Dell ECS Storage

Overview

Dell ECS (Enterprise Cloud Storage) is an on-premises object storage platform that provides S3-compatible APIs. Iceberg supports Dell ECS through the dedicated ECS catalog, available since version 0.15.0.

Prerequisites

Dell ECS installation with S3 API enabled
ECS access credentials (username and secret key)
Network access to ECS endpoint

Configuration Parameters

Parameter	Required	Description
`ecs.s3.endpoint`	Yes	ECS S3 service endpoint (e.g., `http://10.1.2.3:9020`)
`ecs.s3.access-key-id`	Yes	ECS username
`ecs.s3.secret-access-key`	Yes	ECS S3 secret key
`warehouse`	Yes	Location for data and metadata

Warehouse Location Format

The warehouse property supports these formats:

Format	Description	Example
`ecs://bucket-name`	Use entire bucket	`ecs://analytics`
`ecs://bucket-name/`	Use entire bucket (trailing slash ignored)	`ecs://analytics/`
`ecs://bucket-name/prefix`	Use specific namespace prefix	`ecs://analytics/warehouse`

Spark Integration

Starting Spark SQL Shell

ICEBERG_VERSION=1.4.2
SPARK_VERSION=3.5_2.12
ECS_CLIENT_VERSION=3.3.2

DEPENDENCIES="org.apache.iceberg:iceberg-spark-runtime-${SPARK_VERSION}:${ICEBERG_VERSION},\
org.apache.iceberg:iceberg-dell:${ICEBERG_VERSION},\
com.emc.ecs:object-client-bundle:${ECS_CLIENT_VERSION}"

spark-sql --packages ${DEPENDENCIES} \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.ecs_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.ecs_catalog.warehouse=ecs://my-bucket/warehouse \
    --conf spark.sql.catalog.ecs_catalog.catalog-impl=org.apache.iceberg.dell.ecs.EcsCatalog \
    --conf spark.sql.catalog.ecs_catalog.ecs.s3.endpoint=http://10.1.2.3:9020 \
    --conf spark.sql.catalog.ecs_catalog.ecs.s3.access-key-id=<ecs-username> \
    --conf spark.sql.catalog.ecs_catalog.ecs.s3.secret-access-key=<ecs-secret-key>

Using the Catalog

-- Show available namespaces
SHOW NAMESPACES IN ecs_catalog;

-- Show tables in a namespace
SHOW TABLES IN ecs_catalog.my_database;

-- Create a namespace
CREATE NAMESPACE ecs_catalog.analytics;

-- Create a table
CREATE TABLE ecs_catalog.analytics.events (
  event_id bigint,
  event_type string,
  user_id string,
  timestamp timestamp,
  properties map<string, string>
)
USING iceberg
PARTITIONED BY (days(timestamp));

-- Insert data
INSERT INTO ecs_catalog.analytics.events VALUES
  (1, 'page_view', 'user123', current_timestamp(), map('page', '/home')),
  (2, 'click', 'user456', current_timestamp(), map('button', 'signup'));

-- Query data
SELECT * FROM ecs_catalog.analytics.events
WHERE event_type = 'click';

PySpark Example

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Iceberg with Dell ECS") \
    .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
    .config("spark.sql.catalog.ecs_catalog", "org.apache.iceberg.spark.SparkCatalog") \
    .config("spark.sql.catalog.ecs_catalog.catalog-impl", "org.apache.iceberg.dell.ecs.EcsCatalog") \
    .config("spark.sql.catalog.ecs_catalog.warehouse", "ecs://my-bucket/warehouse") \
    .config("spark.sql.catalog.ecs_catalog.ecs.s3.endpoint", "http://10.1.2.3:9020") \
    .config("spark.sql.catalog.ecs_catalog.ecs.s3.access-key-id", "ecs-user") \
    .config("spark.sql.catalog.ecs_catalog.ecs.s3.secret-access-key", "ecs-secret") \
    .getOrCreate()

# Create a DataFrame
data = [
    (1, "product_view", 1000),
    (2, "add_to_cart", 1001),
    (3, "purchase", 1002)
]
df = spark.createDataFrame(data, ["event_id", "event_type", "user_id"])

# Write to Iceberg table
df.writeTo("ecs_catalog.analytics.user_events").create()

# Read from Iceberg table
result = spark.table("ecs_catalog.analytics.user_events")
result.show()

Flink Integration

Setting Up Flink Environment

# Set Hadoop classpath
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`

# Download dependencies
MAVEN_URL=https://repo1.maven.org/maven2
ICEBERG_VERSION=1.4.2
FLINK_VERSION=1.17
ECS_CLIENT_VERSION=3.3.2

