What Is Apache Cassandra Monitoring?

• Apache Cassandra Monitoring Definition

  Apache Cassandra Monitoring Definition

  Apache Cassandra monitoring is the practice of tracking and analyzing key performance metrics, system health, and resource utilization to ensure the stability, reliability, and efficiency of an Apache Cassandra database cluster. With effective Apache Cassandra monitoring, you can more easily detect potential issues, such as resource bottlenecks, disk usage spikes, node failures, and high latency before they significantly impact performance or availability.

  Organizations often use the Dropwizard Metrics library to collect and manage Apache Cassandra monitoring metrics. These metrics are collected on a per-node basis and can be queried via Java Management Extensions (JMX) or aggregated using a third-party monitoring system. By continuously monitoring Apache Cassandra metrics, you can optimize its performance, fine-tune your configurations, and scale your cluster as needed.
• What Is the Apache Cassandra Database?

  What Is the Apache Cassandra Database?

  Apache Cassandra database is an open-source, NoSQL database, meaning it stores data in a non-relational way and avoids the tabular structure of relational databases. As a result, it is more flexible and capable of storing unstructured data when compared to its SQL counterparts. It is also more scalable, thanks to its ability to add new nodes without downtime.

  Apache Cassandra is capable of handling large amounts of structured and semi-structured data across several nodes without a single point of failure. Due to its distributed architecture, it is also highly fault tolerant. Since Apache Cassandra is a distributed database that copies and stores information on multiple servers, it also offers increased data availability and reliability.

  Apache Cassandra is written in Java and runs on a Java Virtual Machine (JVM). Consequently, you can use Cassandra JMX metrics to collect Apache Cassandra monitoring metrics.
• Key Metrics to Monitor

  Key Metrics to Monitor

  There are plenty of performance metrics you can track while monitoring Cassandra. This can make it difficult to focus on the metrics that truly matter. To help you concentrate your Apache Cassandra monitoring efforts, here are some of the most important metrics you need to keep an eye on:

  • Throughput

  Throughput is a measurement of the number of read and write operations Cassandra processes per second. Monitoring throughput is essential for understanding how well your cluster handles workload demands and whether your system is operating efficiently under its current load.

  The read throughput measures the read requests processed per second. A sudden spike in read throughput might suggest an increase in user activity, inefficient queries, or issues with caching. The write throughput is the number of write requests processed each second. Apache Cassandra is capable of handling large volumes of write requests. Monitoring the volume of write requests is essential, as a high write throughput without proper tuning can increase disk utilization and compaction tasks.

  Cassandra also provides exponentially weighted moving averages for requests. These are expressed over fifteen-minute, five-minute, and one-minute intervals, with the one-minute intervals providing almost real-time visibility. You can access and view these metrics by request type or column family.

  It’s important to monitor the volume of both read and write requests, as this can give you a good idea of your cluster’s overall performance and activity levels. You can spot unexpected spikes and dips in read and write requests, which could help you identify potential Cassandra performance issues, detect anomalies in user activity, and proactively address bottlenecks before they impact your system. Monitoring these trends can also help you optimize resource allocation, appropriately scale your Apache Cassandra cluster, and fine-tune performance settings to ensure smooth and efficient operations. Plus, it will allow you to make a more informed decision when selecting your compaction strategy, as compaction strategies handle read and write workloads differently.

  • Latency
    It’s also vital to pay attention to latency, the amount of time it takes to fulfill a read or write request. The read latency represents the read response time in microseconds, while the write latency is the write response time expressed in microseconds. Read and write latencies are measured and represented with histograms at 50th, 75th, 90th, 95th, 99th, and 99.9th percentile values.

  Cassandra usually writes faster than it reads, as it requires less I/O and only needs to record writes in memory and append them to a commit log. Having consistently slow write speeds may mean you need to review your consistency settings or upgrade your disks to run on faster SSDs. However, suddenly experiencing slow write times indicates a change in usage patterns or new network issues.

  Compared to writing, reading is a slower process for Cassandra, particularly if you regularly update a row, causing it to be spread across multiple SSTables. Slow read times signal hardware issues, data model problems, or incorrect and inefficient current parameters. If you notice read latency increasing in an Apache Cassandra cluster running level-tiered compaction, you may need to tune your compaction strategy or add additional nodes.

  By viewing read and write metrics such as latencies, consistency level (how many replica nodes need to respond to a read or write request before it is successful), and replication factor (how many nodes have a replica of each row of data), you can gain insight into potential problems and usage changes. For example, consistently high latency or sudden jumps in latency might be a sign of data model issues, underlying infrastructure problems, or a cluster that has reached its available processing capacity. Reduced disk access, increased network latency, or a new replication configuration can also impact your read and write latency.

  • Disk usage and compaction

  By regularly reviewing how much disk space Cassandra uses on each node in your cluster, you can proactively add additional nodes before you run out of disk storage space. The exact amount of free disk space you need varies based on your compaction strategy, but it’s best to aim for around 30%.

  Your Apache Cassandra monitoring strategy should include looking at the load (or disk space used on a node), total disk space used, completed compaction tasks, and pending compaction tasks. If your disk usage increases too quickly or your pending compaction tasks start to accumulate, this can be a sign of performance issues. By carefully monitoring your disk usage, you can develop an understanding of potential resource constraints. This will help you add more nodes to your cluster as needed to prevent resource shortages and ensure Apache Cassandra is always able to run compactions.

  Compaction is a key element in managing disk usage, as it deletes old SSTables after creating and completing new ones. Essentially, compaction consolidates SSTables and removes outdated data marked with tombstones, which can lead to optimized read performance and disk usage.

