DataStax Enterprise 2.1 Documentation
Planning a Production DataStax Enterprise Deployment¶
This section provides guidelines for determining the size of your production Cassandra cluster based on the data you plan to store.
Planning includes the following activities:
- Prerequisites
- Selecting Hardware for Enterprise Implementations
- Planning an Amazon EC2 Cluster
- Calculating Usable Disk Capacity
- Calculating User Data Size
- Choosing Node Configuration Options
- Choosing Keyspace Replication Options
Prerequisites¶
Before starting to plan a production cluster, you need:
- A good understanding of the size of the raw data you plan to store.
- A good estimate of your typical application workload.
- A plan to model your data in Cassandra (number of column families, rows, columns per row, and so on).
Selecting Hardware for Enterprise Implementations¶
As with any application, choosing appropriate hardware depends on selecting the right balance of the following resources: memory, CPU, disks, number of nodes, and network.
Note
Hadoop and Solr nodes require their own nodes/disks and have specific hardware requirements. See the Hadoop and Solr documentation for more information when determining your capacity requirements.
Memory¶
The more memory a Cassandra node has, the better read performance. More RAM allows for a larger file system cache and reduces disk I/O for reads. The ideal amount of RAM depends on the anticipated size of your hot data.
DataStax recommends the following memory requirements:
- For dedicated hardware, a minimum of 8GB of RAM is needed. For most implementations you should use 16GB to 32GB.
- Java heap space should be set to a maximum of 8GB or half of your total RAM, whichever is lower. (A greater heap size has more intense garbage collection periods.)
- For a virtual environment use a minimum of 4GB, such as Amazon EC2 Large instances. For production clusters with a healthy amount of traffic, 8GB is more common.
- For Solr and Hadoop nodes, use 32GB or more of total RAM.
CPU¶
Insert-heavy workloads are CPU-bound in Cassandra before becoming memory-bound. Cassandra is highly concurrent and uses as many CPU cores as available.
- For dedicated hardware, 8-core processors are the current price-performance sweet spot.
- For virtual environments, consider using a provider that allows CPU bursting, such as Rackspace Cloud Servers.
Disk¶
What you need for your environment depends a lot on the usage, so it's important to understand the mechanism. Cassandra writes data to disk for two purposes:
- All data is appended to the commit log for durability.
- When thresholds are reached, Cassandra periodically flushes in-memory data structures (memtables) to immutable SSTable data files for storage of column family data.
Commit logs receive every write made to a Cassandra node, but are only read during node start up. Commit logs are purged after the corresponding data is flushed. Conversely, SSTable (data file) writes occur asynchronously and are read during client look-ups. Additionally, SSTables are periodically compacted. Compaction improves performance by merging and rewriting data and discarding old data. However, during compaction (or node repair), disk utilization and data directory volume can substantially increase. For this reason, DataStax recommends leaving an adequate amount of free disk space available on a node (50% [worst case] for tiered compaction, 10% for leveled compaction).
Recommendations:
DataStax neither supports nor recommends using Network Attached Storage (NAS) because of performances issues, such as network saturation, I/O overload, pending-task swamp, excessive memory usage, and disk contention.
When choosing disks, consider both capacity (how much data you plan to store) and I/O (the write/read throughput rate). Most workloads are best served by using less expensive SATA disks and scaling disk capacity and I/O by adding more nodes (with more RAM).
Solid-state drives (SSDs) are a valid choice for Cassandra. Cassandra's sequential, streaming write patterns minimize the undesirable effects of write amplification associated with SSDs.
Ideally Cassandra needs at least two disks, 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 - this disk does not need to be large, but it should be fast enough to receive all of your writes as appends (sequential I/O).
Data disks - use one or more disks 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 - the compaction process can temporarily require up to double the normal data directory volume. This means when approaching 50% of disk capacity, you should use RAID 0 or RAID 10 for your data directory volumes. RAID also helps smooth out I/O hotspots within a single SSTable.
- Use RAID0 if disk capacity is a bottleneck and rely on Cassandra's replication capabilities for disk failure tolerance. If you lose a disk on a node, you can recover lost data through Cassandra's built-in repair.
