Capacity planning and hardware selection for DataStax Distribution of Apache Cassandra implementations

Choosing the appropriate hardware for DataStax Distribution of Apache Cassandra depends on selecting the right balance of memory, CPU, disks, number of nodes, and network resources.

General guidelines

Follow these guidelines when choosing hardware for your DataStax database:
  • Hardware choices depends on your particular use case. The right balance of memory, CPU, disks, number of nodes, and network are vastly different for environments with static data that are accessed infrequently than for volatile data that is accessed frequently.
  • The suggested guidelines are the minimum required. You may need to increase memory, CPU capacity, and disk space from the recommended minimums.
  • Be sure to read Anti-patterns for important information about SAN storage, NAS devices, and NFS.
  • Thoroughly test your configuration before deployment.


The more memory a DataStax Distribution of Apache Cassandra™ (DDAC) node has, the better read performance. More RAM also allows memory tables (memtables) to hold more recently written data. Larger memtables lead to a fewer number of SSTables being flushed to disk, more data held in the operating system page cache, and fewer files to scan from disk during a read. The ideal amount of RAM depends on the anticipated size of your hot data.

Table 1. Recommended minimum memory for dedicated hardware and virtual environments
Node type System memory Heap
Cassandra/DDAC (transactional) 32 GB 8 GB
Development (non-load testing environments)
Any 8 GB 4 GB
Any 16 GB 8 GB


Insert-heavy workloads are CPU-bound in DDAC before becoming memory-bound. All writes go to the commit log, but the database is so efficient in writing that the CPU is the limiting factor. The DataStax database is highly concurrent and uses as many CPU cores as available. Recommendations:

  • Minimum dedicated hardware for production: 16-core CPU processors (logical).
  • Dedicated hardware in development in non-loading testing environments: 2-core CPU processors (logical) are sufficient.

Disk space

Disk space depends on usage, so it's important to understand the mechanism. The database writes data to disk when appending data to the commit log for durability and when flushing memtables to SSTable data files for persistent storage. The commit log has a different access pattern (read/writes ratio) than the pattern for reading data from SSTables. This is more important for spinning disks than for SSDs.

SSTables are periodically compacted. Compaction improves performance by merging and rewriting data and discarding old data. However, depending on the type of compaction and size of the compactions, disk utilization and data directory volume temporarily increases during compaction. For this reason, be sure to leave an adequate amount of free disk space available on a node.

The following table provides guidelines for the minimum disk space requirements based on the compaction strategy:

Note: DateTieredCompactionStrategy (DTCS) is deprecated.
Table 2. Disk space requirements per compaction strategy
Compaction strategy Minimum requirements
SizeTieredCompactionStrategy (STCS) Sum of all the SSTables compacting must be smaller than the remaining disk space.
Note: Worst case: 50% of free disk space. This scenario can occur in a manual compaction where all SSTables are merged into one giant SSTable.
LeveledCompactionStrategy (LCS) Generally 10%. Worse case: 50% when the Level 0 backlog exceeds 32 SSTables (LCS uses STCS for Level 0).
TimeWindowCompactionStrategy (TWCS) TWCS requirements are similar to STCS. TWCS requires approximately 50% extra disk space for the total size of SSTables in the last created bucket.

To ensure adequate disk space, determine the size of the largest bucket ever generated and add 50% extra disk space.

Estimating usable disk capacity

To estimate how much data your nodes can hold, calculate the usable disk capacity per node and then multiply that by the number of nodes in your cluster. For a production cluster, DataStax recommends separating the commit log and data directories on different disks.

  1. Start with the raw capacity of the physical disks:
    raw_capacity = disk_size * number_of_data_disks
  2. Calculate the usable disk space accounting for file system formatting overhead (roughly 10 percent):
    formatted_disk_space = (raw_capacity * 0.9)
  3. Calculate the recommended working disk capacity:
    usable_disk_space = formatted_disk_space * (0.5 to 0.8)

During normal operations, the database routinely requires disk capacity for compaction and repair operations. For optimal performance and cluster health, DataStax recommends not filling your disks to capacity, but running at 50% to 80% capacity. See compaction strategy and size of the compactions.

Minimum disk space recommendations

Important: Thoroughly test before deploying to production.

DataStax highly recommends testing with the cassandra-stress tool at your desired configuration. Be sure to test common administrative operations, such as bootstrap, repair, and failure, to make certain your hardware selections meet your business needs. See Testing your cluster before production.

DDAC capacity policy

Unless a use-case specific exemption is approved by DataStax, a data capacity limitation of 1TB per Node data capacity limitation applies. Exceeding this value has the following effects:

Exceeding this data capacity limitations may have the following effects:
  • Extremely long times (days) for bootstrapping new nodes.
  • Negative impact on maintenance (day-to-day operations), such as recovering, adding, and replacing nodes.
  • Reduced efficiency when running repairs.
  • Significantly extended time to expand datacenters.
  • Substantially increased compactions per node.
Higher capacity nodes work best with static data and low access rates, such as time-series data.
Note: Additional data density is possible. Contact the DataStax Support Team to determine if the workload and hardware being used is appropriate for higher densities.

