Phoenix - Your AI AssistantHive Query Optimisation — Slow Queries Tuned & Explained
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4 days
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What you get with this Offer
I will analyse and optimise up to 5 slow HiveQL queries — identifying the specific causes of poor performance (full table scans, missing partitioning, data skew, suboptimal join strategies, non-vectorised execution, or missing bucketing) and delivering rewritten, optimised versions with execution plan analysis and before/after performance comparison. Hive queries that run for hours instead of minutes are almost always fixable; the bottlenecks are predictable and diagnosable once the execution plan is read correctly.
The optimisation covers EXPLAIN plan analysis, partition pruning verification, dynamic partition elimination, join type selection (broadcast, sort-merge, or skew join), ORC or Parquet vectorisation, CBO (Cost-Based Optimizer) statistics update, query rewrite for predicate pushdown, and a brief tuning report explaining every change made and the specific performance gain attributed to each.
This service suits data analysts, data engineers, and organisations running Hive on Hadoop or cloud platforms whose analytical queries are taking unacceptably long and creating downstream delays in reporting and modelling pipelines.
The optimisation covers EXPLAIN plan analysis, partition pruning verification, dynamic partition elimination, join type selection (broadcast, sort-merge, or skew join), ORC or Parquet vectorisation, CBO (Cost-Based Optimizer) statistics update, query rewrite for predicate pushdown, and a brief tuning report explaining every change made and the specific performance gain attributed to each.
This service suits data analysts, data engineers, and organisations running Hive on Hadoop or cloud platforms whose analytical queries are taking unacceptably long and creating downstream delays in reporting and modelling pipelines.
What the Freelancer needs to start the work
Please share the HiveQL queries to optimise, your table definitions and partitioning schema, execution plan output if available (EXPLAIN extended), your Hive version and execution engine (Tez, Spark, or MR), and current query runtimes and your target runtime.