(TPC-H queries on a secure cluster were benchmarked Impala optionally skips an arbitrary number of header lines from text input Overall performance improvements for join queries, by using a prefetching mechanism queried tables with up to approximately 3000 partitions. Join order optimizations. Although the metadata for Kudu tables is stored in the metastore database, currently Hive cannot access Kudu tables. remote reads.Impala queries benefit from the improved alignment of row groups with HDFS blocks for Parquet files written by Hive, MapReduce, and other components in Many new built-in scalar functions, for convenience and enhanced portability of SQL that uses common industry extensions.Impala can now run a specified command to obtain the password to decrypt a private-key PEM file, rather than having the private-key file be unencrypted on disk. It is enabled by setting a non-zero value for the You can specify the minimum required TLS/SSL version using the You can specify the set of allowed TLS ciphers using the More granular Sentry authorization for Kudu tables. Impala recognizes the compression

The CPU requirement for Impala, which had become more restrictive in Impala 2.0.x and 2.1.x, has now been relaxed. An enhancement to the statestore component lets it transmit heartbeat information independently of files on HDFS based on the The memory buffer used during spill-to-disk processing is smaller: instead of 8 MiB, this buffer defaults to 2 MiB and Impala can reduce it to as little as 64 KiB when appropriate.This feature includes new query options for fine-tuning memory areas used during query processing: Improvements to the mechanism for caching HDFS file handles. database systems, provide more flexibility for handling time zones, and future-proof the handling of This The Impala debug web UI now can display a visual representation of the query plan. This feature reduces the time spent in query planning, for partitioned tables with thousands of partitions. This limitation is tracked by the issue Improved flexibility for intermediate data types in user-defined aggregate functions (UDAFs). This technique is especially valuable when performing join queries involving partitioned tables. Several new built-in functions, such as You can specify more settings, such This feature makes use of the underlying YARN service, plus an additional service It also provides more user-friendly conflict resolution

A new optimization speeds up aggregation operations that involve only the partition key See

)Usability improvements for case-insensitive queries. require any special table settings to work in an Impala text table. reads. Integration with Apache Kudu: The experimental Impala support for the Kudu storage layer has been folded into the main Impala development branch. slightly more memory for queries than in previous releases. The The following are some of the most significant new features in this release:Impala can now read and write data stored on the Microsoft Azure Data Lake Store (ADLS).

Kudu tables can now use column-level privileges. The following new built-in scalar and aggregate functions are available: intermediate results for operations such as Spill-to-disk feature now always recommended. HDFS data block, when cached replicas are available on multiple hosts.

hotspots and bottlenecks that could occur if heavily used

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