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Iceberg Dell Integration🔗

Dell ECS Integration🔗

Iceberg can be used with Dell's Enterprise Object Storage (ECS) by using the ECS catalog since 0.15.0.

See Dell ECS for more information on Dell ECS.


When using Dell ECS with Iceberg, these configuration parameters are required:

Name Description
ecs.s3.endpoint ECS S3 service endpoint
ecs.s3.access-key-id ECS Username
ecs.s3.secret-access-key S3 Secret Key
warehouse The location of data and metadata

The warehouse should use the following formats:

Example Description
ecs://bucket-a Use the whole bucket as the data
ecs://bucket-a/ Use the whole bucket as the data. The last / is ignored.
ecs://bucket-a/namespace-a Use a prefix to access the data only in this specific namespace

The Iceberg runtime jar supports different versions of Spark and Flink. You should pick the correct version.

Even though the Dell ECS client jar is backward compatible, Dell EMC still recommends using the latest version of the client.


To use the Dell ECS catalog with Spark 3.5.0, you should create a Spark session like:



spark-sql --packages ${DEPENDENCIES} \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.my_catalog.warehouse=ecs://bucket-a/namespace-a \
    --conf \
    --conf spark.sql.catalog.my_catalog.ecs.s3.endpoint=http://10.x.x.x:9020 \
    --conf spark.sql.catalog.my_catalog.ecs.s3.access-key-id=<Your-ecs-s3-access-key> \
    --conf spark.sql.catalog.my_catalog.ecs.s3.secret-access-key=<Your-ecs-s3-secret-access-key>

Then, use my_catalog to access the data in ECS. You can use SHOW NAMESPACES IN my_catalog and SHOW TABLES IN my_catalog to fetch the namespaces and tables of the catalog.

The related problems of catalog usage:

  1. The SparkSession.catalog won't access the 3rd-party catalog of Spark in both Python and Scala, so please use DDL SQL to list all tables and namespaces.

Use the Dell ECS catalog with Flink, you first must create a Flink environment.

# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`

# download Iceberg dependency
wget ${MAVEN_URL}/org/apache/iceberg/iceberg-flink-runtime-${FLINK_VERSION}/${ICEBERG_VERSION}/iceberg-flink-runtime-${FLINK_VERSION}-${ICEBERG_VERSION}.jar
wget ${MAVEN_URL}/org/apache/iceberg/iceberg-dell/${ICEBERG_VERSION}/iceberg-dell-${ICEBERG_VERSION}.jar

# download ECS object client
wget ${MAVEN_URL}/com/emc/ecs/object-client-bundle/${ECS_CLIENT_VERSION}/object-client-bundle-${ECS_CLIENT_VERSION}.jar

# open the SQL client.
/path/to/bin/ embedded \
    -j iceberg-flink-runtime-${FLINK_VERSION}-${ICEBERG_VERSION}.jar \
    -j iceberg-dell-${ICEBERG_VERSION}.jar \
    -j object-client-bundle-${ECS_CLIENT_VERSION}.jar \

Then, use Flink SQL to create a catalog named my_catalog:

    'warehouse' = 'ecs://bucket-a/namespace-a',
    'ecs.s3.endpoint' = 'http://10.x.x.x:9020',
    'ecs.s3.access-key-id' = '<Your-ecs-s3-access-key>',
    'ecs.s3.secret-access-key' = '<Your-ecs-s3-secret-access-key>');

Then, you can run USE CATALOG my_catalog, SHOW DATABASES, and SHOW TABLES to fetch the namespaces and tables of the catalog.


When you use the catalog with Dell ECS only, you should care about these limitations:

  1. RENAME statements are supported without other protections. When you try to rename a table, you need to guarantee all commits are finished in the original table.
  2. RENAME statements only rename the table without moving any data files. This can lead to a table's data being stored in a path outside of the configured warehouse path.
  3. The CAS operations used by table commits are based on the checksum of the object. There is a very small probability of a checksum conflict.