I am running Cassandra as a Kubernetes pod . One pod is having one Cassandra container.we are running Cassandra of version 3.11.4 and auto_bootstrap set to true.I am having 5 node in production and it holds 20GB data.
Because of some maintenance activity and if I restart any Cassandra pod it is taking 30 min for bootstrap then it is coming UP and Normal state.In production 30 min is a huge time.
How can I reduce the bootup time for cassandra pod ?
Thank you !!
If you're restarting the existing node, and data is still there, then it's not a bootstrap of the node - it's just restart.
One of the potential problems that you have is that you're not draining the node before restart, and all commit logs need to be replayed on the start, and this can take a lot of time if you have a lot of data in commit log (you can just check system.log on what Cassandra is doing at that time). So the solution could be is to execute nodetool drain before stopping the node.
If the node is restarted before crash or something like, you can thing in the direction of the regular flush of the data from memtable, for example via nodetool flush, or configuring tables with periodic flush via memtable_flush_period_in_ms option on the most busy tables. But be careful with that approach as it may create a lot of small SSTables, and this will add more load on compaction process.
Related
URGENT_MESSAGES-[no-channel] dropping message of type GOSSIP_DIGEST_SYN whose timeout expired before reaching the network
Thank you for your message. Yesterday we solved the Problem.
The reason was a "dead node" oviously leaved form a change in the kubernetes deployment.
So, allways look out for dead nodes, after changing something in the cluster deployment.
You didn't provide a lot of information but I'm assuming that your cluster is running into a known issue where gossip messages are being dropped during startup of a Cassandra node (CASSANDRA-16877).
The starting node sends GOSSIP_DIGEST_SYN with a high priority (URGENT_MESSAGES) but for large clusters, Cassandra 4.0 nodes cannot serialise the gossip state when the size of the state exceeds 128kb and no acknowledgement gets sent. Since a node can not gossip with other nodes, it fails to start.
This was urgently fixed in Cassandra 4.0.1 last year. Upgrade the binaries on the affected Cassandra 4.0 nodes and that should allow them to start successfully and join the ring. Cheers!
I deployed gridgain cluster in google kubernetes cluster following[1]. I enabled native persistency using statefulset. In statefulset.yaml in [2] terminationGracePeriodSeconds set to 60000. What is the purpose of this large timeout?
When deleting pod using kubectl delete pod command it take very large time. What is the suitable value for terminationGracePeriodSeconds without loss any data.
[1]. https://www.gridgain.com/docs/latest/installation-guide/kubernetes/gke-deployment
[2]. https://www.gridgain.com/docs/latest/installation-guide/kubernetes/gke-deployment#creating-pod-configuration
I believe the reason behind setting it to 60000 was - do not rely on it. Prior to Ignite 2.9 there was an issue with the startup script that didn't bypass SYS SIGNAL to the underlying Java app, making it impossible to perform a graceful shutdown.
If a node is being restarted gracefully and IGNITE_WAIT_FOR_BACKUPS_ON_SHUTDOWN is enabled, Ignite will ensure that the node leave won't lead to a data loss. Sometimes a rebalance might take a while.
Keeping the above in mind: the hang issue might happen for Apache Ignite 2.8 and below, keeping the recommended terminationGracePeriodSeconds should be fine and never be used in practice (in a normal flow).
According to both of these Link1 and Link2, my Airflow DAG run is returning the error INFO - Task exited with return code -9 due to an out-of-memory issue. My DAG run has 10 tasks/operators, and each task simply:
makes a query to get one of my BigQuery tables, and
writes the results to a collection in my Mongo database.
The size of the 10 BigQuery tables range from 1MB to 400MB, and the total size of all 10 tables is ~1GB. My docker container has default 2GB of memory and I've increased this to 4GB, however I am still receiving this error from a few of the tasks. I am confused about this, as 4GB should be plenty of memory for this. I am also concerned because, in the future, these tables may become larger (a single table query could be 1-2GB), and I'd like to avoid these return code -9 errors at that time.
