Delta lake spark

26.04.2021 By Toramar

To add your organization here, email us at info delta. Within the project, we make decisions based on these rules. Latest News. Delta Lake Newsletter, Edition March 20, Delta Lake 0. Key Features. ACID Transactions:. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions.

It provides serializability, the strongest level of isolation level. Scalable Metadata Handling:.

Making Apache Spark™ Better with Delta Lake

In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata.

As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Time Travel data versioning :. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments. Open Format:. All data in Delta Lake is stored in Apache Parquet format enabling Delta Lake to leverage the efficient compression and encoding schemes that are native to Parquet.

Unified Batch and Streaming Source and Sink:. A table in Delta Lake is both a batch table, as well as a streaming source and sink. Streaming data ingest, batch historic backfill, and interactive queries all just work out of the box.

Schema Enforcement:. Delta Lake provides the ability to specify your schema and enforce it. This helps ensure that the data types are correct and required columns are present, preventing bad data from causing data corruption. Schema Evolution:. Big data is continuously changing. Delta Lake enables you to make changes to a table schema that can be applied automatically, without the need for cumbersome DDL. Audit History:. Delta Lake transaction log records details about every change made to data providing a full audit trail of the changes.At a minimum you must specify the format delta :.

You can partition data to speed up queries or DML that have predicates involving the partition columns. To partition data when you create a Delta table, specify partition by columns. A common pattern is to partition by date, for example:. Delta Lake time travel allows you to query an older snapshot of a Delta table.

Time travel has many use cases, including:. This section describes the supported methods for querying older versions of tables, data retention concerns, and provides examples. DataFrameReader options allow you to create a DataFrame from a Delta table that is fixed to a specific version of the table. For example, "" and "'T' A common pattern is to use the latest state of the Delta table throughout the execution of a Databricks job to update downstream applications.

Using append mode you can atomically add new data to an existing Delta table:. To atomically replace all of the data in a table, you can use overwrite mode:. You can selectively overwrite only the data that matches predicates over partition columns.

The following command atomically replaces the month of January with the data in df :.

delta lake spark

This sample code writes out the data in dfvalidates that it all falls within the specified partitions, and performs an atomic replacement.

This means that by default overwrites do not replace the schema of an existing table. For Delta Lake support for updating tables, see Update a table. Delta Lake automatically validates that the schema of the DataFrame being written is compatible with the schema of the table.

Delta Lake uses the following rules to determine whether a write from a DataFrame to a table is compatible:.

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If you specify other options, such as partitionByin combination with append mode, Delta Lake validates that they match and throws an error for any mismatch. When partitionBy is not present, appends automatically follow the partitioning of the existing data.

Delta Lake can automatically update the schema of a table as part of a DML transaction either appending or overwritingand make the schema compatible with the data being written. Columns that are present in the DataFrame but missing from the table are automatically added as part of a write transaction when:. The added columns are appended to the end of the struct they are present in.

delta lake spark

Case is preserved when appending a new column. When a different data type is received for that column, Delta Lake merges the schema to the new data type. If Delta Lake receives a NullType for an existing column, the old schema is retained and the new column is dropped during the write. NullType in streaming is not supported. Since you must set schemas when using streaming this should be very rare.

By default, overwriting the data in a table does not overwrite the schema. When overwriting a table using mode "overwrite" without replaceWhereyou may still want to overwrite the schema of the data being written.

You replace the schema and partitioning of the table by setting the overwriteSchema option to true :. Delta Lake supports the creation of views on top of Delta tables just like you might with a data source table. The core challenge when you operate with views is resolving the schemas.Send us feedback.

Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns and provides optimized layouts and indexes for fast interactive queries.

For information on Delta Lake on Databricks, see Optimizations. The Quickstart shows how to build pipeline that reads JSON data into a Delta table, modify the table, read the table, display table history, and optimize the table. For runnable notebooks that demonstrate these features, see Introductory notebooks.

To try out Delta Lake, see Sign up for a free Databricks trial. Updated Apr 16, Send us feedback. Introduction to Delta Lake Delta Lake is an open source storage layer that brings reliability to data lakes.