Unless an exemption is granted, DataStax will have no obligation to provide Support or any warranty for Nodes that exceed the data capacity limitations. Where customer is entitled to receive Support under its Subscription, DataStax will use reasonable efforts to respond to customer Support requests for Supported Software. However, DataStax may not be able to provide assistance for Issues relating to customer Nodes that are over capacity.

Capacity per node (node density)
Node capacity is highly dependent on the environment. Determining node density depends on many factors, including:
  • Data frequency change and access frequency.
  • Using HDDs or SSDs.
  • Storage speed and whether the storage is local.
  • SLAs (service-level agreements) and ability to handle outages.
  • Data compression.
  • Compaction strategy: choice of compaction strategy depends of whether the workload is write-intensive or read-intensive or time dependent. See Disk space above.
  • Network performance: remote links likely limits storage bandwidth and increase latency.
  • Replication factor: See About data distribution and replication.
To avoid problems, DataStax recommends keeping data per node near or below 1TB on DDAC. Exceeding this value has the following effects:
  • Extremely long times (days) for bootstrapping new nodes.
  • Impacts maintenance (day-to-day operations), such as recovering, adding, and replacing nodes.
  • Reduces efficiency when running repairs.
  • Significantly extends the time it takes to expand datacenters.
  • Substantially increases compactions per node.

Higher capacity nodes works best with static data and low access rates.

Note: Additional data density is possible. Contact the DataStax Services team to determine if the workload and hardware being used is appropriate for higher densities.
Capacity and I/O
When choosing disks for your nodes, consider both capacity (how much data you plan to store) and I/O (the write/read throughput rate). Some workloads are best served by using less expensive SATA disks and scaling disk capacity and I/O by adding more nodes (with more RAM).
Number of disks - HDD
DataStax recommends using at least two disks per node: one for the commit log and the other for the data directories. At a minimum, the commit log should be on its own partition.
Commit log disk - HDD
The disk need not be large, but it should be fast enough to receive all of your writes as appends (sequential I/O).
Commit log disk - SSD
Unlike spinning disks, there is less of a penalty for sharing commit logs and data directories on SSD than there is on HDD. DataStax recommends separating commit logs and data for highest performance and resiliency.
Data disks
Use one or more disks per node and make sure they are large enough for the data volume and fast enough to both satisfy reads that are not cached in memory and to keep up with compaction.
RAID on data disks
It is generally not necessary to use RAID for the following reasons:
  • Data is replicated across the cluster based on the replication factor you've chosen.
  • DDAC includes a JBOD (Just a bunch of disks) feature for disk management. Because the database responds according to your availability/consistency requirements to a disk failure either by stopping the affected node or by blacklisting the failed drive, you can deploy nodes with large disk arrays without the overhead of RAID 10. You can configure the database to stop the affected node or blacklist the drive according to your availability/consistency requirements. Also see Recovering from a single disk failure using JBOD.
RAID on the commit log disk
Generally RAID is not needed for the commit log disk. Replication adequately prevents data loss. If you need extra redundancy, use RAID 1.
Extended file systems
DataStax recommends deploying on XFS or ext4. On ext2 or ext3, the maximum file size is 2TB even using a 64-bit kernel. On ext4 it is 16TB.

Because the database can use almost half your disk space for a single file when using SizeTieredCompactionStrategy (STCS), use XFS when using large disks, particularly if using a 32-bit kernel. XFS file size limits are 16TB max on a 32-bit kernel, and essentially unlimited on 64-bit.

Estimating partition size

For efficient operation, partitions must be sized within certain limits. Two measures of partition size are the number of values in a partition and the partition size on disk. The practical limit of cells per partition is 2 billion. Sizing the disk space is more complex, and involves the number of rows and the number of columns, primary key columns and static columns in each table. Each application will have different efficiency parameters, but a good rule of thumb is to keep the maximum number of rows below 100,000 items and the disk size under 100 MB.


Minimum recommended bandwidth: 1000 Mb/s (gigabit).

A distributed data store puts load on the network to handle read/write requests and replication of data across nodes. Be sure that your network can handle inter-node traffic without bottlenecks. DataStax recommends binding your interfaces to separate Network Interface Cards (NIC). You can use public or private NICs depending on your requirements.

The database efficiently routes requests to replicas that are geographically closest to the coordinator node and chooses a replica in the same rack when possible. The database will always choose replicas located in the same datacenter over replicas in a remote datacenter.


If using a firewall, make sure that nodes within a cluster can communicate with each other. See Configuring firewall port access.