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

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

Creating ECS Catalog in Flink

CREATE CATALOG ecs_catalog WITH (
    'type' = 'iceberg',
    'warehouse' = 'ecs://my-bucket/warehouse',
    'catalog-impl' = 'org.apache.iceberg.dell.ecs.EcsCatalog',
    'ecs.s3.endpoint' = 'http://10.1.2.3:9020',
    'ecs.s3.access-key-id' = '<ecs-username>',
    'ecs.s3.secret-access-key' = '<ecs-secret-key>'
);

USE CATALOG ecs_catalog;
SHOW DATABASES;
SHOW TABLES;

Flink Streaming Example

-- Create a table
CREATE TABLE ecs_catalog.default_db.sensor_data (
  sensor_id STRING,
  temperature DOUBLE,
  humidity DOUBLE,
  timestamp_col TIMESTAMP(3),
  PRIMARY KEY (sensor_id, timestamp_col) NOT ENFORCED
) WITH (
  'format-version' = '2',
  'write.upsert.enabled' = 'true'
);

-- Stream data into the table
INSERT INTO ecs_catalog.default_db.sensor_data
SELECT 
  sensor_id,
  temperature,
  humidity,
  event_time
FROM sensor_stream;

-- Query the table
SELECT * FROM ecs_catalog.default_db.sensor_data
WHERE temperature > 25.0;

Java API

import org.apache.iceberg.catalog.Catalog;
import org.apache.iceberg.catalog.TableIdentifier;
import org.apache.iceberg.Table;
import org.apache.iceberg.Schema;
import org.apache.iceberg.types.Types;
import org.apache.iceberg.PartitionSpec;
import org.apache.iceberg.CatalogUtil;
import java.util.HashMap;
import java.util.Map;

// Configure ECS catalog
Map<String, String> properties = new HashMap<>();
properties.put("warehouse", "ecs://my-bucket/warehouse");
properties.put("ecs.s3.endpoint", "http://10.1.2.3:9020");
properties.put("ecs.s3.access-key-id", "ecs-user");
properties.put("ecs.s3.secret-access-key", "ecs-secret");

// Create catalog instance
Catalog catalog = CatalogUtil.loadCatalog(
    "org.apache.iceberg.dell.ecs.EcsCatalog",
    "ecs_catalog",
    properties,
    new org.apache.hadoop.conf.Configuration()
);

// Define schema
Schema schema = new Schema(
    Types.NestedField.required(1, "id", Types.LongType.get()),
    Types.NestedField.required(2, "data", Types.StringType.get()),
    Types.NestedField.required(3, "timestamp", Types.TimestampType.withoutZone())
);

// Create table
TableIdentifier tableId = TableIdentifier.of("my_database", "my_table");
Table table = catalog.createTable(
    tableId,
    schema,
    PartitionSpec.builderFor(schema).day("timestamp").build()
);

System.out.println("Table created: " + table.location());

Important Limitations

Be aware of these limitations when using ECS catalog:

1. RENAME Operations

No transaction protection: RENAME statements are supported but without additional safeguards
Require manual coordination: Ensure all commits are finished before renaming a table
No data movement: RENAME only updates metadata; data files remain in original location
Potential path mismatch: Renamed tables may have data outside configured warehouse path

-- Ensure no active writes before renaming
ALTER TABLE ecs_catalog.db.old_name RENAME TO ecs_catalog.db.new_name;

2. CAS (Compare-and-Swap) Operations

Checksum-based: ECS uses object checksums for atomic commits
Small collision probability: Very low but non-zero chance of checksum conflicts
Retry logic recommended: Implement retries for critical operations

3. Concurrent Access

Limited coordination: Less robust than cloud-native catalogs (Glue, DynamoDB)
Best for single-writer: Optimal when one writer per table
Careful with multi-writer: Test thoroughly if multiple writers are required

Best Practices

Use Namespace Prefixes

Organize data with namespace prefixes in warehouse path:

ecs://analytics/prod/
ecs://analytics/dev/
ecs://analytics/staging/

Monitor ECS Performance

Track object operation latencies
Monitor storage capacity
Set up alerts for slow operations
Use ECS monitoring tools

Implement Retry Logic

int maxRetries = 3;
for (int i = 0; i < maxRetries; i++) {
    try {
        table.newAppend()
            .appendFile(dataFile)
            .commit();
        break;
    } catch (CommitFailedException e) {
        if (i == maxRetries - 1) throw e;
        Thread.sleep(1000 * (i + 1));
    }
}

