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Databricks released this image in September 2020. It was declared Long Term Support (LTS) in October 2020.

The following release notes provide information about Databricks Runtime 7.3 LTS, powered by Apache Spark 3.0.

For help with migration from Databricks Runtime 6.x, see Databricks Runtime 7.x migration guide. Good kid maad city clean download zip.

New features

Delta Lake features and improvements

This release provides the following Delta Lake features and improvements:

In this section

Delta Lake performance optimizations significantly reduce overhead

This release enables a collection of optimizations that reduce the overhead of Delta Lake operations from seconds to tens of milliseconds. To enable these optimizations you must upgrade all of your clusters that write to and read your Delta table to Databricks Runtime 7.3 LTS or above.

For details and caveats, see Enhanced checkpoints for low-latency queries.

Clone metrics now available

Clone operation metrics are now recorded and can be viewed when you run DESCRIBE HISTORY. For details, see Operation metrics keys.

Delta Lake `MERGE INTO` improvements

Merge now supports any number of MATCHED and NOT MATCHED clauses.
Merge queries that unconditionally delete matched rows no longer throw errors on multiple matches.

Specify the initial position for Delta Lake Structured Streaming source

The Delta Lake Structured Streaming connector now supports startingVersion and startingTimestamp options to specify the starting point of the streaming query without processing the entire table. For details, see Specify initial position.

Adaptive query execution

Adaptive query execution (AQE) is a query re-optimization framework that dynamically adjusts query plans during execution based on runtime statistics collected. AQE is enabled by default in Databricks Runtime 7.3 LTS. You can now try out all AQE features. For details, see Adaptive query execution.

Improvements

Auto Loader

Now supports Azure Data Lake Storage Gen1 in directory listing mode
If the file format is text or binaryFile you no longer need to provide the schema. Because the two file formats have a fixed schema, Auto Loader can automatically use a fixed schema.

New Azure Synapse Analytics connector column length control

You can now use a new parameter maxbinlength to control the column length of BinaryType columns. This parameter is translated as VARBINARY(maxbinlength).

The previous (un-configurable) default was VARBINARY(MAX), which caused issues when creating tables with binary columns and the default indexing scheme. Specifically, the table creation would fail, as the default columnstore index does not support MAX in the string and binary columns.

pandas to Spark DataFrame conversion simplified

To enable the following improvements, set the Spark configuration spark.databricks.execution.pandasZeroConfConversion.enabled:

createDataFrame(pandas_df) has been optimized on Azure Databricks.
A warning is displayed if the conversion cannot be done efficiently or is not possible.

New `maxResultSize` in `toPandas()` call

A new configuration setting (spark.databricks.driver.localMaxResultSize) lets you control maxResultSize for a df.toPandas() call. The limit is temporarily changed to max(spark.driver.maxResultSize, spark.databricks.driver.localMaxResultSize). The default value for the configuration setting is the physical memory size divided by 4.

A warning is displayed if the df.toPandas() result size is greater than spark.driver.maxResultSize and less than spark.databricks.driver.localMaxResultSize.

This configuration setting affects only DataFrames created by a df.toPandas() call.

Note

spark.databricks.driver.localMaxResultSize takes effect only when spark.databricks.execution.pandasZeroConfConversion.enabled is true.

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Debuggability of pandas and PySpark UDFs

When a pandas or PySpark UDF fails in a Azure Databricks notebook, the error message now includes the line number where the code failed and information about the root cause.

Library upgrades

Upgraded Python libraries:
- koalas upgraded from 1.1.0 to 1.2.0.
- pyarrow upgraded from 0.15.1 to 1.0.1.
Upgraded several installed R libraries. See Installed R Libraries.
OpenJDK 8 build is changed from Ubuntu OpenJDK 8 to Zulu OpenJDK 8.

Apache Spark

Databricks Runtime 7.3 LTS includes Apache Spark 3.0.1. This release includes all Spark fixes and improvementsincluded in Databricks Runtime 7.2 (Unsupported), as well as the following additional bug fixes and improvements made to Spark:

[SPARK-32302][SPARK-28169] [SQL] Partially push down disjunctive predicates through Join/Partitions
[SPARK-32337] [SQL] Show initial plan in AQE plan tree string
[SPARK-30662] Put back the API changes for HasBlockSize
[SPARK-32620] [SQL] Reset the numPartitions metric when DPP is enabled
[SPARK-32646] [SQL] ORC predicate pushdown should work with case-insensitive analysis
[SPARK-32676] [ML] Fix double caching in KMeans/BiKMeans
[SPARK-32092] [ML][PYSPARK] Removed foldCol related code
[SPARK-32594] [SQL] Override get() and use Julian days in DaysWritable
[SPARK-32608] [SQL] Script Transform ROW FORMAT DELIMIT value should format value
[SPARK-32092] [ML][PYSPARK] Fix parameters not being copied in CrossValidatorModel.copy(), read() and write()
[SPARK-32672] [SQL] Fix data corruption in boolean bit set compression
[SPARK-32663] [CORE] Avoid individual closing of pooled TransportClients (which must be closed through the pool)
[SPARK-32621] [SQL] path’ option can cause issues while inferring schema in CSV/JSON datasources
[SPARK-28863] [SQL] Do not reuse the physical plan
[SPARK-32608] [SQL] Script Transform ROW FORMAT DELIMIT value should format value
[SPARK-28863] [SQL] Introduce AlreadyPlanned to prevent reanalysis of V1FallbackWriters
[SPARK-32451] [R] Support Apache Arrow 1.0.0
[SPARK-32652] [SQL] ObjectSerializerPruning fails for RowEncoder
[SPARK-32518] [CORE] CoarseGrainedSchedulerBackend.maxNumConcurrentTasks should consider all kinds of resources
[SPARK-32559] [SQL] Fix the trim logic in UTF8String.toInt/toLong did’t handle non-ASCII characters correctly
[SPARK-32625] [SQL] Log error message when falling back to interpreter mode
[SPARK-32543] [R] Remove arrow::as_tibble usage in SparkR
[SPARK-31703] [SQL] Parquet RLE float/double are read incorrectly on big endian platfo