5 most popular Cassandra DB interview questions (explained)

Question 1. What are the key features of Cassandra DB that differentiate it from other NoSQL databases?

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Cassandra DB, a highly scalable and distributed NoSQL database, stands out for its exceptional capabilities in handling large volumes of data across multiple nodes without a single point of failure. Here are its key features, along with code examples and real-world use cases:

Distributed Architecture: Cassandra offers a peer-to-peer distributed system across its nodes, ensuring no single point of failure. This architecture makes it exceptionally scalable and resilient.

Code Example:

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Cluster cluster = Cluster.builder()
                         .addContactPoint("127.0.0.1")
                         .build();
Session session = cluster.connect();

Linear Scalability: It provides linear scalability, meaning performance increases linearly with the number of nodes. You can add more nodes without downtime.

Code Example:

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ALTER KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'NetworkTopologyStrategy', 'datacenter1' : 3 };

Flexible Data Storage: Cassandra accommodates a wide variety of data formats. It's schema-less, allowing you to add columns to any row without affecting other rows.

Code Example:

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INSERT INTO users (user_id, email, name) VALUES (111, '[email protected]', 'John');

High Availability: It provides high availability with its masterless architecture. Data is replicated across multiple nodes to prevent data loss.

Code Example:

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CREATE TABLE mytable (
    id UUID PRIMARY KEY,
    data text
) WITH REPLICATION = {'class' : 'SimpleStrategy', 'replication_factor' : 3};

Tunable Consistency: Cassandra allows tuning the consistency level per operation, balancing between consistency and performance.

Code Example:

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ResultSet result = session.execute(
    SimpleStatement.newInstance("SELECT * FROM mytable WHERE id = 123")
                   .setConsistencyLevel(ConsistencyLevel.QUORUM));

Real-World Use Cases

Financial Services: Used for fraud detection systems due to its ability to handle large, unstructured data sets in real-time.
E-Commerce: Manages inventory, user data, and transaction logs, offering high scalability during peak times.
Social Media: Powers real-time messaging and feeds due to its fast write and read capabilities.
Telecommunications: Manages large volumes of CDR (Call Detail Records) for real-time analysis and billing.
Healthcare: Stores patient records and large datasets for medical research, benefiting from its high data availability.

Question 2. How does Cassandra handle data replication and consistency, and what consistency levels does it support?

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Cassandra DB's approach to data replication and consistency is fundamental to its performance and reliability. Here's an in-depth explanation, complete with code examples and real-world use cases.

Data Replication

Cassandra replicates data across multiple nodes to ensure reliability and fault tolerance. It uses a replication strategy to determine the nodes where replicas are placed.

SimpleStrategy: Used for a single data center. Replicates data on the next nodes clockwise in the ring.

Code Example:

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CREATE KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'SimpleStrategy', 'replication_factor' : 3 };

NetworkTopologyStrategy: Used for multiple data centers. Allows specifying how many replicas you want in each data center.

Code Example:

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CREATE KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'NetworkTopologyStrategy', 'DC1' : 3, 'DC2' : 2 };

Replication Factor: Determines the number of replicas in the cluster.

Code Example:

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ALTER KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'NetworkTopologyStrategy', 'replication_factor' : 3 };

Consistency Levels

Cassandra offers various consistency levels, allowing a balance between availability and data accuracy.

ONE: Returns a response from the closest replica, as determined by the snitch.

Code Example:

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session.execute("SELECT * FROM mytable WHERE id = 123",
                ConsistencyLevel.ONE);

QUORUM: Ensures that the read or write operation is successful on the majority of the replicas.

Code Example:

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session.execute("SELECT * FROM mytable WHERE id = 123",
                ConsistencyLevel.QUORUM);

ALL: The highest level of consistency, requiring all replicas to respond.

Code Example:

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session.execute("SELECT * FROM mytable WHERE id = 123",
                ConsistencyLevel.ALL);

Real-World Use Cases

E-Commerce Platforms: For managing distributed product catalogs and user data.
Content Management Systems: To store and retrieve various types of content across geographically distributed data centers.
IoT Applications: For handling time-series data from sensors, spread across different locations.
Messaging Services: To provide reliable message delivery and storage across different regions.
Online Gaming: For player data and real-time event processing in a distributed manner.

Question 3. Can you explain the architecture of Cassandra, including concepts like nodes, clusters, data centers, and partitioning?

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Cassandra's architecture is distinguished by its decentralized, distributed nature. Let's delve into its key components:

Nodes, Clusters, Data Centers, and Partitioning

Node: The fundamental unit in Cassandra, where data is stored.

Code Example:

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Node node = cluster.getMetadata().getAllHosts().iterator().next();

Cluster: A collection of nodes, where each node contains similar data.

Code Example:

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Metadata metadata = cluster.getMetadata();
System.out.printf("Connected to cluster: %s\\n", metadata.getClusterName());

Data Center: A group of nodes, typically located physically close together.

Code Example:

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CREATE KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'NetworkTopologyStrategy', 'datacenter1' : 3 };

Partitioning: Determines how data is distributed across the nodes in the cluster.

