15 Cassandra Interview Questions and Answers

Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without any single point of failure. Known for its robust architecture and high availability, Cassandra is widely used in industries that require real-time data processing and large-scale data storage solutions. Its ability to manage vast datasets with minimal latency makes it a preferred choice for applications that demand high performance and reliability.

This article offers a curated selection of interview questions tailored to help you demonstrate your proficiency with Cassandra. By reviewing these questions and their detailed answers, you will be better prepared to showcase your understanding of Cassandra’s architecture, data modeling, and operational best practices, thereby enhancing your readiness for technical interviews.

Cassandra Interview Questions and Answers

1. Describe the basic principles of data modeling.

Data modeling in Cassandra differs from traditional relational databases. Key principles include:

• Denormalization: Cassandra encourages duplicating data to optimize read performance, minimizing the need for joins.
• Partitioning: Data is distributed across nodes using a partition key, which ensures even data distribution and efficient query performance.
• Query-Driven Design: The schema is designed to optimize read performance for specific queries, ensuring efficient data retrieval.
• Clustering Columns: Within a partition, data is sorted using clustering columns, allowing for efficient range queries and data ordering.
• Avoiding Joins and Aggregations: Cassandra does not support joins and complex aggregations natively, so the data model should avoid these operations.

2. Write a CQL query to create a table with a composite primary key.

In Cassandra, a composite primary key uniquely identifies rows by combining multiple columns, useful for complex data models. It consists of a partition key and clustering columns. The partition key determines data distribution, while clustering columns determine data order within a partition.

Example CQL query:

CREATE TABLE users (
    user_id UUID,
    email TEXT,
    first_name TEXT,
    last_name TEXT,
    PRIMARY KEY (user_id, email)
);

Here, user_id is the partition key, and email is the clustering column.

3. Explain the significance of the replication factor in a cluster.

The replication factor in Cassandra specifies the number of data replicas across nodes. For example, a replication factor of 3 means three copies of each data piece are stored on different nodes. This ensures data availability, fault tolerance, and helps balance consistency with availability.

4. What are the different consistency levels available, and when would you use each?

Cassandra offers several consistency levels to balance availability and consistency:

• ANY: Write to at least one node, including hints, prioritizing availability.
• ONE: Write to at least one replica node, suitable for low-latency needs.
• QUORUM: Write to a majority of replica nodes, balancing consistency and availability.
• LOCAL_QUORUM: Similar to QUORUM but within the local data center, useful for multi-data center deployments.
• EACH_QUORUM: Write to a quorum of replica nodes in each data center, for strong consistency across data centers.
• ALL: Write to all replica nodes, providing the highest consistency.

5. How does partitioning work, and why is it important?

Partitioning in Cassandra divides data into partitions, each with a unique partition key determining its storage node. The partition key is hashed, and the hash value determines the responsible node. Partitioning ensures scalability, fault tolerance, and balanced performance.

6. What are the different compaction strategies available, and how do they differ?

Cassandra offers several compaction strategies:

• Size-Tiered Compaction Strategy (STCS): Groups SSTables of similar sizes for compaction, suitable for write-heavy workloads.
• Leveled Compaction Strategy (LCS): Organizes SSTables into levels, reducing read amplification, ideal for read-heavy workloads.
• Time-Window Compaction Strategy (TWCS): Groups SSTables based on time windows, useful for time-series data.
• Hybrid Compaction Strategy (HCS): Combines STCS and LCS, balancing read and write operations.

7. Explain lightweight transactions and provide an example scenario where they would be useful.

Lightweight transactions (LWT) in Cassandra perform conditional updates with linearizable consistency using the Paxos protocol. They ensure a transaction is fully completed or not executed at all, useful for scenarios like ensuring unique usernames before account creation.

