After data is marked with a tombstone, the data is automatically removed during the normal compaction process. A modular assertion was implemented to take advantage of CL feature is Cassandra. This assertion supports two features:.

This allows each table to be optimised based on how it will be used. If a compaction strategy is not specified, SizeTieredCompactionStrategy will be used.

This is the default compaction strategy. A minor compaction does not involve all the tables in a keyspace. Additional parameters allow STCS to be tuned to increase or decrease the number of compactions it performs and how tombstones are handled. You can manually start compaction using the nodetool compact command. It is up to customer to leave it to Cassandra to clean up tombstones based on its default compaction behavior, or use an explicit script to aggressively do the cleanup at intervals.

The volume of tombstones and frequency of compaction affects the overall performance of the database. Replication Factor RF Cassandra stores replicas on multiple nodes to ensure reliability and fault tolerance. Two replication strategies are available: SimpleStrategy is used only for a single datacenter and one rack. SimpleStrategy places the first replica on a node determined by the partitioner. Additional replicas are placed on the next nodes clockwise in the ring without considering topology rack or datacenter location.

A partitioner determines how data is distributed across the nodes in the cluster including replicas. NetworkTopologyStrategy is used for a cluster deployed across multiple datacenters.

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This strategy specifies how many replicas you want in each datacenter. NetworkTopologyStrategy places replicas in the same data center by walking the ring clockwise until reaching the first node in another rack.

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NetworkTopologyStrategy attempts to place replicas on distinct racks because nodes in the same rack or similar physical grouping often fail at the same time due to power, cooling, or network issues. Consistency Level CL Write consistency level determines the number of replicas on which the write must succeed before returning an acknowledgment to the client application. Read consistency level specifies how many replicas must respond to a read request before returning data to the client application. Cassandra Write Path Logging data in the commit log : When a write occurs, Cassandra appends the data to sequential, memory-mapped commit log on disk.

This provides configurable durability, as the commit log can be used to rebuild MemTables if a crash occurs before the MemTable is flushed to disk. Each physical table on each replica node has an associated MemTable. The memtable is a write-back cache of data partitions that Cassandra looks up by key.

The memtable stores writes until reaching a limit, and then is flushed. Flushing data from the MemTable : When MemTable contents exceed a configurable threshold, the memtable data, which includes indexes, is put in a queue with a configurable length, to be flushed to disk. If the data to be flushed exceeds the queue size, Cassandra blocks writes until the next flush succeeds. Storing data on disk in SSTables : Data in the commit log is purged after its corresponding data in the memtable is flushed to an SSTable.

Memtables and SSTables are maintained per table. SSTables are immutable, not written to again after the memtable is flushed. A replication factor of 2 means two copies of each row, where each copy is on a different node. All replicas are equally important; there is no primary or master replica. As a general rule, the replication factor should not exceed the number of nodes in the cluster.

However, you can increase the replication factor and then add the desired number of nodes later. When replication factor exceeds the number of nodes, writes are rejected, but reads are served as long as the desired consistency level can be met. There are the two primary considerations when deciding how many replicas to configure in each data center:.

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