Code Example:

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TokenRange range = metadata.getTokenRanges().iterator().next();
Set<Host> hosts = metadata.getReplicas("mykeyspace", range);

Vnodes (Virtual Nodes): Helps in distributing data evenly across the cluster.

Code Example:

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ALTER TABLE mytable WITH compaction = {'class': 'LeveledCompactionStrategy'};

Real-World Use Cases

Content Delivery Networks (CDNs): For distributed storage of web and video content.
Telecommunications: For handling and storing large volumes of call logs and network data.
Log Management Solutions: Storing and analyzing large-scale log data from various sources.
Retail and E-commerce: For managing user data, product information, and inventory across multiple geographical locations.
Time Series Data in IoT: Storing sensor data from various devices in a scalable way.

Question 4. Describe how Cassandra ensures high availability and fault tolerance.

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Cassandra DB's design focuses heavily on high availability and fault tolerance, making it a robust choice for distributed systems. Here's an explanation with code examples and real-world use cases.

High Availability

Cassandra ensures high availability through its decentralized architecture, which allows it to handle failures gracefully.

Decentralized Nodes: Every node in Cassandra is identical. There is no master node, so the failure of a node doesn't affect the availability of data.

Code Example:

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Cluster cluster = Cluster.builder().addContactPoint("127.0.0.1").build();
Session session = cluster.connect("mykeyspace");

Data Replication: Data is replicated across multiple nodes to ensure no single point of failure.

Code Example:

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CREATE KEYSPACE mykeyspace WITH REPLICATION =
{ 'class' : 'SimpleStrategy', 'replication_factor' : 3 };

Hinted Handoff: Temporarily stores data meant for a downed node until it comes back online.

Code Example:

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ALTER TABLE mytable WITH gc_grace_seconds = 864000;

Read Repair: Inconsistencies detected during reads are corrected on the fly.

Code Example:

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session.execute(new SimpleStatement("SELECT * FROM mytable").setConsistencyLevel(ConsistencyLevel.ONE));

Write Timeouts: Allows customization of write operations' timeout settings for handling high loads and network issues.

Code Example:

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Statement stmt = new SimpleStatement("INSERT INTO mytable (id, name) VALUES (123, 'test')");
stmt.setWriteTimeoutMillis(5000);
session.execute(stmt);

Real-World Use Cases

Online Retail: Ensures 24/7 product availability and transaction processing.
Banking and Finance: For high-speed transactions and reliable data storage.
Healthcare Systems: For storing patient records, ensuring they are always accessible.
Telecommunications: Manages call records and network data without service interruptions.
Large-Scale IoT Deployments: Ensures constant availability of data from sensors and devices.

Question 5. How does Cassandra perform read and write operations, and what role does the concept of 'tunable consistency' play in these operations?

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Cassandra DB's performance is significantly influenced by its read and write operations, which are designed for high efficiency and scalability. Let's explore these operations with code examples and real-world use cases.

Write Operations

Write Path: Writes in Cassandra are first written to a commit log and then to a memory structure called MemTable.

Code Example:

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session.execute("INSERT INTO mytable (id, name) VALUES (1, 'Data Entry')");

Commit Log: Ensures data durability. If a node crashes, data can be recovered from the commit log.

Code Example:

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session.execute("INSERT INTO mytable (id, name) VALUES (2, 'Commit Log Entry')");

MemTable: A write-back cache of data partitions that have been recently written.

Code Example:

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PreparedStatement pst = session.prepare("INSERT INTO mytable (id, name) VALUES (?, ?)");
session.execute(pst.bind(3, "MemTable Entry"));

SSTable: When a MemTable is full, it's flushed to disk as an SSTable (Sorted String Table).

Code Example:

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session.execute("INSERT INTO mytable (id, name) VALUES (4, 'SSTable Entry')");

Compaction: The process of merging SSTables to optimize read operations.

Code Example:

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session.execute("ALTER TABLE mytable WITH compaction = {'class': 'SizeTieredCompactionStrategy'}");

Read Operations

Read Path: Reads in Cassandra involve checking both MemTables and SSTables.

Code Example:

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ResultSet rs = session.execute("SELECT * FROM mytable WHERE id = 1");

Bloom Filters: Used to quickly determine if a row is not present in an SSTable, reducing disk I/O.

Code Example:

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// Bloom filters are used internally in read operations and are not directly accessible via CQL or Java API.

Partition Summary: Helps in locating the partition within the SSTable.

Code Example:

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// Partition summaries are used internally and are not directly accessible via CQL or Java API.

Partition Index: Used to find the exact location of rows within the partition.

Code Example:

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// Partition indexes are used internally and are not directly accessible via CQL or Java API.

Caching: Cassandra uses row cache and key cache to speed up read operations.

Code Example:

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session.execute("SELECT * FROM mytable WHERE id = 2");

Real-World Use Cases

Social Media Platforms: For managing large volumes of user-generated content and interactions.
Streaming Services: Handling large-scale, real-time data for personalized content delivery.
E-Commerce Sites: Managing inventory and user behavior data for real-time recommendations.
Financial Analytics: Processing and analyzing large datasets for real-time financial insights.
Sensor Data in Smart Cities: Managing and analyzing data from various IoT devices for urban planning and management.