Another factor when choosing disk space is the compaction strategy. This strategy is well known for wasting disk space. Considering that it is mandatory to use local and ultra-fast disks i. For this reason, it is essential to reduce the overhead, and the only way to do so is by using a different compaction strategy.
Date-Tiered Compaction in Apache Cassandra
When the data to be stored in Cassandra is based on time series metrics, meaning immutable entries based on timestamps to be stored on tables, the best approach is to use TWCS Time Window Compaction Strategy. The overhead when using this strategy can be as big as each time-windowed chunk. However, it is important to keep in mind the time-series constraint: the data has to be immutable. Data should only be evicted by TTL. Fortunately, we can consider the data stored by OpenNMS through Newts time-series data to use this strategy.
Here is a way to configure this strategy:.
Class AbstractCompactionStrategy
The reason why choosing seven days is the following: for a one-year retention, the number of compacted chunks will be 52 as there are 52 weeks in a year. This is a little bit higher than the recommended number of chunks, but in practice, this is reasonable, especially to simplify the calculations. For different retentions, try to target around 40 chunks or less.
To have the whole picture in mind, the following is how the entire keyspace for Newts would look:. Use cqlsh to create the keyspace. When configuring OpenNMS, the administrator should choose one value to use as the retention for every single metric collected on the system. Unlike with RRDtool, it is impossible to have different retention values for different metrics, meaning that if a given customer wants different retentions, the customer in question would have to configure an OpenNMS server with a dedicated Newts keyspace for each TTL.
With this schema in place, Grafana is the only way to have a unified view of all the metrics.
As mentioned, retention is the amount of time a given metric will exist on the Cassandra cluster. That means, when this time expires, the data will be evicted from the keyspace during compaction. Every environment is different. However, there are ways to estimate the effective metric size on average that a given row of the newts.
I used the stress tool described later to have SSTables populated on disk on a test cluster to figure out that value. I used the following script to analyze the SSTables using sstablemetadata a tool available after installing the cassandra-tools package :. As you can see, the average size with the default compression settings is 12 Bytes. Also, you can see the compression rate of the data. That is why it is better to assume a bigger value to compensate for what the calculations are ignoring and perhaps increase the disk overhead to avoid running out of disk space.
Knowing the number of metrics to be persisted a. Each installation is different, but on average, we can consider that the size of a single metric i.
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Unfortunately, it is tough to estimate what the real size would be due to how dynamic the non-deterministic elements of the samples table could be and the fact that SSTables might contain compressed data with different settings without taking into consideration the metadata and other files that would exist along with the main SSTables files that hold the data. That means the actual value can be less or even higher, but I believe it is better to over-provision disks rather than be at risk of running out of disk space.
Choose the number of nodes to calculate the required available bytes per node; or vice versa, choose the disk size to calculate the expected number of nodes. In general, for Cassandra, we recommend never to use a disk greater than 4TB.
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Calculate the number of nodes like this:. With the above assumptions, we need an approximately eight nodes cluster with 3TB of disk space per instance, using TWCS with a replication factor of two, to persist 35 million metrics every 5 min. In this case, the total amount of metrics to be collected is the injection rate multiplied by the collection interval, which is another way to obtain the number of metrics. As shown, the assumption includes knowing the number of metrics to be collected.
When this is not known, an assumption on the number of nodes has to be made to estimate the total amount of metrics the cluster will handle. Cassandra grows linearly. That means, to have four times that capacity a.
We do not recommend increasing the disk size per Cassandra node as this is an anti-pattern, so the obvious variable to increase is the number of nodes within the cluster. If we increase from 8 to 36, keeping the same assumptions, we cover the new requirement. With that number of nodes, we should increase the replication factor to have more room for potential outages.
If that parameter changes, more nodes will be required, easily inferred from the formulas. At this point, we know that we should configure in OpenNMS the following parameters at a minimum:. One thing to keep in mind is that it is possible to end with the same injection rate with a different combination of resources and groups, impacting the ring buffer.
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Actions Shares. No notes for slide. Are we lazy, or crazy to talk about a five-year-old technology 5. Background Design Principles and Basic Concepts 9. What is overlap? Basic Design Principles cont. Going from highest level, any level having score higher than 1. How it affects you? Best Practices Myth Busters Observations that often confuse people But this falls apart spectacularly once you get behind. LevelDB's way around this is to simply block writes if L0 compaction falls behind.
We don't have that luxury.
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This isn't a magic wand -- if you are consistently writing too fast for LCS to keep up, you're still screwed. But if instead you have intermittent bursts of activity, it can help a lot. The best part of incremental backup is that the hard link is created only for newly created file due to the flushing of in-memory data contents. Files created due to compaction are not hard-linked.
Incremental backup in combination with snapshots is another rudimentary way to take Cassandra point-in-time backups. Periodically, data is backed up from the snapshot, while incremental backup-based files are used for point-in-time needs. This means periodically the incremental backup-based files, which are already covered via snapshot-based backup, must be deleted. This method is similar to incremental backup. Periodical snapshot-based backup provides the bulk of backup data, while the archived commit-log is used for the point-in-time backup.
Overall, Cassandra provides multiple options for backup.
TWCS part 1 - how does it work and when should you use it ?
If point-in-time restore is not a requirement, simple snapshot-based backup is much easier to implement, though it is not a true backup and will result in heavy repairs. Most of these options require significant manual effort and are error prone. For mission-critical, always-on applications where minimizing data loss and operational resiliency are important factors, administrators should consider leveraging next-generation data protection products from companies that specialize in this area. Related Posts.