Overcoming Tez ORC Snappy Compression Issues

Tez, a processing engine for Hadoop, has gained popularity due to its ability to handle complex queries efficiently. One of the key features that make Tez stand out is its support for columnar storage formats like ORC (Optimized Row Columnar). However, users have reported issues with snappy compression in ORC files, which can lead to reduced query performance.

One common issue that users face is reduced total read bandwidth due to snappy compression. This might be a concern for large-scale datasets where data retrieval is critical. Fortunately, traffic from one disk doesn't affect the other, allowing users to set up separate disks for MR intermediate directories and HDFS data-node directories.

In addition to disk configuration, query optimization plays a crucial role in improving performance. The Transaction Processing Performance Council (TPC) suggests a set of benchmarks for query processing, which can help developers identify areas for improvement. For instance, query execution time is an essential aspect that can be optimized by tweaking the physical query-execution layer.

Another common issue with snappy compression is its impact on query latency. This can be addressed by optimizing the query plan and minimizing the number of partition instances. Gopal and Jitendra's talk on Hive/Tez provides valuable insights into query optimization strategies.

The importance of indexing and filtering cannot be overstated in improving query performance. If data is frequently accessed or filtered on a certain column, sorting on that column and using index-filters can significantly reduce query latency.

In conclusion, while snappy compression may pose some challenges with Tez ORC files, careful disk configuration, query optimization, and indexing strategies can help alleviate these issues. By acknowledging the limitations of snappy compression and adopting best practices for data storage and querying, developers can unlock the full potential of Tez and achieve improved performance in their big data applications.

Tez orc snappy compression issues