- Use a setra setting of 512, especially on Amazon EC2 RAID0 devices. See http://www.datastax.com/docs/1.0/install/recommended_settings#optimum-blockdev-setra-settings-for-raid.
- Use RAID10 to avoid large repair operations after a single disk failure, or if you have disk capacity to spare.
- Because data is stored in the memtable, generally RAID is not needed for the commit log disk, but if you need the extra redundancy, use RAID 1.
Extended file systems - On ext2 or ext3, the maximum file size is 2TB even using a 64-bit kernel. On ext4 it is 16TB.
Because Cassandra can use almost half your disk space for a single file, use XFS when raiding large disks together, 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.
Number of Nodes¶
The amount of data on each disk in the array isn't as important as the total size per node. Using a greater number of smaller nodes is better than using fewer larger nodes because of potential bottlenecks on larger nodes during compaction.
Network¶
Since Cassandra is a distributed data store, it puts load on the network to handle read/write requests and replication of data across nodes. Be sure to choose reliable, redundant network interfaces and make sure that your network can handle traffic between nodes without bottlenecks.
- Recommended bandwith is 1000 Mbit/s (Gigabit) or greater.
- Bind the Thrift interface (listen_address) to a specific NIC (Network Interface Card).
- Bind the RPC server interface (rpc_address) to another NIC.
Cassandra is efficient at routing requests to replicas that are geographically closest to the coordinator node handling the request. Cassandra will pick a replica in the same rack if possible, and will choose replicas located in the same data center over replicas in a remote data center.
Ports¶
If using a firewall, make sure that nodes within a cluster can reach each other. See Configuring Firewall Port Access.
Planning an Amazon EC2 Cluster¶
DataStax Enterprise clusters can be deployed on cloud infrastructures such as Amazon EC2.
For most production clusters, use or Extra Large instances with local storage. For low to medium data throughput production clusters, use Large instances with local storage (which are generally adequate for about a year). RAID0 the ephemeral disks, and put both the data directory and the commit log on that volume. This has proved to be better in practice than putting the commit log on the root volume (which is also a shared resource). For data redundancy, consider deploying your cluster across multiple availability zones or using EBS volumes to store your backup files.
EBS volumes are not recommended for Cassandra data volumes - their network performance and disk I/O are not good fits for Cassandra for the following reasons:
- EBS volumes contend directly for network throughput with standard packets. This means that EBS throughput is likely to fail if you saturate a network link.
- EBS volumes have unreliable performance. I/O performance can be exceptionally slow, causing the system to backload reads and writes until the entire cluster becomes unresponsive.
- Adding capacity by increasing the number of EBS volumes per host does not scale. You can easily surpass the ability of the system to keep effective buffer caches and concurrently serve requests for all of the data it is responsible for managing.
DataStax provides an Amazon Machine Image (AMI) to allow you to quickly deploy a multi-node Cassandra cluster on Amazon EC2. The DataStax AMI initializes all nodes in one availability zone using the SimpleSnitch.
If you want an EC2 cluster that spans multiple regions and availability zones, do not use the DataStax AMI. Instead, initialize your EC2 instances for each Cassandra node and then configure the cluster as a multi data center cluster.
Calculating Usable Disk Capacity¶
To calculate how much data your Cassandra nodes can hold, calculate the usable disk capacity per node and then multiply that by the number of nodes in your cluster. Remember that in a production cluster, you will typically have your commit log and data directories on different disks. This calculation is for estimating the usable capacity of the data volume.
Start with the raw capacity of the physical disks:
raw_capacity = disk_size * number_of_disks
Account for file system formatting overhead (roughly 10 percent) and the RAID level you are using. For example, if using RAID-10, the calculation would be:
(raw_capacity * 0.9) / 2 = formatted_disk_space
During normal operations, Cassandra routinely requires disk capacity for compaction and repair operations. For optimal performance and cluster health, DataStax recommends that you do not fill your disks to capacity, but run at 50-80 percent capacity. With this in mind, calculate the usable disk space as follows (example below uses 50%):
formatted_disk_space * 0.5 = usable_disk_space
Calculating User Data Size¶
As with all data storage systems, the size of your raw data will be larger once it is loaded into Cassandra due to storage overhead. On average, raw data will be about 2 times larger on disk after it is loaded into the database, but could be much smaller or larger depending on the characteristics of your data and column families. The calculations in this section account for data persisted to disk, not for data stored in memory.