  However, if you don’t properly manage compaction processes, you may experience excessive disk usage, increased I/O load, and Cassandra performance bottleneck issues. An excessive number of pending compaction tasks may be a sign your system can’t keep up with your current write load, leading to inefficient disk utilization and poor query performance. By selecting the right compaction strategy, monitoring compaction throughput, and carefully planning for capacity expansion, you can prevent disk space shortages and help Apache Cassandra maintain optimal performance.

  • Garbage collection

  As a Java-based system, Apache Cassandra regularly uses Java garbage collection processes to free up its memory. The more activity occurring in your Cassandra cluster, the more often your Java garbage collector will run.

  If you have many young generations (new objects), you will have a higher ParNew count (number of young-generation collections). This is because young-generation garbage collections occur more frequently than old-generation ones. ParNew garbage collections will pause all application threads during collection, meaning an increase in ParNew latency can significantly impact the performance of Apache Cassandra.

  In contrast, the ConcurrentMarkSweep (CMS) collector is for low-pause garbage collection that handles old objects. It will only temporarily and intermittently stop your application threads as it works to free up unused memory. If you notice your CMS garbage collection is taking a long time to complete or is happening regularly, it might be a sign your Cassandra cluster is running out of memory.

  • Errors and overruns

  Monitor your errors, overruns, and exceptions, as they can indicate underlying issues affecting the stability and reliability of your Apache Cassandra cluster. Notably, you should pay attention to timeout exceptions (requests not acknowledged within your configurable timeout window) and unavailable exceptions (requests where the required number of nodes was unavailable). Timeout exceptions can be signs of network issues or disks nearing storage capacity. In contrast, unavailable exceptions usually mean one or more nodes were down when they received the read or write request.

  It’s also a good idea to keep an eye on pending and current blocked tasks that cannot be queued for processing. Having too many read or write requests can lead to queue backlogs, increased latencies, potential data loss, and overruns. This is a sign you need to scale up your Apache Cassandra cluster or fine-tune its performance.
• Best Practices for Effective Monitoring

  Best Practices for Effective Monitoring

  To proactively detect and address issues and help your Cassandra database thrive, you need to use effective monitoring techniques and best practices. More specifically, you will want to:

  • Establish baseline performance: Understanding your Apache Cassandra system’s normal behavior is crucial for identifying anomalies, so make sure to track key performance metrics, such as throughput, latency, and disk usage under normal operating conditions. Once you have a baseline, you can quickly detect deviations that may indicate potential problems.
  • Set up continuous monitoring: By regularly monitoring the real-time and historical performance metrics of Cassandra, you can better detect issues before they affect your operations. Focus on read and write latency, throughput, disk usage, compaction tasks, pending and blocked tasks, and node health.
  • Configure alerts: It’s also a good idea to carefully configure real-time alerts so you will receive notifications when key thresholds are crossed. This will allow you to act immediately to prevent potential issues from escalating and impacting your Cassandra cluster’s performance or availability.
  • Perform regular maintenance: Regular maintenance tasks are vital. Not only should you periodically adjust your compaction strategies and replication settings based on your current workload patterns and performance, but you should also monitor and rebalance nodes to ensure data is evenly distributed and remove obsolete data from decommissioned nodes. It’s also important to regularly update and patch Apache Cassandra.
  • Carefully plan capacity: Proper capacity planning is an essential part of ensuring your Apache Cassandra cluster can handle growing workloads without performance degradation or unexpected failures. Monitor your growth trends, set storage thresholds, and plan for horizontal scaling.
• Troubleshooting Common Performance Issues

  Troubleshooting Common Performance Issues

  When Apache Cassandra suffers from poor performance or availability, it’s important to quickly understand the root cause. Common performance issues you may encounter include:

  • High read or write latencies: If you notice high read or write latencies, check your nodes to make sure they aren’t overloaded. Inefficient queries or hardware resources may also be at the root of the problem, so fine-tune your queries and add additional hardware when needed. You should also tune caching settings and optimize your data modeling.
  • Excessive pending compaction tasks: If you have too many pending compaction tasks, take a closer look at your compaction strategy. Make sure it matches your current workload, and you have allocated enough disk space to complete compaction tasks.
  • High disk usage: If you are using a high percentage of your disk, you can monitor the available space, run a nodetool cleanup, or change your compaction settings to free up storage.
  • Node failures or unavailable exceptions: Node failures and unavailable exceptions may point to hardware failures, network issues, and incorrect replication settings.
• What Is an Apache Cassandra Monitoring Tool?

  What Is an Apache Cassandra Monitoring Tool?

  Apache Cassandra monitoring is an extremely involved process that requires collecting, analyzing, and interpreting vast amounts of performance data, including latency, throughput, disk usage, and compaction tasks. However, manually managing these metrics can be overwhelming, especially as your Cassandra cluster grows. To effectively monitor and optimize Cassandra performance, you need a dedicated monitoring tool that can automate data collection, provide real-time visibility, and alert you to potential issues before they impact operations.

  A good Apache Cassandra monitoring tool should simplify performance tracking by offering comprehensive visual dashboards, automated alerts, and historical data analysis. It should help you monitor CPU and memory usage, node availability, read/write performance, and compaction efficiency—all in one place. The tool should also allow customizable alerts, enabling your team to take immediate action whenever a critical threshold is crossed. By using a reliable monitoring tool, you can proactively optimize resources, prevent downtime, and ensure peak database performance.