Avoid Concurrent Renames

Coordinate RENAME operations:

Use external locking mechanism
Schedule during maintenance windows
Document rename procedures

Security Configuration

SSL/TLS

spark-sql \
  --conf spark.sql.catalog.ecs_catalog.ecs.s3.endpoint=https://ecs.example.com:9021 \
  --conf spark.sql.catalog.ecs_catalog.ecs.s3.protocol=https

Custom Trust Store

spark-sql \
  --conf spark.hadoop.fs.s3a.ssl.channel.mode=openssl \
  --conf spark.hadoop.fs.s3a.connection.ssl.enabled=true \
  --driver-java-options "-Djavax.net.ssl.trustStore=/path/to/truststore.jks -Djavax.net.ssl.trustStorePassword=changeit"

Troubleshooting

Connection Issues

Unable to connect to ECS endpoint

Check:

ECS endpoint is reachable: curl http://10.1.2.3:9020
Firewall rules allow access
Correct port number (default: 9020 HTTP, 9021 HTTPS)
ECS service is running

Authentication Errors

Access Denied or Invalid credentials

Verify:

Username (access-key-id) is correct
Secret key is correct and not expired
User has S3 permissions in ECS
Bucket exists and user has access

Performance Issues

Slow read/write operations

Investigate:

ECS cluster health and load
Network latency between Spark/Flink and ECS
ECS configuration (retention, replication)
Partition strategy (too many small files)

Metadata Conflicts

CommitFailedException: Commit failed, please retry

Solutions:

Implement retry logic with exponential backoff
Reduce concurrent writers
Check for checksum collisions in ECS logs

Version Compatibility

Iceberg Version	ECS Client Version	Spark Version	Flink Version
1.4.x	3.3.2	3.5.x	1.17.x
1.3.x	3.3.1	3.4.x	1.16.x
1.0.x - 1.2.x	3.3.0	3.2.x - 3.3.x	1.14.x - 1.15.x

Migration Path

Migrating from other storage to ECS:

-- Export from S3-based catalog
CREATE TABLE ecs_catalog.db.migrated_table
USING iceberg
AS SELECT * FROM s3_catalog.db.source_table;

-- Or copy with metadata preservation
CALL ecs_catalog.system.snapshot_table(
  source_table => 's3://source-bucket/warehouse/db/table',
  table => 'db.migrated_table'
);

Getting Started

Core Concepts

Table Operations

Query Engines

Catalogs & Storage

Advanced Features

Migration

Integrations

Overview

Prerequisites

Configuration Parameters

Warehouse Location Format

Spark Integration

Starting Spark SQL Shell

Using the Catalog

PySpark Example

Flink Integration

Setting Up Flink Environment

Creating ECS Catalog in Flink

Flink Streaming Example

Java API

Important Limitations

1. RENAME Operations

2. CAS (Compare-and-Swap) Operations

3. Concurrent Access

Best Practices

Security Configuration

SSL/TLS

Custom Trust Store

Troubleshooting

Connection Issues

Authentication Errors

Performance Issues

Metadata Conflicts

Version Compatibility

Migration Path

Next Steps

AWS S3 Storage

Custom FileIO

​Overview

​Prerequisites

​Configuration Parameters

​Warehouse Location Format

​Spark Integration

​Starting Spark SQL Shell

​Using the Catalog

​PySpark Example

​Flink Integration

​Setting Up Flink Environment

​Creating ECS Catalog in Flink

​Flink Streaming Example

​Java API

​Important Limitations

​1. RENAME Operations

​2. CAS (Compare-and-Swap) Operations

​3. Concurrent Access

​Best Practices

​Security Configuration

​SSL/TLS

​Custom Trust Store

​Troubleshooting

​Connection Issues

​Authentication Errors

​Performance Issues

​Metadata Conflicts

​Version Compatibility

​Migration Path

​Next Steps

AWS S3 Storage

Custom FileIO

Overview

Prerequisites

Configuration Parameters

Warehouse Location Format

Spark Integration

Starting Spark SQL Shell

Using the Catalog

PySpark Example

Flink Integration

Setting Up Flink Environment

Creating ECS Catalog in Flink

Flink Streaming Example

Java API

Important Limitations

1. RENAME Operations

2. CAS (Compare-and-Swap) Operations

3. Concurrent Access

Best Practices

Security Configuration

SSL/TLS

Custom Trust Store

Troubleshooting

Connection Issues

Authentication Errors

Performance Issues

Metadata Conflicts

Version Compatibility

Migration Path

Next Steps