What are important factors in sizing a ksqlDB cluster? (Choose three.)

Sizing a ksqlDB cluster requires evaluating the workload's demands on compute, memory, and network resources. The three primary factors are:

1. Number of Queries: Each persistent query runs as a dedicated Kafka Streams application. More queries consume more CPU and memory. The complexity of these queries (e.g., joins, aggregations) also significantly impacts resource utilization.

2. Number of Partitions: ksqlDB achieves parallelism by assigning topic partitions to processing tasks. The number of partitions in the input topics dictates the maximum level of parallelism, directly influencing how many ksqlDB nodes can share the processing load and thus determining the required cluster size for a given throughput.

3. Data Schema: The complexity and size of the message schema affect the CPU and memory needed for serialization/deserialization and processing each message. Complex, nested schemas require more resources than simple, flat ones.

D. Data Encryption: While TLS encryption adds some CPU overhead, it is a baseline operational cost and not considered a primary sizing dimension compared to the core workload characteristics like query volume or data complexity.

E. Topic Data Retention: This setting primarily affects storage on the Kafka brokers, not the ksqlDB cluster's compute resources. ksqlDB's state size is determined by query logic (e.g., window size), not topic retention.

1. Confluent Documentation, "Capacity Sizing for ksqlDB": This document explicitly outlines the key factors for sizing. It states, "The primary factors that affect the performance of your ksqlDB applications are the number of events to be processed and the complexity of the processing." This supports the importance of Data Schema (A) and Number of Queries (B).

Reference: Confluent, Inc. (2023). Capacity Sizing for ksqlDB. Confluent Documentation. Retrieved from https://docs.confluent.io/platform/current/ksql/capacity-sizing.html (See section "Factors that affect performance").

2. Confluent Documentation, "Scale Your ksqlDB Application": This guide details the relationship between scaling, servers, and partitions. It clarifies, "The maximum parallelism of a ksqlDB application is bounded by the number of partitions of its input topics." This directly supports the significance of the Number of Partitions (C) in determining how a cluster can be scaled out.

Reference: Confluent, Inc. (2023). Scale Your ksqlDB Application. Confluent Documentation. Retrieved from https://docs.confluent.io/platform/current/ksql/scale-ksqldb.html (See section "Scaling ksqlDB").