Bootstrap Checks for X-Pack
- Starting Elasticsearch
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- Discovery and cluster formation
- Add and remove nodes in your cluster
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- Remote clusters
- Set up X-Pack
- Configuring X-Pack Java Clients
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
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- Phonetic
- Porter stem
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- Remove duplicates
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- Synonym
- Synonym graph
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- Character filters reference
- Normalizers
- Index templates
- Data streams
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- Search your data
- Query DSL
- Aggregations
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- Auto-interval date histogram
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- Terms
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- EQL
- SQL access
- Overview
- Getting Started with SQL
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- Security
- SQL REST API
- SQL Translate API
- SQL CLI
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- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Manage Filebeat time-based indices
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Resolve lifecycle policy execution errors
- Start and stop index lifecycle management
- Manage existing indices
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- Secure a cluster
- Overview
- Configuring security
- User authentication
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- Active Directory user authentication
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- Integrating with other authentication systems
- Enabling anonymous access
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- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
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- Granting privileges for data streams and index aliases
- Mapping users and groups to roles
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- Customizing roles and authorization
- Enabling audit logging
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- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
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- Common SSL/TLS exceptions
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- Limitations
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- cat aliases
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- Cluster APIs
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- Add index alias
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
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- Get trained models stats
- Start data frame analytics jobs
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- Migration APIs
- Reload search analyzers API
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- Authenticate
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- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- Transform APIs
- Usage API
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- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.10.2
- Elasticsearch version 7.10.1
- Elasticsearch version 7.10.0
- Elasticsearch version 7.9.3
- Elasticsearch version 7.9.2
- Elasticsearch version 7.9.1
- Elasticsearch version 7.9.0
- Elasticsearch version 7.8.1
- Elasticsearch version 7.8.0
- Elasticsearch version 7.7.1
- Elasticsearch version 7.7.0
- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
- Dependencies and versions
How transform checkpoints work
editHow transform checkpoints work
editEach time a transform examines the source indices and creates or updates the destination index, it generates a checkpoint.
If your transform runs only once, there is logically only one checkpoint. If your transform runs continuously, however, it creates checkpoints as it ingests and transforms new source data.
To create a checkpoint, the continuous transform:
-
Checks for changes to source indices.
Using a simple periodic timer, the transform checks for changes to the source indices. This check is done based on the interval defined in the transform’s
frequency
property.If the source indices remain unchanged or if a checkpoint is already in progress then it waits for the next timer.
-
Identifies which entities have changed.
The transform searches to see which entities have changed since the last time it checked. The
sync
configuration object in the transform identifies a time field in the source indices. The transform uses the values in that field to synchronize the source and destination indices. -
Updates the destination index (the data frame) with the changed entities.
The transform applies changes related to either new or changed entities to the destination index. The set of changed entities is paginated. For each page, the transform performs a composite aggregation using a
terms
query. After all the pages of changes have been applied, the checkpoint is complete.
This checkpoint process involves both search and indexing activity on the cluster. We have attempted to favor control over performance while developing transforms. We decided it was preferable for the transform to take longer to complete, rather than to finish quickly and take precedence in resource consumption. That being said, the cluster still requires enough resources to support both the composite aggregation search and the indexing of its results.
If the cluster experiences unsuitable performance degradation due to the transform, stop the transform and refer to Performance considerations.
Error handling
editFailures in transforms tend to be related to searching or indexing. To increase the resiliency of transforms, the cursor positions of the aggregated search and the changed entities search are tracked in memory and persisted periodically.
Checkpoint failures can be categorized as follows:
- Temporary failures: The checkpoint is retried. If 10 consecutive failures occur, the transform has a failed status. For example, this situation might occur when there are shard failures and queries return only partial results.
- Irrecoverable failures: The transform immediately fails. For example, this situation occurs when the source index is not found.
-
Adjustment failures: The transform retries with adjusted settings.
For example, if a parent circuit breaker memory errors occur during the
composite aggregation, the transform receives partial results. The aggregated
search is retried with a smaller number of buckets. This retry is performed at
the interval defined in the
frequency
property for the transform. If the search is retried to the point where it reaches a minimal number of buckets, an irrecoverable failure occurs.
If the node running the transforms fails, the transform restarts from the most recent persisted cursor position. This recovery process might repeat some of the work the transform had already done, but it ensures data consistency.
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