thbas.blogg.se - Redshift failed to init the cuda driver api

Redshift failed to init the cuda driver api upgrade#
Redshift failed to init the cuda driver api full#

Data Skipping in DML - UPDATE, DELETE, and MERGE now use statistics when locating files that need to be rewritten.

Improved parallelism for OPTIMIZE - OPTIMIZE now runs as a single Spark task and will use all parallelism available on the cluster (previously was limited to 100 compacted files at a time).

Filter pushdown in the streaming source - Streaming queries now use partitioning when starting a new stream to skip irrelevant data.This is applicable to queries in notebooks due to the implicit limit=1000 in effect for all notebook commands. Support for limit push down - Statistics are used to limit the number of files scanned for queries that have LIMIT and predicates over partition columns.To configure the number of columns, set the table property delta.dataSkippingNumIndexedCols=. Databricks Delta write performance has been improved by up to 2x due to the reduction in stats collection overhead. Reduced stats collection overhead - The efficiency of stats collection has been improved and stats are now only collected for a configurable number of columns, set to 32 by default.For example, you can specify LIMIT, ORDER BY and INLINE TABLE in the source. MERGE INTO source - Adds more comprehensive support to the source query specification of MERGE.You can block deletes and modifications to a table by setting the table property delta.appendOnly=true. Append-only tables - Databricks Delta now supports basic data governance.Streaming tables - Streaming DataFrames can be created using ("delta").table("").The Data sidebar also shows detailed table information and history for Databricks Delta tables. Table details - Provenance information is now available for each write to a table.Detailed table information - You can see the current reader and writer versions of a table by running DESCRIBE DETAIL.All DDL and DML commands support both table name and delta.``.save() and saveAsTable() now have identical semantics.

Redshift failed to init the cuda driver api full#

Full support for table DDL and saveAsTable().

ALTER TABLE CHANGE COLUMNS to change column ordering.

ALTER TABLE ADD COLUMN to add new columns to a table.

Databricks Delta now supports the following DDL to modify schema explicitly:.

Databricks Delta continues to support automatic schema migration.

Schema management - Databricks Delta now validates appends and overwrites to an existing table to ensure that the schema being written matches the schema. Tables can no longer have columns that differ only by case.ĭelta-specific table configurations must now be prefixed with delta.

Redshift failed to init the cuda driver api upgrade#

If you see either of these errors, upgrade to Databricks Runtime 4.1: : For input string: "00000000000000.crc" If you are running earlier versions of Databricks Delta, you must upgrade all jobs before you use Databricks Runtime 4.1. To enable the previous behavior, set the mergeSchema option to true. Writes are now validated against the current schema of the table rather than, as before, automatically adding columns that are missing from the destination table.

See Table versioning for more information. Then run: .upgradeTableProtocol("" or "") To upgrade an existing table, first upgrade all jobs that are writing to the table. You must upgrade existing tables in order to take advantage of these improvements. Tables created with Databricks Runtime 4.1 automatically use the new version and cannot be written to by older versions of Databricks Runtime. Breaking changesĭatabricks Runtime 4.1 includes changes to the transaction protocol to enable new features, such as validation. Contact your account manager or sign up at. This release remains in Private Preview, but it represents a candidate release in anticipation of the upcoming general availability (GA) release.ĭelta Lake is now also available in Private Preview to Azure Databricks users. Databricks highly recommends that all Delta Lake customers upgrade to the new runtime. Delta Lakeĭatabricks Runtime version 4.1 adds major quality improvements and functionality to Delta Lake. The following release notes provide information about Databricks Runtime 4.1, powered by Apache Spark. For more information about the Databricks Runtime deprecation policy and schedule, see Databricks runtime support lifecycle. This release was deprecated on January 17, 2019.