Kernel version | Description |
V1.9.0 | New and optimized features The framework for concurrent queries is reconstructed and optimized for Kernel-enhanced Edition clusters. The query duration is reduced. Memory can be reused, and high Java virtual machine (JVM) memory usage and garbage collection (GC) overhead are improved. This increases resource utilization. The duration of the fetch phase in the concurrent fetch of raw text is reduced. For example, if the size parameter is set to 10000, the duration of the fetch phase can be reduced by 6 to 10 times, and the overall duration can be reduced by 50%. The following types of aggregations are supported in queries: percentile aggregations, percentile rank aggregations, sampler aggregations, diversified sampler aggregations, significant text aggregations, geodistance aggregations, geohash grid aggregations, geotile grid aggregations, geobounds aggregations, geocentroid aggregations, and scripted metric aggregations.
Fields such as traceId and a query duration-related field are added to end-to-end access logs. You can use traceId to concatenate query processes. The custom index structure and mapping parsing of raw text are optimized. This doubles write performance for raw text.
The following code can be used to enable caching. This can resolve the following issue: caching is not enabled for subqueries in some scenarios where few primary queries but a large number of subqueries are performed. PUT _cluster / settings
{
"persistent": {
"search.query_cache_get_wait_lock_enable": "true",
"search.query_cache_skip_factor": "200000000"
}
}
Data inconsistency between primary shards and replica shards is optimized in scenarios with k-nearest neighbors (k-NN) queries. Bug fixes
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V1.8.0 | The aliyun-timestream plug-in is provided. The plug-in is used to enhance the storage and usage performance of time series data. The plug-in allows you to create, modify, query, and delete time series indexes, execute PromQL statements to query data stored in Elasticsearch, and write data to time series indexes by using the InfluxDB line protocol. The plug-in helps simplify the operations that are required to manage time series data in time series scenarios. For more information, see Overview of aliyun-timestream, Integrate Elasticsearch with Prometheus and Grafana based on aliyun-timestream to implement integrated monitoring, and Integrate aliyun-timestream with the InfluxDB line protocol. |
V1.7.0 | New features The analytic-search plug-in is provided, which significantly improves the query performance in log-related scenarios. The following descriptions provide the details: Index merging policies and date histogram aggregation policies are optimized. This improves the performance of unconditional or single-condition queries by more than six times in log query scenarios, such as queries performed on the Discover page of the Kibana console. In scenarios where more than 1 TB data is added every day, the time to complete a query is reduced from minutes to 5 seconds or even less. Concurrent queries are optimized. For concurrent queries, concurrent data recall is supported. This improves resource utilization and reduces the average time required for data recall in log-related scenarios by 50%. Read-only small segments are continuously merged before forced merging. This improves query performance by 20%.
Performance improvements The lightweight compression algorithm LZ4 is used to transmit write requests between client nodes and data nodes. This reduces the network bandwidth overheads of nodes by 30%. Forced merging can be performed in parallel for shards. This reduces the duration of forced merging. Large data blocks in raw text can be compressed, and parameters for the zstd compression algorithm are optimized. This reduces the size of raw text by 8%. In addition, the Patched Frame of Reference (PFOR) method is supported for Lucene postings. This reduces the size of an index by 3%.
Bug fixes
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V1.6.0 | The source_reuse_doc_values feature is added to the aliyun-codec plug-in to further reduce index sizes and costs. For more information, see Use the aliyun-codec plug-in. The aliyun-qos plug-in is updated to V2.0 to support finer-grained throttling types and parameters. For more information, see Use the aliyun-qos plug-in.
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V1.5.0 | The aliyun-codec plug-in is provided to enhance the compression performance of the kernel for a cluster. For more information, see Use the aliyun-codec plug-in. The bug related to the search_as_you_type field type is fixed. For more information, see search_as_you_type.
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V1.4.0 | The aliyun-knn plug-in is updated to improve write performance. The plug-in supports script queries and is integrated with the optimized capabilities of the related hardware to improve the vector search feature. The aliyun-qos plug-in is optimized to improve cluster-level throttling. When you use this plug-in, you do not need to focus on the topology and load of the nodes in your Elasticsearch cluster. Traffic is automatically distributed to the nodes. This improves cluster usability and stability.
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V1.3.0 | The slow query isolation feature is provided to reduce the impact of anomalous queries on cluster stability. The gig plug-in is provided to perform a switchover within seconds after an exception occurs on a cluster. This plug-in prevents query jitters caused by anomalous nodes.
Note For Elasticsearch V7.10.0 clusters of the Standard Edition, the gig plug-in is integrated into the aliyun-qos plug-in. The aliyun-qos plug-in is installed by default. The physical replication feature is provided to improve the write performance of indexes that have replica shards. The pruning feature is provided for time series indexes to improve the query performance of the indexes. The access logs of clusters can be viewed. These logs contain fields such as Time, Node IP, and Content. You can use these logs to troubleshoot issues and analyze requests. The scheduling performance of dedicated master nodes is improved by 10 times. Each dedicated master node can schedule more shards.
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