Processing Large Datasets Efficiently with Spring Boot and JDK

Handling large datasets efficiently requires careful consideration of three key factors:

• Input – Where the data originates (CSV, Oracle, etc.).
• Process – How the data is processed (Spring Boot, multi-threading, batch processing).
• Output – Where the processed data is stored (Oracle, distributed storage, etc.).

This post explores strategies for optimizing data processing using Spring Boot, multi-threading, partitioning, and cloud-based solutions.

GitHub Repo : Data Processor

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Scenario 1: CSV as Input, Oracle as Output

Approach

1. Start with a single partition.
2. If performance is an issue, break the dataset into multiple chunks.

Example:

• Total Records: 1,000
• Chunk Size: 100
• Partitions: 10
  • Each partition independently processes and stores data in Oracle.

Optimizations

• Use Streaming APIs (OpenCSV, Apache Commons CSV) instead of loading the entire CSV into memory.
• If CSV is very large (>GBs), process it via AWS S3 + Lambda + SQS to distribute workloads.

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Scenario 2: Oracle as Input, Oracle as Output

Approach

1. Start with a single partition.
2. If performance is still an issue:
   • Optimize the SQL query (indexing, table joins, query restructuring).
   • Refactor processing logic (validate DB/API calls, optimize memory usage).
3. If still slow, break the processing into multiple chunks.

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Partitioning Strategies

Option 1: Partition All Three (Input, Process, Output)

Each partition independently:

• Fetches data
• Processes data
• Stores data

Example:

• Partition 1 -> Fetch, Process, Store
• Partition 2 -> Fetch, Process, Store
• Partition 3 -> Fetch, Process, Store

Option 2: Fetch First, Then Partition Processing & Output

1. Fetch data from Oracle once.
2. Break the dataset into chunks.
3. Process and store in Oracle in smaller batches.

Example:

• Input -> Fetch data (30 records)
• Partition 1 -> Process & Store (10 records)
• Partition 2 -> Process & Store (10 records)
• Partition 3 -> Process & Store (10 records)

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Scaling Strategies

If a single instance or multi-threading isn’t enough, we scale further:

1. Multi-Threading (Within a Single Instance)

• Use Spring Batch + TaskExecutor for parallel processing.
• Use CompletableFuture.supplyAsync() to execute independent tasks concurrently.

2. Multi-Instance Processing (Scaling Horizontally)

• Run multiple application instances where each instance processes a separate partition.
• Use Kubernetes (EKS) or AWS ECS to manage instances.
• Coordinate work using Kafka, SQS, or Redis.

Example:

• Instance 1: Processes records 1-1000
• Instance 2: Processes records 1001-2000
• Instance 3: Processes records 2001-3000

3. Distributed Processing

For extreme scalability, distribute workload across multiple nodes using a message-driven architecture.

• Kafka / SQS / RabbitMQ for queue-based processing
• Use Apache Spark (AWS EMR) for large-scale batch processing
• For real-time data, use Apache Flink

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Key Considerations for Performance Optimization

Input Optimization

• JDBC Cursor & JdbcTemplate – Stream large datasets instead of loading them into memory.
• Use pagination techniques (OFFSET-FETCH, ROWNUM, PARTITION BY) to fetch in batches.

Processing Optimization

• Worker Queues – Offload processing to Kafka, SQS, and RabbitMQ.
• Multi-Threading – Execute multiple partitions concurrently.
• Cache frequently used data (Redis) to reduce redundant DB calls.

Output Optimization

• Batch Inserts (JdbcTemplate.batchUpdate()) instead of inserting one record at a time.
• Oracle Table Partitioning for handling large datasets efficiently.
• Indexing Strategy – Ensure indexes optimize read/write operations.

Cloud-Based Scaling Solutions

1. AWS Step Functions + AWS Lambda

• Use AWS Lambda to process each chunk independently.
• Step Functions orchestrate the entire workflow (fetch, process, store).
• Fully serverless, scales automatically.

2. AWS Glue

• Best for ETL tasks on massive datasets.
• Supports automatic schema inference and integration with S3, Redshift, and Athena.

3. Apache Spark on AWS EMR

• Distributed batch processing for very large datasets (> TBs).
• Supports parallel execution across multiple nodes.

Final Thoughts

Start with simple optimizations, then scale step by step.

• For small to medium datasets, optimize SQL queries, refactor processing logic, and introduce multi-threading.
• For large-scale processing, partition data, introduce worker queues, and scale horizontally.
• For cloud-native solutions, AWS Lambda, Step Functions, Kafka, and Apache Spark provide scalable alternatives.

By choosing the right strategy, you can efficiently process massive datasets while keeping performance and scalability in check!