How to effectively optimize queries in Presto?

Presto query optimization refers to the techniques and processes used to improve the performance and efficiency of queries executed in Presto, an open-source distributed SQL query engine. Query optimization aims to minimize query execution time and resource utilization while maximizing throughput and scalability.

Here are some key aspects of Presto query optimization.

1. Predicate Pushdown: Presto's optimizer pushes predicates (filters) as close to the data source as possible. This reduces the amount of data transferred over the network and improves query performance by minimizing the data processed. Example:

   SELECT col1, col2
   FROM table
   WHERE col3 = 'value'

   
   
2. Join Strategies: Presto supports various join strategies, including broadcast join, shuffle join, and semi-join. The optimizer analyzes the join conditions, data sizes, and available memory to choose the appropriate join strategy. For example, a broadcast join is effective when one side of the join is small enough to fit in memory.

   Example:

   SELECT t1.col1, t2.col2
   FROM table1 t1
   JOIN table2 t2 ON t1.id = t2.id
3. Aggregation Optimization: Presto optimizes aggregations by leveraging techniques such as partial aggregation and pipe lining. Partial aggregation reduces the amount of data processed by performing aggregations at intermediate stages, while pipe lining allows data to be processed incrementally, reducing memory requirements.
   Example:
   

   SELECT col1, COUNT(col2)
   FROM table
   GROUP BY col1

4. Predicate Simplification: Presto simplifies complex expressions and predicates during query optimization. This simplification eliminates unnecessary computations and reduces the complexity of the query, leading to improved performance.
   Example:
   

   SELECT col1, col2
   FROM table
   WHERE col3 = 1 AND (col4 > 10 OR col5 < 5)

5. Dynamic Filtering: Presto supports dynamic filtering, where information from one stage of the query execution is used to optimize subsequent stages. For example, if a filter applied early in the query reduces the data size significantly, this information can be used to optimize subsequent joins and aggregations.
   Example:
   

   SELECT t1.col1, t2.col2
   FROM table1 t1
   JOIN table2 t2 ON t1.id = t2.id
   WHERE t1.col3 = 'value'

6. Statistics and Cost Estimation: Presto relies on statistics and cost estimation to make informed decisions during query optimization. Accurate statistics about data distribution, column cardinality, and data size help the optimizer estimate costs more effectively and choose optimal query plans.
   Example:
   

   SELECT t1.col1, t2.col2
   FROM table1 t1
   JOIN table2 t2 ON t1.id = t2.id
   WHERE t1.col3 = 'value' AND t2.col4 > 100

7. Query Plan Caching: Presto can cache query plans for reusability. If a similar query is executed again, Presto can reuse the cached plan instead of re-optimizing the query, which can save processing time. Example: -- Query 1 SELECT col1, col2 FROM table WHERE col3 = 'value'
   
   When you execute the above query for the first time, Presto's optimizer will analyze the query, generate an optimized query plan, and execute it. During this process, the query plan can be cached for reusability.

   Now, let's assume the same query is executed again:
   

   -- Query 2
   SELECT col1, col2
   FROM table
   WHERE col3 = 'value'

   Instead of re-optimizing Query 2 from scratch, Presto can check if a cached query plan exists for a similar query. If a matching cached plan is found, Presto can reuse the plan directly, bypassing the optimization phase. This can save processing time and resources, resulting in faster query execution.

1. Configuration Tuning: Presto provides various configuration options that can be tuned to optimize query performance. Parameters such as memory limits, parallelism, concurrency, and resource allocation can be adjusted based on the cluster size, workload characteristics, and available resources.

1. Example:
   

   SET SESSION task_writer_count = 4;
   SET SESSION query_max_memory = '2GB';
   SELECT col1, col2
   FROM table
   WHERE col3 = 'value'

Conclusion:

Presto's query optimization is an ongoing process, and the engine continually evolves to incorporate new optimization techniques and improvements. By employing these optimization strategies, users can achieve faster query execution and better utilization of cluster resources in Presto.

Relevant Questions: The same question can be asked in multiple ways.

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