Streaming and batch unification: A table in Delta Lake is a batch table as well as a streaming source and sink. Streaming data ingest, batch historic backfill, interactive queries all just work out of the box.

Schema enforcement: Automatically handles schema variations to prevent insertion of bad records during ingestion.

delta lake spark

Time travel: Data versioning enables rollbacks, full historical audit trails, and reproducible machine learning experiments. Upserts and deletes: Supports merge, update and delete operations to enable complex use cases like change-data-capture, slowly-changing-dimension SCD operations, streaming upserts, and so on. Delta Lake quickstart. For further resources, including blog posts, talks, and examples, see Additional resources.Send us feedback.

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquetcsvjsonand so on, to delta. For all file types, you read the files into a DataFrame and write out in delta format:. These operations create a new managed table using the schema that was inferred from the JSON data. For the full set of options available when you create a new Delta table, see Create a table and Write to a table. If your source files are in Parquet format, you can use the SQL Convert to Delta statement to convert files in place to create an unmanaged table:.

To speed up queries that have predicates involving the partition columns, you can partition data. You can write data into a Delta table using Structured Streaming. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. For more information about Delta Lake integration with Structured Streaming, see Table streaming reads and writes.

For example, the following statement takes a stream of updates and merges it into the events table. When there is already an event present with the same eventIdDelta Lake updates the data column using the given expression.

When there is no matching event, Delta Lake adds a new row. You must specify a value for every column in your table when you perform an INSERT for example, when there is no matching row in the existing dataset. However, you do not need to update all values. For example, "" and "'T' For example, to query version 0 from the history above, use:.

Because version 1 is at timestamp ' 'to query version 0 you can use any timestamp in the range ' ' to ' ' inclusive. DataFrameReader options allow you to create a DataFrame from a Delta table that is fixed to a specific version of the table. For details, see Query an older snapshot of a table time travel. Once you have performed multiple changes to a table, you might have a lot of small files.

To improve read performance further, you can co-locate related information in the same set of files by Z-Ordering. This co-locality is automatically used by Delta Lake data-skipping algorithms to dramatically reduce the amount of data that needs to be read.

For example, to co-locate by eventTyperun:.

delta lake spark

Eventually however, you should clean up old snapshots. Updated Apr 16, Send us feedback. Delta Lake quickstart Create a table To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquetcsvjsonand so on, to delta. Partition data To speed up queries that have predicates involving the partition columns, you can partition data.

Modify a table Delta Lake supports a rich set of operations to modify tables. Stream writes to a table You can write data into a Delta table using Structured Streaming.

Query an earlier version of the table time travel Delta Lake time travel allows you to query an older snapshot of a Delta table. Note Because version 1 is at timestamp ' 'to query version 0 you can use any timestamp in the range ' ' to ' ' inclusive. Optimize a table Once you have performed multiple changes to a table, you might have a lot of small files. Z-order by columns To improve read performance further, you can co-locate related information in the same set of files by Z-Ordering.Delta Lake is an open source storage layer that brings reliability to data lakes.

Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake on Azure Databricks allows you to configure Delta Lake based on your workload patterns and provides optimized layouts and indexes for fast interactive queries. You may also leave feedback directly on GitHub. Skip to main content. Exit focus mode. This section covers Delta Lake on Azure Databricks.

How is Delta Lake related to Apache Spark? What format does Delta Lake use to store data? How can I read and write data with Delta Lake? Where does Delta Lake store the data? Can I stream data directly into and from Delta tables? How do Delta tables compare to Hive SerDe tables?

Does Delta Lake support multi-table transactions? How can I change the type of a column? What does it mean that Delta Lake supports multi-cluster writes? Can I modify a Delta table from different workspaces? Can I access Delta tables outside of Databricks Runtime? Yes No. Any additional feedback?

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View on GitHub. Is this page helpful?This guide helps you quickly explore the main features of Delta Lake. It provides code snippets that show how to read from and write to Delta tables from interactive, batch, and streaming queries. Delta Lake requires Apache Spark version 2. Follow the instructions below to set up Delta Lake with Spark.