I'm not quite sure how to handle this issue, since the point of the DAG is to transfer data from BigQuery to Mongo daily, and the queries / data in-memory for the DAG's tasks is necessarily fairly large then, based on the size of the tables.
As you said, the error message you get corresponds to an out of memory issue.
Referring to the official documentation:
DAG execution is RAM limited. Each task execution starts with two
Airflow processes: task execution and monitoring. Currently, each node
can take up to 6 concurrent tasks. More memory can be consumed,
depending on the size of the DAG.
High memory pressure in any of the GKE nodes will lead the Kubernetes scheduler to evict pods from nodes in an attempt to relieve that pressure. While many different Airflow components are running within GKE, most don't tend to use much memory, so the case that happens most frequently is that a user uploaded a resource-intensive DAG. The Airflow workers run those DAGs, run out of resources, and then get evicted.
You can check it with following steps:
In the Cloud Console, navigate to Kubernetes Engine -> Workloads
Click on airflow-worker, and look under Managed pods
If there are pods that show Evicted, click each evicted pod and look for the The node was low on resource: memory message at the top of the window.
What are the possible ways to fix OOM issue?
Create a new Cloud Composer environment with a larger machine type than the current machine type.
Ensure that the tasks in the DAG are idempotent, which means that the result of running the same DAG run multiple times should be the same as the result of running it once.
Configure task retries by setting the number of retries on the task - this way when your task gets -9'ed by the scheduler it will go to up_for_retry instead of failed
Additionally you can check the behavior of CPU:
In the Cloud Console, navigate to Kubernetes Engine -> Clusters
Locate Node Pools at the bottom of the page, and expand the default-pool section
Click the link listed under Instance groups
Switch to the Monitoring tab, where you can find CPU utilization
Ideally, the GCE instances shouldn't be running over 70% CPU at all times, or the Composer environment may become unstable during resource usage.
I hope you find the above pieces of information useful.
I am going to chunk the data so that less is loaded into any 1 task at any given time. I'm not sure yet whether I will need to use GCS/S3 for intermediary storage.
I have a python code where I process some data, write neo4j queries and then commit these queries to neo4j. When I run the code on my local machine and write the output to local neo4j it doesn't take more than 15 minutes. However, when I run my code locally and write the output to noe4j pod in k8s pod it takes double the time, and when I build my code and deploy it to k8s and run that pod and write the output to neo4j pod it takes a round 3 hours. since I'm new to k8s deployment it might something in the pod configurations or settings, so I appreciate if I can get some hints
There could be few reasons of that.
I would first check how much resources does your pod consume while you are processing data, you can do that using kubectl top pod.
Second I would check if there are any limits inside pod. You can read a great deal about them on Managing Compute Resources for Containers.
If you have a limit set then it might be too low and that's causing the extended time while processing data.
If limits are not set then it might be because of how you installed minik8s. I think as default it's being installed with 4G is memory, you can look at alternative methods of installing minik8s. With multipass you can specify more memory to allocate.
There also can be a issue with Page Cache Sizing, Heap Sizing or number of open files. Please read the Neo4j Performance Tuning.
We had lately several times the same problems on Google compute engine environment with PostgreSQL streaming replication and I would like to understand reasons and if I can repair it in some smoother way.
From time to time we see some communication problems in Google's internal network in GCE datacenter and they always trigger replication lags between our PG master and its replicas. All machines are Debian-8 and PostgreSQL 9.5.
When situation happens everything seems to be OK - no errors in PG logs on master or replicas just communication between master and replicas seems to be incredibly slow or repeatedly failing so new WAL logs are transfered to replicas with big delays and therefore replication lag is still growing.
Restart of replication from within PostgreSQl or restart of PostgreSQL on replica does not really help - after several WAL logs copied using scp in recovery command communication is back in previous incredibly slow status. Only restart of the whole instance help. When whole VM is restarted communication is back to normal and recovery even from lag many hours long is done in a few minutes. So main reason for this behavior seems to be on OS level. I tried to check net traffic but without finding anything significant. I also do not see anything relevant in any OS log.
Could restart of some OS service help? So I do not need to restart the whole VM? Thank you very much for any ideas.