Apache Cassandra Write Performance Tunning Tips | 2023

Performance tuning in Cassandra involves optimizing various configuration parameters and settings to ensure efficient read and write operations. Here are some example commands and configurations you can use to improve write performance in Cassandra:

1. Batch Writes: Use batch statements to combine multiple write operations into a single request. This reduces the number of round-trips between the client and the Cassandra cluster.

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BEGIN BATCH
INSERT INTO table_name (column1, column2) VALUES ('value1', 'value2');
INSERT INTO table_name (column1, column2) VALUES ('value3', 'value4');
APPLY BATCH;

1. Tune Consistency Levels: Adjust the consistency level for write operations to balance between performance and data consistency. Lower consistency levels (ONE, ANY) can provide better write performance at the cost of potential data inconsistencies during failures.

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CONSISTENCY ONE; -- or ANY, depending on your use case
INSERT INTO table_name (column1, column2) VALUES ('value1', 'value2');

1. Batch Size: Configure the batch size for write requests to control the number of mutations per batch. Adjust this based on your hardware and network capabilities.

In cassandra.yaml:

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batch_size_warn_threshold_in_kb: 5
batch_size_fail_threshold_in_kb: 50

1. Compression: Enable compression to reduce the size of data on disk and during network transfer.

In cassandra.yaml:

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internode_compression: all

1. Memtable Configuration: Adjust memtable settings to control how much data is kept in memory before flushing to disk.

In cassandra.yaml:

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memtable_allocation_type: heap_buffers
memtable_flush_writers: 1

1. Commitlog Configuration: Optimize commitlog settings to balance write performance and durability.

In cassandra.yaml:

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commitlog_total_space_in_mb: 4096
commitlog_segment_size_in_mb: 128

1. Partition Key Design: Design your data model with efficient partition keys to evenly distribute writes across nodes.
2. Data Modeling: Choose an appropriate data model based on your query patterns to minimize write amplification and avoid hotspots.
3. Remember that performance tuning is a complex process, and the optimal settings may vary depending on your specific use case, hardware, and workload. Always monitor your system and benchmark different configurations to find the best settings for your Cassandra cluster.