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 09, 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. 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. Delta Lake supports several statements to facilitate deleting data from and updating data in Delta tables. You can remove data that matches a predicate from a Delta table.
For instance, to delete all events from beforeyou can run the following:. See vacuum for more details. When possible, provide predicates on the partition columns for a partitioned Delta table as such predicates can significantly speed up the operation. You can update data that matches a predicate in a Delta table. For example, to fix a spelling mistake in the eventTypeyou can run the following:.
Similar to delete, update operations can get a significant speedup with predicates on partitions.The Genesis of Delta Lake - An Interview with Burak Yavuz
Suppose you have a Spark DataFrame that contains new data for events with eventId. Some of these events may already be present in the events table. To merge the new data into the events table, you want to update the matching rows that is, eventId already present and insert the new rows that is, eventId not present.
You can run the following:. Here is a detailed description of the merge programmatic operation. A merge operation can fail if multiple rows of the source dataset match and attempt to update the same same row of the target Delta table. By SQL semantics of merge, such an update operation is ambiguous as it is unclear which source should be used to update the matching target row. You can preprocess the source table to eliminate the possibility of multiple matches.
See the change-data-capture example — it preprocesses the change dataset that is, the source dataset to retain only the latest change for each key before applying that change into the target Delta table. You should add as much information to the merge condition to both reduce the amount of work and the chance of transaction conflicts.
For example, suppose you have a table that is partitioned by country and date and you want to use merge to update information for the last day country by country.
Adding the condition events. Here are a few examples on how to use merge in different scenarios. A common ETL use case is to collect logs into Delta table by appending them to a table.Send us feedback. Rename an existing table or view. If the destination table name already exists, an exception is thrown. This operation does not support moving tables across databases.
For managed tables, renaming a table moves the table location; for unmanaged external tables, renaming a table does not move the table location. For further information on managed versus unmanaged external tables, see Managed and unmanaged tables. Set the properties of an existing table or view. If a particular property was already set, this overrides the old value with the new one. Property names are case sensitive.
If you have key1 and then later set Key1a new table property is created. Drop one or more properties of an existing table or view. If a specified property does not exist, an exception is thrown. Set the SerDe or the SerDe properties of a table or partition.
If a specified SerDe property was already set, this overrides the old value with the new one. Setting the SerDe is allowed only for tables created using the Hive format. Delta Lake supports additional constructs for modifying table schema: add, change, and replace columns.
For add, change, and replace column examples, see Explicitly update schema. Add columns to an existing table. It supports adding nested column. If a column with the same name already exists in the table or the same nested struct, an exception is thrown. Change a column definition of an existing table. You can change the comment of the column and reorder columns. Replace the column definitions of an existing table. It supports changing the comments of columns, adding columns, and reordering columns.
If specified column definitions are not compatible with the existing definitions, an exception is thrown. Updated Apr 09, Send us feedback.
Note Property names are case sensitive. Delta Lake Schema Constructs Delta Lake supports additional constructs for modifying table schema: add, change, and replace columns.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:.
Eventually however, you should clean up old snapshots. Updated Apr 09, 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.
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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have to update a table column with inner join with other table. I have tried using the below sql. I tried different ways of updating the table. Can someone help me on this issue how to fix this? If I am getting your question correct you want to use databricks merge into construct to update your table 1 say destination columns by joining it to other table 2 source.
Same query can be extended to insert data as well if no marching row is present in source and destination table. But for the merge operation, you need to have a Delta table first. Learn more. Asked 1 year, 1 month ago. Active 4 months ago. Viewed 1k times. What are you trying to update? ORC table? No it's a normal table. In the above code i want to update a name column of test table. I have tried below query also. It also gave me some error. Is there another way to update apart from using Merge query like Update Table1 join Table2 on Table1.
Active Oldest Votes. I think your command is ok. Fabio Schultz Fabio Schultz 6 6 bronze badges. Note- My answer is based on assumption i made at the start. If you can elobrate further, that will be helpful for understanding your problem better. Pabbati Pabbati 1, 2 2 gold badges 4 4 silver badges 3 3 bronze badges.
Sign up or log in Sign up using Google. Sign up using Facebook.We got the following error when we tried to UPDATE a delta table running concurrent notebooks that all end with an update to the same table.
ConcurrentAppendException: Files were added matching 'true' by a concurrent update. Please try the operation again. I tried to perform serveral UPDATEs manually at the same time with the same cluster and it seems to works good, but it failed with the concurrent notebooks.
In my case, I could fixed partitioning the table and I think is the only way for concurrent update in the same table but you can find different serialization level in the doc for the others operations.
Attachments: Up to 2 attachments including images can be used with a maximum of Streaming from delta table: get only changes 0 Answers. All rights reserved. Create Ask a question Create an article.
Databricks Delta - UPDATE error
Hi, We got the following error when we tried to UPDATE a delta table running concurrent notebooks that all end with an update to the same table. Anyone knows how to fix it? Thanks in advance.
Was there ever a resolution to this issue? I am getting the same problem now.
Hi matt direction. I hope It is hopeful. Your answer. Hint: You can notify a user about this post by typing username. Follow this Question. Related Questions. Databricks Inc. Twitter LinkedIn Facebook Facebook.You can remove files no longer referenced by a Delta table and are older than the retention threshold by running the vacuum command on the table.
The default retention threshold for the files is 7 days. The ability to time travel back to a version older than the retention period is lost after running vacuum.
See Vacuum a Delta table for details. We do not recommend that you set a retention interval shorter than 7 days, because old snapshots and uncommitted files can still be in use by concurrent readers or writers to the table.
If you are certain that there are no operations being performed on this table that take longer than the retention interval you plan to specify, you can turn off this safety check by setting the Apache Spark configuration property spark.
You must choose an interval that is longer than the longest running concurrent transaction and the longest period that any stream can lag behind the most recent update to the table. You can retrieve information on the operations, user, timestamp, and so on for each write to a Delta table by running the history command. The operations are returned in reverse chronological order.
By default table history is retained for 30 days. See Describe History for details. Convert an existing Parquet table to a Delta table in-place. This command lists all the files in the directory, creates a Delta Lake transaction log that tracks these files, and automatically infers the data schema by reading the footers of all Parquet files. If your data is partitioned, you must specify the schema of the partition columns.
Any file not tracked by Delta Lake is invisible and can be deleted when you run vacuum. You should avoid updating or appending data files during the conversion process. After the table is converted, make sure all writes go through Delta Lake. You may also leave feedback directly on GitHub. Skip to main content. Exit focus mode. Vacuum You can remove files no longer referenced by a Delta table and are older than the retention threshold by running the vacuum command on the table. Important The ability to time travel back to a version older than the retention period is lost after running vacuum.
Warning We do not recommend that you set a retention interval shorter than 7 days, because old snapshots and uncommitted files can still be in use by concurrent readers or writers to the table.
Note Operation metrics are available only when the history command as well as the operation in the history were run using Databricks Runtime 6. Note Any file not tracked by Delta Lake is invisible and can be deleted when you run vacuum. Is this page helpful? Yes No. Any additional feedback?