Example:

-- Create a table for user accounts
CREATE TABLE users (
    username TEXT PRIMARY KEY,
    email TEXT,
    created_at TIMESTAMP
);

-- Insert a new user only if the username does not already exist
INSERT INTO users (username, email, created_at)
VALUES ('john_doe', '[email protected]', toTimestamp(now()))
IF NOT EXISTS;

The IF NOT EXISTS clause ensures the insert operation succeeds only if the username ‘john_doe’ does not exist.

8. What security features are offered to protect data?

Cassandra offers security features to protect data:

• Authentication: Supports pluggable authentication mechanisms, with PasswordAuthenticator as the default.
• Authorization: Provides role-based access control (RBAC) to manage permissions.
• Encryption: Supports encryption at rest and in transit, with Transparent Data Encryption (TDE) and SSL/TLS.
• Auditing: Includes auditing capabilities to track and log user activities.

9. How can Cassandra be integrated with Apache Spark for analytics purposes?

Cassandra integrates with Apache Spark for analytics using the Spark-Cassandra Connector. This allows Spark to read from and write to Cassandra tables, leveraging Spark’s API for data processing.

Example:

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder \
    .appName("CassandraSparkIntegration") \
    .config("spark.cassandra.connection.host", "127.0.0.1") \
    .getOrCreate()

# Read data from Cassandra table
df = spark.read \
    .format("org.apache.spark.sql.cassandra") \
    .options(table="my_table", keyspace="my_keyspace") \
    .load()

# Perform some transformations
df_filtered = df.filter(df["column_name"] > 100)

# Show the results
df_filtered.show()

# Write data back to Cassandra table
df_filtered.write \
    .format("org.apache.spark.sql.cassandra") \
    .options(table="my_table", keyspace="my_keyspace") \
    .mode("append") \
    .save()

10. Explain the gossip protocol and its role in a cluster.

The gossip protocol in Cassandra is a peer-to-peer communication mechanism for disseminating state information about the cluster. Each node periodically exchanges state information with a subset of other nodes, including node status, data ownership, and metadata.

Roles of the gossip protocol:

• Node Discovery: Helps nodes discover other nodes in the cluster.
• Failure Detection: Detects failures and changes in the cluster.
• State Dissemination: Propagates state changes throughout the cluster.
• Load Balancing: Aids in distributing data evenly across the cluster.

11. What are some key performance tuning parameters, and how do they affect performance?

Key performance tuning parameters in Cassandra include:

• Compaction Strategy: Determines how SSTables are merged and compacted, impacting read and write performance.
• Memtable Settings: Manage memory usage and improve write performance.
• Cache Settings: Enhance read performance by reducing disk I/O.
• Concurrency Settings: Control the number of concurrent operations, balancing load and throughput.
• Thread Pool Settings: Optimize resource utilization and performance.
• GC Settings: Impact performance, reducing latency and improving throughput.
• Disk I/O Configuration: Enhance performance with proper disk I/O configuration.

12. How does data get distributed across nodes?

In Cassandra, data distribution across nodes is managed through partitioning and replication. Data is distributed using a consistent hashing mechanism, with each piece assigned a unique partition key. Replication ensures high availability and fault tolerance, with the replication factor determining the number of data copies.

Replication strategies:

SimpleStrategy is used for single data center deployments, placing replicas on the next nodes in the ring. NetworkTopologyStrategy is used for multi-data center deployments, distributing replicas across different racks and data centers.

13. What is read repair, and why is it important?

Read repair in Cassandra ensures data consistency across nodes. When a read request is made, Cassandra reads data from multiple replicas and initiates a read repair process if discrepancies are detected. This updates out-of-date replicas with the most recent data, maintaining data integrity.

14. Describe hinted handoff and its role in ensuring availability.

Hinted handoff in Cassandra improves availability and fault tolerance. When a node is down, another node temporarily stores write operations intended for the unavailable node. Once the node is back online, the stored hints are replayed, ensuring no write operations are lost.

15. What are tombstones, and how do they impact performance?

Tombstones in Cassandra are markers indicating data deletion. They ensure eventual consistency across nodes but can impact performance, particularly during read operations. Accumulated tombstones can increase disk space usage and compaction times.