Column Overhead - Every column in Cassandra incurs 15 bytes of overhead. Since each row in a column family can have different column names as well as differing numbers of columns, metadata is stored for each column. For counter columns and expiring columns, add an additional 8 bytes (23 bytes column overhead). So the total size of a regular column is:
total_column_size = column_name_size + column_value_size + 15
Row Overhead - Just like columns, every row also incurs some overhead when stored on disk. Every row in Cassandra incurs 23 bytes of overhead.
Primary Key Index - Every column family also maintains a primary index of its row keys. Primary index overhead becomes more significant when you have lots of skinny rows. Sizing of the primary row key index can be estimated as follows (in bytes):
primary_key_index = number_of_rows * (32 + average_key_size)
Replication Overhead - The replication factor obviously plays a role in how much disk capacity is used. For a replication factor of 1, there is no overhead for replicas (as only one copy of your data is stored in the cluster). If replication factor is greater than 1, then your total data storage requirement will include replication overhead.
replication_overhead = total_data_size * (replication_factor - 1)
Choosing Node Configuration Options¶
A major part of planning your Cassandra cluster deployment is understanding and setting the various node configuration properties. This section explains the various configuration decisions that need to be made before deploying a Cassandra cluster, be it a single-node, multi-node, or multi-data center cluster.
The properties mentioned in this section are set in the cassandra.yaml configuration file. Each node should be correctly configured before starting it for the first time.
Storage Settings¶
By default, a node is configured to store the data it manages in /var/lib/cassandra. In a production cluster deployment, you should change the commitlog_directory so it is on a different disk device than the data_file_directories.
Gossip Settings¶
The gossip settings control a nodes participation in a cluster and how the node is known to the cluster.
| Property | Description |
|---|---|
| cluster_name | Name of the cluster that this node is joining. Should be the same for every node in the cluster. |
| listen_address | The IP address or hostname that other Cassandra nodes will use to connect to this node. Should be changed from localhost to the public address for the host. |
| seeds | A comma-delimited list of node IP addresses used to bootstrap the gossip process. Every node should have the same list of seeds. In multi data center clusters of Cassandra or analytics nodes, the seed list should include a node from each data center. |
| storage_port | The intra-node communication port (default is 7000). Should be the same for every node in the cluster. |
| initial_token | The initial token is used to determine the range of data this node is responsible for. |
Purging Gossip State on a Node¶
Gossip information is also persisted locally by each node to use immediately next restart without having to wait for gossip. To clear gossip history on node restart (for example, if node IP addresses have changed), add the following line to the cassandra-env.sh file. This file is located in /usr/share/cassandra or $CASSANDRA_HOME/conf in Cassandra installations.
-Dcassandra.load_ring_state=false
Partitioner Settings¶
When you deploy a Cassandra cluster, you need to make sure that each node is responsible for roughly an equal amount of data. This is also known as load balancing. This is done by configuring the partitioner for each node, and correctly assigning the node an initial-token value.
DataStax strongly recommends using the RandomPartitioner (the default) for all cluster deployments. Assuming use of this partitioner, each node in the cluster is assigned a token that represents a hash value within the range of 0 to 2**127.
For clusters where all nodes are in a single data center, you can calculate tokens by dividing the range by the total number of nodes in the cluster. In multi-data center deployments, tokens should be calculated such that each data center is individually load balanced as well. See Generating Tokens for the different approaches to generating tokens for nodes in single and multi-data center clusters.
Snitch Settings¶
The snitch is responsible for knowing the location of nodes within your network topology. The location of nodes affects where replicas are placed and how requests are routed between replicas. All nodes must have exactly the same snitch configuration. The endpoint_snitch property configures the snitch for a node.
In cassandra.yaml, the snitch is set to the DSE Delegated Snitch (endpoint_snitch: com.datastax.bdp.snitch.DseDelegateSnitch). The Delegated Snitch is used to implement Elastic Workload Re-provisioning. The following sections describe a few commonly-used snitches. All snitches are described in the Apache Cassandra documentation.
