Anti-patterns
Implementation or design patterns that are ineffective and/or counterproductive in Cassandra production installations. Correct patterns are suggested in most cases.
Storage area network
DataStax strongly recommends against using SAN storage for on-premises deployments.
- SAN ROI (return on investment) does not scale along with that of Cassandra databases, in terms of capital expenses and engineering resources.
- In distributed architectures, SAN generally introduces a bottleneck and single point of failure because the database's IO frequently surpasses the array controller's ability to keep up.
- External storage, even when used with a high-speed network and SSD, adds latency for all operations.
- Heap pressure is increased because pending I/O operations take longer.
- When the SAN transport shares operations with internal and external database traffic, it can saturate the network and lead to network availability problems.
Taken together these factors can create problems that are difficult to resolve in production. In particular, new users deploying Cassandra databases with SAN must first develop adequate methods and allocate sufficient engineering resources to identify these issues before they become a problem in production. For example, methods are needed for all key scaling factors, such as operational rates and SAN fiber saturation.
Network attached storage
DataStax does not recommend storing SSTables on a network attached storage (NAS) device. Using a NAS device often results in network related bottlenecks resulting from high levels of I/O wait time on reads and writes. The causes of these bottlenecks include:
- Router latency.
- The Network Interface Card (NIC) in the node.
- The NIC in the NAS device.
Excessive heap space size
This information applies only to single-token architecture, not to virtual nodes. See "Tuning Java resources" (3.x | 3.0 | 2.2 | 2.1).
Rack feature in single-token architecture deployments
This information applies only to single-token architecture, not to virtual nodes. See "Data distribution and replication" (3.x | 3.0 | 2.2 | 2.1).
Defining one rack for the entire cluster is the simplest and most common implementation. Multiple racks should be avoided for the following reasons:
- Most users tend to ignore or forget rack requirements that racks should be organized in an alternating order. This order allows the data to get distributed safely and appropriately.
- Many users do not use the rack information effectively. For example, setting up with as many racks as nodes or similar non-beneficial scenarios.
- Expanding a cluster when using racks can be tedious. The procedure typically involves several node moves and must ensure that racks are distributing data correctly and evenly. When clusters need immediate expansion, racks should be the last concern.
To use racks correctly:
- Use the same number of nodes in each rack.
- Use one rack and place the nodes in different racks in an alternating pattern. The rack feature benefits from quick and fully functional cluster expansions. Once the cluster is stable, you can swap nodes and make the appropriate moves to ensure that nodes are placed in the ring in an alternating fashion with respect to the racks.
SELECT ... IN or index lookups
Using the Byte Ordered Partitioner
The Byte Ordered Partitioner (BOP) is not recommended.
Use virtual nodes (vnodes) instead. Vnodes allow each node to own a large number of small ranges distributed throughout the cluster. Using vnodes saves you the effort of generating tokens and assigning tokens to your nodes. If not using vnodes, these partitioners are recommended because all writes occur on the hash of the key and are therefore spread out throughout the ring amongst tokens range. These partitioners ensure that your cluster evenly distributes data by placing the key at the correct token using the key's hash value.
Reading before writing
Reads take time for every request, as they typically have multiple disk hits for uncached reads. In workflows requiring reads before writes, this small amount of latency can affect overall throughput. All write I/O in the database is sequential so there is very little performance difference regardless of data size or key distribution.Load balancers
The Cassandra database was designed to avoid the need for load balancers. Putting load balancers between the database and clients is harmful to performance, cost, availability, debugging, testing, and scaling. All high-level clients, such as the Java and Python drivers, implement load balancing directly. For available drivers, see DataStax drivers.
Insufficient testing
Be sure to test at scale and production loads. This the best way to ensure your system will function properly when your application goes live. The information you gather from testing is the best indicator of what throughput per node is needed for future expansion calculations.
To properly test, see Testing your cluster before production.
Too many tables
Substantial performance degradation can be caused by having too many tables in a cluster. Because performance is influenced by a range of other factors, it is difficult to determine a universal table threshold; however, DataStax provides the following guidelines:
- Warning threshold: 200 tables.
The cluster may run smoothly above this threshold, but once your database reaches the warning threshold, it's time to start monitoring and planning a re-architecture. If possible, remove unused and underutilized tables.
- Failure threshold: 500 tables.
On a cluster that has exceeded 500 tables, expect problems and errors, including (but not limited) issues related to high memory usage and compactions.
Lack of familiarity with Linux
Linux has a great collection of tools. Familiarity with the Linux built-in tools will greatly help you and ease operation and management costs in normal, routine functions. Learn these essential tools and techniques:
- Parallel SSH and Cluster SSH: The pssh and cssh tools allow SSH access to multiple nodes. This is useful for inspections and cluster wide changes.
- Passwordless SSH: SSH authentication is carried out by using public and private keys. This allows SSH connections to easily hop from node to node without password access. In cases where more security is required, you can implement a bastion host and/or VPN.
- Useful common command-line tools include:
- dstat: Shows all system resources instantly. For example, you can compare disk usage in combination with interrupts from your IDE controller, or compare the network bandwidth numbers directly with the disk throughput (in the same interval).
- top: Provides an ongoing look at CPU processor activity in real time.
- System performance tools: Tools such as iostat, mpstat, iftop, sar, lsof, netstat, htop, vmstat, and similar can collect and report a variety of metrics about the operation of the system.
- vmstat: Reports information about processes, memory, paging, block I/O, traps, and CPU activity.
- iftop: Shows a list of network connections. Connections are ordered by bandwidth usage, with the pair of hosts responsible for the most traffic at the top of list. This tool makes it easier to identify the hosts causing network congestion.
Running without the recommended settings
Be sure to use the recommended settings for your DataStax database. See Recommended production settings (3.x | 3.0 | 2.2 | 2.1).