What is spark vectorization?

Vectorized query execution is a feature that greatly reduces the CPU usage for typical query operations such as scans, filters, aggregates, and joins. Vectorization is also implemented for the ORC format. Spark also uses Whole Stage Codegen and this vectorization (for Parquet) since Spark 2.0.

What is CBO in Hive?

Hive’s Cost-Based Optimizer (CBO) is a core component in Hive’s query processing engine. The chosen logical plan is then converted by Hive to a physical operator tree, optimized and converted to Tez jobs, and then executed on the Hadoop cluster.

Does spark use vectorization?

So Spark already used vectorization to multiple purposes, which already improves the performance of the Apache Spark program. Vectorized Parquet Reader, Vectorized ORC Reader, Pandas UDF employ Spark.

How do I disable vectorization in Hive?

If a compile time or run time error occurs that appears related to vectorization, please file a Hive JIRA. To work around such an error, disable vectorization by setting hive. vectorized. execution.

What is optimization in hive?

Vectorization In Hive – Hive Optimization Techniques, to improve the performance of operations we use Vectorized query execution. Here operations refer to scans, aggregations, filters, and joins. It happens by performing them in batches of 1024 rows at once instead of single row each time.

What is difference between partition and bucket in hive?

At a high level, Hive Partition is a way to split the large table into smaller tables based on the values of a column(one partition for each distinct values) whereas Bucket is a technique to divide the data in a manageable form (you can specify how many buckets you want).

What is static and dynamic partition in hive?

Usually when loading files (big files) into Hive tables static partitions are preferred. That saves your time in loading data compared to dynamic partition. You “statically” add a partition in table and move the file into the partition of the table. Since the files are big they are usually generated in HDFS.

When partition is archive in hive?

Internally, when a partition is archived, a HAR is created using the files from the partition’s original location (such as /warehouse/table/ds=1 ). The parent directory of the partition is specified to be the same as the original location and the resulting archive is named ‘data.

What is Vectorized Query Execution in hive?

By default, the Hive query execution engine processes one row of a table at a time. The single row of data goes through all the operators in the query before the next row is processed, resulting in very inefficient CPU usage. In vectorized query execution, data rows are batched together and represented as a set of column vectors.

How do I fix the vectorization error in hive?

To work around such an error, disable vectorization by setting hive.vectorized.execution.enabled to false for the specific query that is failing, to run it in standard mode. Vectorized support continues to be added for additional functions and expressions.

What is vectorvectorized query execution?

Vectorized query execution reads batch of rows as column vectors and each operator processes the whole vectors at a time. This vector mode of execution has been proven to be an order of magnitude faster for cpu performance. In hive we also gain manifolds improvements by removing the layers of branching and virtual method calls in the inner loop.

What is query vectorization in CDH?

When query vectorization is enabled, the query engine processes vectors of columns, which greatly improves CPU utilization for typical query operations like scans, filters, aggregates, and joins. Hive query vectorization is enabled by default in CDH 6 and CDH 5.