Superset Integration with Apache Spark and Trino

অ্যাপাচি সুপারসেট is a modern, enterprise-ready data exploration and visualization platform. In the context of Ilum, it serves as the primary BI interface, enabling users to create interactive dashboards directly on top of distributed compute engines like অ্যাপাচি স্পার্ক এবং ট্রিনো .

Built on a robust python stack (ফ্লাস্ক , pandasএবং SQLAlchemy ), Superset integrates seamlessly with Ilum's Kubernetes-native architecture. It leverages the পাইহাইভ driver to communicate with Ilum's Thrift servers, effectively treating massive distributed datasets as standard SQL tables.

Architecture and Engine Selection​

Ilum exposes two distinct query engines that Superset can leverage, each optimized for specific workloads. Choosing the right engine is critical for dashboard performance.

Deployment and Configuration​

Superset is deployed as a sub-chart within the Ilum ecosystem. To enable it, you must modify your Helm values.

Basic Enablement​

To deploy the default Superset instance:

--সেট সুপারসেট.সক্ষম=সত্য 

Advanced Helm Configuration​

For production environments, you should configure resource quotas and persistence to ensure stability.

superset: 
  সক্ষম :  সত্য 
  # Persist dashboard metadata (PostgreSQL)
  postgresql: 
    persistence: 
      সক্ষম :  সত্য 
      আকার :  10Gi
  # Resource limits for the Superset web server/worker
  সংস্থান : 
    requests: 
      cpu:  "500m"
      memory:  "1Gi"
    limits: 
      cpu:  "1000m"
      memory:  "2Gi"
  # Disable loading examples to speed up startup
  init : 
    loadExamples:  মিথ্যা 

Once deployed, the Superset UI is accessible via the মডিউল tab in the Ilum interface.

• ডিফল্ট প্রমাণপত্রাদি : অ্যাডমিন / অ্যাডমিন

Connecting to Data Sources​

One of Ilum's key advantages is its Zero-Config Connectivity. When Superset is enabled alongside Ilum's core components, it automatically discovers and configures connections to the available query engines.

You do not need to manually enter SQLAlchemy URIs or manage driver versions. Ilum automatically provisions the following connections if the respective modules are active:

1. স্পার্ক এসকিউএল : Automatically connected via the ilum-sql-thrift-binary service.
2. ট্রিনো : Automatically connected if the Trino module is enabled in Helm.

To view or fine-tune these connections, navigate to Settings tod Database Connections.

Fine-Tuning Spark Connections​

While the connection is established automatically, you may want to optimize the Engine Parameters for specific dashboard workloads. This is done by editing the connection and modifying the উন্নত $\to$ Otherক্ষেত্র।

You can inject native Spark configuration properties directly through the connect_args JSON object.

Example: Targeting a Specific Ilum Cluster To route queries from a specific database connection to a dedicated Spark cluster (e.g., a "High Memory" cluster for heavy dashboards), use the configuration object:

{ 
  "connect_args" :  { 
    "কনফিগারেশন" :  { 
      "spark.ilum.sql.cluster.id" :  "heavy-workload-cluster-1"
    } 
  } 
} 

Example: Tuning Spark Execution You can also tune the Spark session for the specific needs of BI queries, such as increasing the number of shuffle partitions or setting timeouts:

{ 
  "connect_args" :  { 
    "কনফিগারেশন" :  { 
      "spark.sql.shuffle.partitions":  "200", 
      "spark.network.timeout":  "300s", 
      "spark.sql.broadcastTimeout":  "600"
    } 
  } 
} 

Data Modeling and Semantic Layer​

Superset provides a semantic layer that allows you to define metrics and calculated columns before visualization. This ensures consistency across dashboards and simplifies the user experience for non-technical creators.

Virtual Datasets via SQL Lab​

Instead of connecting charts directly to raw physical tables, best practice is to use Virtual Datasets.

1. Navigate to SQL Lab.
2. Write an optimized SQL query to aggregate or filter your data (e.g., pre-calculating daily revenue).
3. টিপুন Explore to visualize immediately, or Save Dataset to make it reusable.

This approach pushes the heavy computational lifting (joins, aggregations) to the distributed engine (Spark/Trino) rather than fetching millions of rows into Superset.

Defining Metrics​

In the dataset editor, you can define Metrics (e.g., SUM(revenue)) and Calculated Columns (e.g., CASE WHEN...). These definitions are translated into SQL expressions at query time, ensuring that the logic remains centralized.

Performance Optimization​

To achieve sub-second dashboard load times on large datasets, a multi-layered optimization strategy is required.

1. Data Layer Optimization (Spark/Iceberg/Delta)​

• বিভাজন : Ensure your underlying tables are partitioned by commonly filtered columns (e.g., তারিখ , region). This allows Spark/Trino to perform partition pruning and skip scanning irrelevant files.
• File Formats: Use columnar formats like Parquet, ডেল্টা লেক বা অ্যাপাচি আইসবার্গ . These formats support statistics skipping (min/max values), which significantly reduces I/O.

2. Caching Strategy​

Superset supports caching query results to avoid re-executing expensive computations.

• Chart Caching: Configure a Redis or Memcached backend in your Helm values (supersetEnv.CACHE_CONFIG). Set a reasonable timeout (e.g., 24 hours) for dashboards that don't need real-time data.
• Superset Meta Database: Ensure the internal SQLite database (default) is replaced with PostgreSQL for production use to handle high concurrency of dashboard access.

সমস্যা সমাধান ​

Query Timeouts & "The query returned no data"​

Symptom: Charts spin for a long time and then fail with a timeout or network error. Cause: The query is taking longer than the configured timeouts in the load balancer (Ingress/Nginx) or Superset itself. সমাধান :

1. Increase Superset Timeouts: In superset_config.py (via Helm), increase SQLLAB_TIMEOUTএবং SUPERSET_WEBSERVER_TIMEOUT.
2. Optimize the Query: Use SQL Lab to analyze the query plan. If it involves a full table scan on a large dataset, add filters or aggregation.

Serialization Errors / Complex Types​

Symptom: TypeError: Object of type '...' is not JSON serializable. Cause: Spark complex types (STRUCT, ARRAY, MAP) may not map cleanly to Superset's visualization logic. সমাধান : In your Virtual Dataset query, explicitly cast complex types to strings (e.g., CAST(my_struct AS STRING)) or extract specific fields (my_struct.field) before visualization.

"Pending" State for Long Periods​

Symptom: Queries remain in "Pending" state indefinitely. Cause: The Spark cluster may lack sufficient resources (CPU/RAM) to launch the executors required for the query. সমাধান : Check the কাজ tab in Ilum. If the job is waiting for resources, either scale up the cluster or increase the resources.requests in the Helm chart.