Cassandra Data Modeling Best Practices Quiz Quiz

Challenge your understanding of essential data modeling best practices in Cassandra, focusing on design strategies, query patterns, denormalization, partitioning, and schema choices to optimize performance and scalability.

1. Primary Key Selection

   Which of the following is a recommended approach when choosing a primary key for a Cassandra table?

   1. Use the same primary key structure as in a relational database
   2. Rely on auto-incremented numeric IDs as the primary key
   3. Pick random fields to distribute data evenly
   4. Include fields that determine uniqueness and support your expected query patterns

   Explanation: Choosing fields that ensure uniqueness and match query requirements is essential for effective primary key selection in Cassandra. Simply copying primary key structures from relational databases may overlook Cassandra's different query model. Randomly picking fields may not reflect query access patterns. Auto-incremented IDs can cause hotspots and are not recommended in distributed environments.

2. Data Denormalization

   Why is denormalization considered a best practice when modeling data in Cassandra?

   1. To reduce storage space requirements
   2. Because joins are not supported, and queries should be fast without requiring additional lookups
   3. To ensure strong transactional consistency
   4. Because it is required for all types of queries

   Explanation: Denormalizing data enables queries to access all necessary information in a single table, avoiding the need for joins. Reducing storage is not the main goal, as denormalization actually increases storage use. Cassandra's consistency model does not focus on strong transactional consistency. Denormalization is not required for every query, but it benefits performance for many common access patterns.

3. Query-Based Table Design

   In Cassandra, why should you design tables around your queries rather than the data structure?

   1. To match normalization rules from relational modeling
   2. Because it automatically adapts tables to changing queries
   3. To minimize the number of partitions at all costs
   4. Because Cassandra is optimized for fast write and read operations when query patterns are pre-defined

   Explanation: Designing tables based on expected queries ensures high performance for both reads and writes. Relational normalization is less applicable in Cassandra's architecture. While partition count matters, blindly minimizing partitions can harm performance. Cassandra does not automatically adjust tables for different queries; explicit design is needed.

4. Partition Key Choice

   When choosing a partition key, what is the most important factor to consider for data distribution?

   1. Ensuring the key distributes data evenly across nodes
   2. Using a single static value for all records
   3. Selecting the shortest possible string
   4. Picking keys that always start with the same letter

   Explanation: Even data distribution across nodes prevents hotspots and ensures scalability in Cassandra. Short strings do not guarantee even distribution and may limit uniqueness. Using a single static value collects all data into one partition, causing performance issues. Keys starting with the same letter can also create uneven distribution if data is not random.

5. Write Patterns

   Which scenario exemplifies a write pattern that should be avoided in Cassandra?

   1. Writing frequently to the same partition with millions of updates
   2. Spreading writes evenly across many partitions
   3. Batching writes by grouping multiple rows for different partitions
   4. Inserting time-series data with modest volume per partition

   Explanation: Writing repeatedly to a single partition can cause hotspots and degrade performance in Cassandra's distributed system. Evenly spread writes across partitions improve scalability. Batching writes for different partitions is efficient because it distributes load. Reasonable volumes of time-series data per partition are generally manageable.

6. Wide Rows

   What is a potential risk of designing wide rows (rows with many columns) in Cassandra?

   1. Rows may become too large to efficiently store or read, causing performance degradation
   2. Wide rows delete older data automatically
   3. Cassandra cannot store more than ten columns in a row
   4. Querying wide rows always results in out-of-memory errors

   Explanation: Extremely wide rows may accumulate so much data that they become inefficient to process, potentially hurting read and write performance. There is no strict limit of ten columns. Out-of-memory errors are not guaranteed but are possible with extreme row width, not just any wide row. Automatic deletion of older data does not occur unless explicitly configured.

7. Secondary Index Usage

   When is it generally safe to use secondary indexes in Cassandra for query support?

   1. For queries expected to return all data from large tables
   2. Whenever data is denormalized
   3. When performing low-cardinality lookups on small tables with infrequent writes
   4. Whenever you need to query by a non-primary key column

   Explanation: Secondary indexes perform best with small tables and low-cardinality values, where writes are not frequent. Using them for any non-key column or large scans is inefficient. Denormalizing data helps avoid the need for secondary indexes, rather than the other way around.

8. Batch Operations

   What is a best practice regarding batch operations in Cassandra?

   1. Avoid batches entirely as they are unsupported
   2. Use large batches to update many unrelated partitions at once
   3. Limit batch operations to rows belonging to the same partition for efficiency
   4. Batch all writes regardless of partition to increase throughput

   Explanation: Batching within a single partition is efficient because it minimizes coordination overhead. Large multi-partition batches can create performance issues and are not recommended. Batching unrelated partitions does not provide the intended benefits and may slow down operations. Batches are supported but should be used judiciously.

9. Time-to-Live (TTL) Use

   What is a common use case for the Time-to-Live (TTL) feature in Cassandra tables?

   1. Forcing permanent retention of all data
   2. Increasing the partition size limit
   3. Automatically expiring cache or session data after a defined period
   4. Ensuring that queries always return the latest data

   Explanation: TTL is designed to remove data after a specified time, making it ideal for cache or session expiration. It does not retain data permanently; in fact, it removes old data. TTL does not affect the default recency of query results and does not increase the partition size.

10. Avoiding Anti-patterns

    Which of the following is an anti-pattern in Cassandra data modeling?

    1. Performing schema changes frequently on large production tables
    2. Using clustering columns for sorting within a partition
    3. Applying denormalization for certain query responses
    4. Adapting data models per query requirements

    Explanation: Frequent schema changes on large tables can disrupt system operation, cause downtime, and impact performance, making it an anti-pattern. Modeling tables around queries is encouraged. Clustering columns are appropriate for organizing and sorting data within a partition. Denormalization is often a recommended practice in Cassandra, not an anti-pattern.