You can run the steps in this guide on your local machine in the following two ways:.

To use Delta Lake interactively within the Spark shell you need a local installation of Apache Spark. Depending on whether you want to use Python or Scala, you can set up either PySpark or the Spark shell, respectively. Download the latest version of Apache Spark 2. Run spark-shell with the Delta Lake package:. If you are seeing the following error, make sure that Apache Spark and delta-core is built for the same Scala version 2.

The pre-built distributions of Apache Spark 2. See this issue for details. If you want to build a project using Delta Lake binaries from Maven Central Repository, you can use the following Maven coordinates. Delta Lake is cross compiled with Scala versions 2. If you are writing a Java project, you can use either version.

To create a Delta table, write a DataFrame out in the delta format. You can use existing Spark SQL code and change the format from parquetcsvjsonand so on, to delta. These operations create a new Delta table using the schema that was inferred from your DataFrame. For the full set of options available when you create a new Delta table, see Create a table and Write to a table. This quickstart uses local paths for Delta table locations.

This example runs a batch job to overwrite the data in the table:. If you read this table again, you should see only the values you have added because you overwrote the previous data.

Delta Lake provides programmatic APIs to conditional update, delete, and merge upsert data into tables. Here are a few examples.

For more information on these operations, see Table Deletes, Updates, and Merges.

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You can query previous snapshots of your Delta table by using a feature called time travel. If you want to access the data that you overwrote, you can query a snapshot of the table before you overwrote the first set of data using the versionAsOf option.

Reliable Data Lakes at Scale

You should see the first set of data, from before you overwrote it. Time travel is an extremely powerful feature that takes advantage of the power of the Delta Lake transaction log to access data that is no longer in the table. Removing the version 0 option or specifying version 1 would let you see the newer data again. For more information, see Query an older snapshot of a table time travel.

You can also write to a Delta table using Structured Streaming. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table:.Delta Lake overcomes many of the limitations typically associated with streaming systems and files, including:. When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started.

You can also control the maximum size of any micro-batch that Delta Lake gives to streaming by setting the maxFilesPerTrigger option. This specifies the maximum number of new files to be considered in every trigger. The default is Structured Streaming does not handle input that is not an append and throws an exception if any modifications occur on the table being used as a source. There are two main strategies for dealing with changes that cannot be propagated downstream automatically:.

When you use ignoreChangesthe new record is propagated downstream with all other unchanged records that were in the same file. Your logic should be able to handle these incoming duplicate records. You can also write data into a Delta table using Structured Streaming. The transaction log enables Delta Lake to guarantee exactly-once processing, even when there are other streams or batch queries running concurrently against the table.

You can also use Structured Streaming to replace the entire table with every batch. One example use case is to compute a summary using aggregation:. The preceding example continuously updates a table that contains the aggregate number of events by customer. For applications with more lenient latency requirements, you can save computing resources with one-time triggers. Use these to update summary aggregation tables on a given schedule, processing only new data that has arrived since the last update.

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Ignoring updates and deletes Structured Streaming does not handle input that is not an append and throws an exception if any modifications occur on the table being used as a source.

There are two main strategies for dealing with changes that cannot be propagated downstream automatically: Since Delta tables retain all history by default, in many cases you can delete the output and checkpoint and restart the stream from the beginning.

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You can set either of these two options: ignoreDeletes ignore transactions that delete data at partition boundaries. For example, if your source table is partitioned by date, and you delete data older than 30 days, the deletion will not be propagated downstream, but the stream can continue to operate.

Unchanged rows may still be emitted, therefore your downstream consumers should be able to handle duplicates. Deletes are not propagated downstream. Therefore if you use ignoreChangesyour stream will not be disrupted by either deletions or updates to the source table.

Delta table as a sink You can also write data into a Delta table using Structured Streaming. Append mode By default, streams run in append mode, which adds new records to the table. Complete mode You can also use Structured Streaming to replace the entire table with every batch. One example use case is to compute a summary using aggregation: spark.