When you port existing workloads to Databricks Delta, you should be aware of the following simplifications and differences compared with the data sources provided by Apache Spark and Apache Hive.
Delta handles the following operations automatically, which you should never perform manually:
- Delta tables always return the most up-to-date information, so there is no need to manually call
REFRESH TABLEafter changes.
- Add and remove partitions
- Delta automatically tracks the set of partitions present in a table and updates the list as data is added or removed. As a result, there is no need to run
ALTER TABLE [ADD|DROP] PARTITIONor
- Load a single partition
- As an optimization, you may sometimes directly load the partition of data you are interested in. For example,
spark.read.parquet("/data/date=2017-01-01"). This is unnecessary with Delta, since it can quickly scan the list of files to find the list of relevant ones. If you are interested in a single partition, specify it using a
WHEREclause. For example,
spark.read.parquet("/data").where("date = '2017-01-01'").
When you port an existing application to Delta, you should avoid the following operations, which bypass the transaction log:
- Manually modify data
- Delta uses the transaction log to atomically commit changes to the table. Because the log is the source of truth, files that are written out but not added to the transaction log are not read by Spark. Similarly, even if you manually delete a file, a pointer to the file is still present in the transaction log. Instead of manually modifying files stored in a Delta table, always use the DML commands that are described in this guide.
- External readers
The data stored in Delta is encoded as Parquet files. However, accessing these files using an external reader is not safe. You’ll see duplicates and uncommitted data and the read may fail when someone runs
Because the files are encoded in an open format, you always have the option to move the files outside Delta. At that point, you can run
VACUUM RETAIN 0and delete the transaction log. This leaves the table’s files in a consistent state that can be read by the external reader of your choice.
Suppose you have Parquet data stored in the DBFS directory
/data-pipeline. To create a Delta table from this data you have two options: Convert to Delta or read into DataFrame and save as Delta. The former approach is faster but results in an unmanaged table. The latter approach copies data, but lets Spark manage the table.
CONVERT TO DELTA parquet.`/data-pipeline`
For details, see Convert To Delta (Databricks Delta).
Read the data into a DataFrame and save it to a new directory in
data = spark.read.parquet("/data-pipeline") data.write.format("delta").save("/delta/data-pipeline/")
Create a Delta table that refers to the files in the Delta directory:
DROP TABLE IF EXISTS pipeline_delta; CREATE TABLE pipeline_delta USING DELTA LOCATION "/delta/data-pipeline/"