So it happened that we had a production issue on the SaaS version of LogSentinel – our Elasticsearch stopped indexing new data. There was no data loss, as elasticsearch is just a secondary storage, but it caused some issues for our customers (they could not see the real-time data on their dashboards). Below is a post-mortem analysis – what happened, why it happened, how we handled it and how we can prevent it.
Let me start with a background of how the system operates – we accept audit trail entries (logs) through a RESTful API (or syslog), and push them to a Kafka topic. Then the Kafka topic is consumed to store the data in the primary storage (Cassandra) and index it for better visualization and analysis in Elasticsearch. The managed AWS Elasticsearch service was chosen because it saves you all the overhead of cluster management, and as a startup we want to minimize our infrastructure management efforts. That’s a blessing and a curse, as we’ll see below.
We have alerting enabled on many elements, including the Elasticsearch storage space and the number of application errors in the log files. This allows us to respond quickly to issues. So the “high number of application errors” alarm triggered. Indexing was blocked due to
FORBIDDEN/8/index write. We have a system call that enables it, so I tried to run it, but after less than a minute it was blocked again. This meant that our Kafka consumers failed to process the messages, which is fine, as we have a sufficient message retention period in Kafka, so no data can be lost.
I investigated the possible reasons for such a block. And there are two, according to Amazon – increased JVM memory pressure and low disk space. I checked the metrics and everything looked okay – JVM memory pressure was barely reaching 70% (and 75% is the threshold), and there was more than 200GiB free storage. There was only one WARN in the elasticsearch application logs (it was “node failure”, but after that there were no issues reported)
There was another strange aspect of the issue – there were twice as many nodes as configured. This usually happens during upgrades, as AWS is using blue/green deployment for Elasticsearch, but we haven’t done any upgrade recently. These additional nodes usually go away after a short period of time (after the redeployment/upgrade is ready), but they wouldn’t go away in this case.
Being unable to SSH into the actual machine, being unable to unblock the indexing through Elasticsearch means, and being unable to shut down or restart the nodes, I raised a ticket with support. And after a few ours and a few exchanges messages, the problem was clear and resolved.
The main reason for the issue is 2-fold. First, we had a configuration that didn’t reflect the cluster status – we had assumed a bit more nodes and our shared and replica configuration meant we have unassigned replicas (more on shards and replicas here and here). The best practice is to have nodes > number of replicas, so that each node gets one replica (plus the main shard). Having unassigned shard replicas is not bad per se, and there are legitimate cases for it. Our can probably be seen as misconfiguration, but not one with immediate negative effects. We chose those settings in part because it’s not possible to change some settings in AWS after a cluster is created. And opening and closing indexes is not supported.
The second issue is AWS Elasticsearch logic for calculating free storage in their circuit breaker that blocks indexing. So even though there were 200+ GiB free space on each of the existing nodes, AWS Elasticsearch thought we were out of space and blocked indexing. There was no way for us to see that, as we only see the available storage, not what AWS thinks is available. So, the calculation gets the total number of shards+replicas and multiplies it by the per-shared storage. Which means unassigned replicas that do not take actual space are calculated as if they take up space. That logic is counterintuitive (if not plain wrong), and there is hardly a way to predict it.
This logic appears to be triggered when blue/green deployment occurs – so in normal operation the actual remaining storage space is checked, but during upgrades, the shard-based check is triggered. That has blocked the entire cluster. But what triggered the blue/green deployment process?
We occasionally need access to Kibana, and because of our strict security rules it is not accessible to anyone by default. So we temporarily change the access policy to allow access from our office IP(s). This change is not expected to trigger a new deployment, and has never lead to that. AWS documentation, however, states:
In most cases, the following operations do not cause blue/green deployments: Changing access policy, Changing the automated snapshot hour, If your domain has dedicated master nodes, changing data instance count.
There are some exceptions. For example, if you haven’t reconfigured your domain since the launch of three Availability Zone support, Amazon ES might perform a one-time blue/green deployment to redistribute your dedicated master nodes across Availability Zones.
There are other exceptions, apparently, and one of them happened to us. That lead to the blue/green deployment, which in turn, because of our flawed configuration, triggered the index block based on the odd logic to assume unassigned replicas as taking up storage space.
How we fixed it – we recreated the index with fewer replicas and started a reindex (it takes data from the primary source and indexes it in batches). That reduced the size taken and AWS manually intervened to “unstuck” the blue/green deployment. Once the problem was known, the fix was easy (and we have to recreate the index anyway due to other index configuration changes).
As I said in the beginning, this did not mean there’s data loss because we have Kafka keep the messages for a sufficient amount of time. However, once the index was writable, we expected the consumer to continue from the last successful message – we have specifically written transactional behaviour that committed the offsets only after successful storing in the primary storage and successful indexing. Unfortunately, the kafka client we are using had auto-commit turned on that we have overlooked. So the consumer has skipped past the failed messages. They are still in Kafka and we are processing them with a separate tool, but that showed us that our assumption was wrong and the fact that the code calls “commit”, this doesn’t actually mean something.
So, the morals of the story:
- Monitor everything. Bad things happen, it’s good to learn about them quickly.
- Check your production configuration and make sure it’s adequate to the current needs. Be it replicas, JVM sizes, disk space, number of retries, auto-scaling rules, etc.
- Be careful with managed cloud services. They save a lot of effort but also take control away from you. And they may have issues for which your only choice is contacting support.
- If providing managed services, make sure you show enough information about potential edge cases. An error console, an activity console, or something, that would allow the customer to know what is happening.
- Validate your assumptions about default settings of your libraries. (Ideally, libraries should warn you if you are doing something not expected in the current state of configuration)
- Make sure your application is fault-tolerant, i.e. that failure in one component doesn’t stop the world and doesn’t lead to data loss.
So overall, a rare event unexpectedly triggered a blue/green deployment, where a combination of flawed configuration and flawed free space calculation resulted in an unwritable cluster. Fortunately, no data is lost and at least I learned something.
The post An AWS Elasticsearch Post-Mortem appeared first on Bozho's tech blog.