DSE Delegated Snitch¶
In DataStax Enterprise, the default delegated snitch is the DseSimpleSnitch (org.apache.cassandra.locator.DseSimpleSnitch), located in:
- Packaged installations: /etc/dse/dse.yaml
- Tarball installations: <install_location>/resources/dse/conf/dse.yaml
You use the delegated snitch for re-provisioning the workload of a cluster.
DseSimpleSnitch¶
To segregate analytics and real-time workloads, this snitch logically configures Hadoop analytics nodes in a separate data center from Cassandra real-time nodes. Use DseSimpleSnitch for mixed-workload DSE clusters located in one physical data center or for multiple data center DSE clusters that have exactly two data centers: one with all Analytics nodes and the other with all Cassandra real-time nodes.
When defining your keyspace strategy_options, use Analytics or Cassandra for your analytics or real-time data center names, respectively.
SimpleSnitch¶
For a single data center (or single node) cluster, using SimpleSnitch is usually sufficient. However, if you plan to expand your cluster at a later time to multiple racks and data centers, it is easier if you choose a rack and data center aware snitch from the start, such as the RackInferringSnitch . All snitches are compatible with all placement strategies.
Configuring the PropertyFileSnitch¶
The PropertyFileSnitch allows you to define your data center and rack names to be whatever you want. Using this snitch requires that you define network details for each node in the cluster in a cassandra-topology.properties configuration file. This file is located in /etc/dse/cassandra/conf/cassandra.yaml in packaged installations or <install_location>/resources/cassandra/conf/cassandra.yaml in binary installations.
Every node in the cluster should be described in this file, and specified exactly the same on every node in the cluster.
For example, supposing you had non-uniform IPs and two physical data centers with two racks in each, and a third logical data center for replicating analytics data:
# Data Center One
175.56.12.105=DC1:RAC1
175.50.13.200=DC1:RAC1
175.54.35.197=DC1:RAC1
120.53.24.101=DC1:RAC2
120.55.16.200=DC1:RAC2
120.57.102.103=DC1:RAC2
# Data Center Two
110.56.12.120=DC2:RAC1
110.50.13.201=DC2:RAC1
110.54.35.184=DC2:RAC1
50.33.23.120=DC2:RAC2
50.45.14.220=DC2:RAC2
50.17.10.203=DC2:RAC2
# Analytics Replication Group
172.106.12.120=DC3:RAC1
172.106.12.121=DC3:RAC1
172.106.12.122=DC3:RAC1
# default for unknown nodes
default=DC3:RAC1
Make sure the data center names defined in the /etc/dse/cassandra/cassandra-topology.properties file correlates to what you name your data centers in your keyspace strategy-options.
Choosing Keyspace Replication Options¶
When you create a keyspace, you must define the replica placement strategy and the number of replicas you want. DataStax recommends always choosing NetworkTopologyStrategy for both single and multi-data center clusters. It is as easy to use as SimpleStrategy and allows for expansion to multiple data centers in the future, should that become useful. It is much easier to configure the most flexible replication strategy up front, than to reconfigure replication after you have already loaded data into your cluster.
NetworkTopologyStrategy takes as options the number of replicas you want per data center. Even for single data center (or single node) clusters, you can use this replica placement strategy and just define the number of replicas for one data center. For example (using cassandra-cli):
[default@unknown] CREATE KEYSPACE test
WITH placement_strategy = 'NetworkTopologyStrategy'
AND strategy_options=[{us-east:6}];
Or for a multi-data center cluster:
[default@unknown] CREATE KEYSPACE test
WITH placement_strategy = 'NetworkTopologyStrategy'
AND strategy_options=[{DC1:6,DC2:6,DC3:3}];
When declaring the keyspace strategy-options, what you name your data centers depends on the snitch you have chosen for your cluster. The data center names must correlate to the snitch you are using in order for replicas to be placed in the correct location.
As a general rule, the number of replicas should not exceed the number of nodes in a replication group. However, it is possible to increase the number of replicas, and then add the desired number of nodes afterwards. When the replication factor exceeds the number of nodes, writes will be rejected, but reads will still be served as long as the desired consistency level can be met.