Apache Storm Interview Questions

Apache Storm is a real-time stream processing system designed for distributed and fault-tolerant processing of large streams of data. It is known for its simplicity, scalability, and robust handling of real-time data streams. 

If you're preparing for a job interview that involves Apache Storm, it's essential to understand its core concepts, features, and best practices. This blog post covers the top 10 frequently asked Apache Storm interview questions and answers to help you prepare effectively.

1. What is Apache Storm, and what are its main features?

Answer: Apache Storm is a distributed, fault-tolerant, and real-time stream processing system. It processes unbounded streams of data in real-time, making it ideal for scenarios requiring low-latency processing and real-time analytics.

Main Features:

  • Real-time Processing: Processes data as it arrives, providing low-latency processing.
  • Scalability: This can be scaled horizontally by adding more nodes to the cluster.
  • Fault Tolerance: Ensures data processing continuity in case of node failures through replication and automatic recovery.
  • Ease of Use: Provides simple APIs for defining complex stream processing workflows.
  • Language Agnostic: Supports multiple programming languages, including Java, Python, and Ruby.

2. Explain the core components of an Apache Storm topology.

Answer: An Apache Storm topology consists of the following core components:

  • Spouts: Sources of data streams. Spouts read data from external sources (e.g., message queues, databases) and emit tuples into the topology.
  • Bolts: Processing units that consume tuples, perform computations and emit new tuples. Bolts can perform operations like filtering, aggregation, joining, and writing to databases.
  • Streams: Unbounded sequences of tuples emitted by spouts and processed by bolts.
  • Topology: A directed acyclic graph (DAG) of spouts and bolts that defines the data flow and processing logic.

3. What is the difference between a reliable and unreliable spout in Apache Storm?

Answer: In Apache Storm, spouts can be either reliable or unreliable based on how they handle tuple acknowledgements.

Reliable Spout:

  • Tracks the processing of each tuple and retries failed tuples.
  • Uses the ack and fail methods to acknowledge successful and failed processing of tuples.
  • Provides guaranteed processing but with potential overhead due to tracking and retrying.

Unreliable Spout:

  • Emits tuples without tracking their processing.
  • Does not retry failed tuples, resulting in faster processing but without guaranteed delivery.

4. How does Apache Storm ensure fault tolerance?

Answer: Apache Storm ensures fault tolerance through:

  • Tuple Acknowledgment: Spouts and bolts acknowledge the successful processing of tuples. If a tuple fails, it is retried.
  • Worker Process Monitoring: Storm's supervisor daemon monitors worker processes and restarts them if they fail.
  • Replication: Data and task states are replicated across multiple nodes to ensure data availability.
  • Nimbus and ZooKeeper: Nimbus (Storm's master node) and ZooKeeper manage cluster state and coordinate task assignment and failover.

5. What are the different types of groupings in Apache Storm, and how do they work?

Answer: Apache Storm provides various types of groupings to define how tuples are routed from spouts to bolts and between bolts. Common groupings include:

  • Shuffle Grouping: Randomly distributes tuples evenly across all target bolt instances.
  • Fields Grouping: Routes tuples with the same field values to the same bolt instance.
  • All Grouping: Sends a copy of each tuple to all target bolt instances.
  • Global Grouping: Routes all tuples to a single bolt instance (usually for aggregation).
  • None Grouping: Similar to shuffle grouping but with unspecified routing.
  • Direct Grouping: Allows bolts to direct tuples to specific target bolt instances.


builder.setBolt("myBolt", new MyBolt())
       .fieldsGrouping("mySpout", new Fields("fieldName"));

6. What is a Trident topology in Apache Storm, and how does it differ from a standard topology?

Answer: Trident is a high-level abstraction for real-time stream processing on top of Apache Storm. It provides a functional programming model for defining complex workflows and stateful computations.

Differences from Standard Topology:

  • Transactional Processing: Supports exactly-once processing semantics and transactions.
  • Declarative API: Provides a high-level API for defining complex operations, including joins, aggregations, and grouping.
  • State Management: Built-in support for managing and querying stateful data.
  • Micro-batching: Processes tuples in small batches for improved performance and reliability.


TridentTopology topology = new TridentTopology();
topology.newStream("mySpout", mySpout)
        .groupBy(new Fields("fieldName"))
        .aggregate(new Count(), new Fields("count"));

7. How do you deploy an Apache Storm topology?

Answer: To deploy an Apache Storm topology, follow these steps:

  1. Package the Topology: Package the topology code and dependencies into a JAR file.
  2. Submit the Topology: Use the storm command-line tool or API to submit the topology to the Storm cluster.
    storm jar my-topology.jar com.example.MyTopology myTopologyName
  3. Monitor the Topology: Use the Storm UI or command-line tool to monitor the topology's status and performance.

8. How does Apache Storm handle stateful processing?

Answer: Apache Storm handles stateful processing using the Trident API, which provides built-in support for managing and querying state. Trident allows you to define stateful operations, maintain state across tuples, and ensure consistent state updates.


TridentTopology topology = new TridentTopology();
StateFactory stateFactory = new MemoryMapState.Factory();
topology.newStream("mySpout", mySpout)
        .groupBy(new Fields("fieldName"))
        .persistentAggregate(stateFactory, new Count(), new Fields("count"));

9. What is the role of Nimbus in an Apache Storm cluster?

Answer: Nimbus is the master node in an Apache Storm cluster. It is responsible for:

  • Topology Management: Submitting, managing, and terminating topologies.
  • Resource Allocation: Assigning tasks to worker nodes and balancing the workload.
  • Monitoring and Failover: Monitoring the health of worker nodes and restarting failed tasks.
  • Coordination: Coordinating with ZooKeeper for cluster state management and task assignment.

10. How do you monitor and debug Apache Storm topologies?

Answer: To monitor and debug Apache Storm topologies, use the following tools and techniques:

  • Storm UI: A web-based user interface that provides detailed information about topologies, including metrics, logs, and task status.
  • Log Files: Examine log files for Nimbus, supervisor, and worker processes to identify errors and performance issues.
  • Metrics: Use built-in metrics and custom metrics to monitor topology performance and resource usage.
  • Profiling: Enable and analyze worker profiling to identify bottlenecks and optimize performance.
  • Command-Line Tools: Use Storm command-line tools (storm list, storm kill, storm rebalance) to manage and monitor topologies.


Apache Storm is a powerful real-time stream processing system that offers robust handling of large streams of data with low latency. Understanding its core concepts, features, and best practices is crucial for any developer working with real-time data processing. This blog post covered some of the most commonly asked Apache Storm interview questions, helping you prepare effectively for your next interview. By mastering these concepts, you will be well-equipped to tackle any Storm-related challenges you may encounter.