Monday 3rd July 2017

Dog Fooding with Instametrics

Case Studies


At Instaclustr we also have a big data challenge that we are solving with Apache Cassandra and Apache Spark.  Instametrics provides us with the perfect opportunity to dogfood the Instaclustr technology stack. 

It is probably no surprise to many that at Instaclustr we also have a big data challenge.  As a managed service company we have been closely monitoring thousands of nodes that we have had under management over an extended period of time. We have been collecting an extensive number of metrics on each of these nodes, that we then feed to a monitoring stream engine (Riemann), which in turn alerts our support team as soon as any issue is detected.

So while we obviously want to process these alerts in real-time, we also want to keep recent raw metrics for a period of time. Keeping the detailed data for an initial period of time is important for us two specific reasons. Firstly, we need to keep information for several days so that our support team can perform analysis as required for recent history of a node and cluster, and secondly we also want to store these metrics to provide a rich set of recent data for either display through our dashboard or to expose the metrics through our monitoring API.

However, in addition to our short-term storage needs, it is also necessary for us to calculate and store summary statics for the longer term to provide us with an historic view of our operating environment and to enable us to identify long term trends.  This longer-term view enables us to apply advanced analytic processing to predict failures before they occur and to manage our underlying infrastructure more effectively.

With our Instametrics solution we are building a big data analytics environment to continually improve our service and capability for our customers.


The challenge that we face with this capability is very similar to those faced by many of our customers:

  1. Throughput. Each of our nodes under management generates data for somewhere between one thousand and several thousand different metrics that we capture. Each individual metric is collected every 10 seconds on each individual node. At the current time we are collecting and storing data and then processing and alerting on over 50,000 metric events per second. Naturally, we want to store this stream back in a data store cluster. This represents a very large and sustained number of writes per seconds that few database systems have the ability to ingest with required performance.
  2. Data volume. With more than 50,000 metrics generated per second we need to be capable of writing towards a Terabyte per 24hr period to disk. We need a data-store than can efficiently store this volume in a way that supports slice queries on time range, as this represents our most common read query pattern. We also need a storage engine with built in TTL (Time to Live) for an easier data life-cycle management, ensuring that data gets automatically removed to make way for fresh data.
  3. Data processing. We need much more than just a simple data store. We also need a storage layer that will allow fast and sustained processing in order to calculate summary statistics on all our data, providing another data set that is more suitable for long term storage. Another requirement is that we been to be capable of running complex analytic queries across the whole data set to more efficiently detect unhealthy nodes and clusters.
  4. Availability. Instametrics is mission-critical for us, not only does it provide information and data to our customers about the health of their nodes and clusters, it also provides the backbone for our alerting and monitoring system, which is a key feature of any managed service.


You can call it dogfooding, or you can say that we are a little biased, but the truth is that the power of Apache Spark on Apache Cassandra is the perfect fit for our requirements. Probably one of the most typical use cases for Cassandra is to store times series data, which is exactly what we produce through the flow of metrics from our nodes under management. We opted for the recent DTCS (DateTieredCompactionStrategy), which ensures that data created close in time are stored in the same SSTable. This is particularly useful for Instametrics and our use case as our typical read pattern involves sliced queries on a time range.

To manage the data size we use a TTL of 72 hours. A convenient characteristic of DTCS is that because SSTables contains data of the same age, the SSTable file gets deleted when the data it contains is older than the TTL, which in turn provides a saving on the CPU intensive compaction/tombstone cleanup activities. We have moved our deployment to the recently supported m4.xl-800 node size on AWS. This solution is cost effective and provides ample disk space for us to store the data. This allows us to use a large 12 nodes cluster, which provides us with over 9TB of storage with a total of 48 CPUs and 192GB of memory across the cluster, which in turn is exactly what the distributed analytic engine provided by Apache Spark is looking for.

We use Apache Spark to roll-up the data in five minute, two hours and one day intervals.  This is only the beginning of our plans as we envisage much more extended and deep analytics as our capability continued to grow.

Finally, maybe one of the most interesting features of Cassandra for us is that as our business continues to grow, and the number of nodes under management increases, we will naturally add more nodes to our own cluster to keep the processing capabilities the same.

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