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:StorageEngine/RollupTurn fine-grained time-based data into coarser-grained dataTurn fine-grained time-based data into coarser-grained data>enhancementTeam:AnalyticsMeta label for analytical engine team (ESQL/Aggs/Geo)Meta label for analytical engine team (ESQL/Aggs/Geo)
Description
Description
We would like to measure performances of downsampling operations using Rally. For this purpose we need to include a new challenge to the existing tsdb Rally track. The new challenge will measure latency for a limited set of downsampling operations using different values for the fixed_interval parameter. As part of the analysis we need to collect JFR recordings and flame graphs so that we can spot areas of the code we can improve.
Right now the tsdb track uses a dataset including more than 116M documents for a total JSON file size of more than 120 GB, which results in a 32.5 GB index. The plan is to measure downsampling latency with a single thread implementation, a single node Elasticsearch cluster and a single shard.
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:StorageEngine/RollupTurn fine-grained time-based data into coarser-grained dataTurn fine-grained time-based data into coarser-grained data>enhancementTeam:AnalyticsMeta label for analytical engine team (ESQL/Aggs/Geo)Meta label for analytical engine team (ESQL/Aggs/Geo)