Apache iceberg example11/29/2023 ![]() t("_", System.getenv("ALIBABA_CLOUD_ACCESS_KEY_ID")) t("_catalog.catalog-impl", ".dlf.DlfCatalog") t(".warehouse", "")ĮMR V3.38.X, EMR V5.3.X, and EMR V5.4.X t("", ".extensions.IcebergSparkSessionExtensions") ![]() t(".catalog-impl", ".")ĮMR V3.39.X and EMR V5.5.X t("", ".extensions.IcebergSparkSessionExtensions") Partitioning is an optimization technique used to divide a table into certain parts based on some attributes. In the following configurations, DLF is used to manage metadata.ĮMR V3.40 or a later minor version, and EMR V5.6.0 or later t("", ".extensions.IcebergSparkSessionExtensions") Article The why and how of partitioning in Apache Iceberg See how easy Iceberg makes partition evolution By Kiersten Stokes Published OctoApache Iceberg is an open source table format for storing huge data sets. It allows you not only to query the data, but also to modify it easily on the row level. Iceberg can be used to rewrite data files to enhance read performance and use delete deltas to quicken the pace of updates. Tutorial 8 min read Apache Spark with Apache Iceberg - a way to boost your data pipeline performance and safety SQL language was invented in 1970 and has powered databases for decades. This makes it possible to complete tasks such as updating existing rows, merging new data, and targeted deletes. For more information, see Configuration of DLF metadata. Expressive SQL Iceberg fully supports flexible SQL commands. The default name of the catalog and the parameters that you must configure vary based on the version of your cluster. The following commands show how to configure a catalog. ![]() In Flink, the SQL CREATE TABLE test (. That means we can just create an iceberg table by specifying connectoriceberg table option in Flink SQL which is similar to usage in the Flink official document. Stack trace: .rpc.DatabricksExceptions$SQLExecutionException: .AnalysisException: MERGE destination only supports Delta sources.Īt .tahoe.DeltaErrors$.notADeltaSourceException(DeltaErrors.scala:343)Īt .(PreprocessTableMerge.scala:201)Īt .tahoe.PreprocessTableMergeEdge$$anonfun$apply$1.applyOrElse(PreprocessTableMergeEdge.scala:39)Īt .tahoe.PreprocessTableMergeEdge$$anonfun$apply$1.applyOrElse(PreprocessTableMergeEdge.scala:36)Īt .$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:112)Īt .$.withOrigin(TreeNode.scala:82)Īt .$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:112)Īt .$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:216)Īt .resolveOperatorsDown(AnalysisHelper.scala:110)Īt .resolveOperatorsDown$(AnalysisHelper.scala:108)Īt .resolveOperatorsDown(LogicalPlan.scala:29)Īt .resolveOperators(AnalysisHelper.scala:73)Īt .resolveOperators$(AnalysisHelper.scala:72)Īt .resolveOperators(LogicalPlan.scala:29)Īt .(PreprocessTableMergeEdge.scala:36)Īt .(PreprocessTableMergeEdge.scala:29)Īt .$anonfun$execute$2(RuleExecutor.Before you call a Spark API to perform operations on an Iceberg table, add the required configuration items to the related SparkConf object to configure a catalog. Apache Flink supports creating Iceberg table directly without creating the explicit Flink catalog in Flink SQL. Iceberg configs =.extensions.IcebergSparkSessionExtensions Primary Question: How can I get Spark to recognize this as an Iceberg query and not Delta? Or is it possible to remove the delta-related SQL rules altogether? All iceberg capabilities are available for spark version 3.x, for version 2.4 there is only support for DataFrame overwrite and append. I'm able to read, append, and overwrite to iceberg tables without issue. Apache Iceberg provides two methods for spark users to interact with ACID tables, via dataframes or using an sql syntax. MERGE triggers the delta rule and then throws an error because it's not a delta table. From what I can tell, this issue is stemming from the order in which Spark tries to execute the plan. I believe the root of the issue is that MERGE is also a keyword for the Delta Lake SQL engine. Generates this error: Error in SQL statement: AnalysisException: MERGE destination only supports Delta sources. MERGE INTO iceberg.db.table t USING (SELECT * FROM iceberg.db.table) u ON t.id = u.id You can see the database name, the location (S3 path) of the Iceberg table, and the metadata location. In October, BigLake, Google Clouds data lake storage engine, began support for Apache Iceberg, with Databricks format Delta and Hudi streaming set to come soon. ![]() INSERT INTO iceberg.db.table SELECT id, data FROM (select * from iceberg.db.table) t WHERE length(data) = 1 The following is an example Iceberg catalog with AWS Glue implementation. ![]() This code: CREATE TABLE iceberg.db.table (id bigint, data string) USING iceberg I'm trying to get Apache Iceberg set up in our Databricks environment and running into an error when executing a MERGE statement in Spark SQL. ![]()
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