Data Locality: Bring your data close to compute. Make your data local to compute workloads for Spark caching, Presto caching, Hive caching and more. Data Accessibility: Make your data accessible. No matter if it sits on-prem or in the cloud, HDFS or S3, make your files and objects accessible in many different ways. Data On-Demand: Make your data as elastic as compute. Effortlessly orchestrate your data for compute in any cloud, even if data is spread across multiple clouds.
Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Slurm requires no kernel modifications for its operation and is relatively self-contained. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.
As we have some business requirements about data aggregation and online processing, so we did a quick PoC on Apache Druid. Next I will show how to build druid quickly and start your ingestion task.
1.Select release version which is compatible to your existing system and download the package.
2.Choose what kind of druid service you want to start with
For single node, just execute the script under bin directory which is start with start-single-server-, or you can execute start-micro-quickstart
For multiple node cluster, please update the configuration files under start-micro-quickstart in one node and sync to other nodes. If you want to connect to your hadoop cluster, please copy corresponding hadoop xml files and kerberos keytab under druid.
Then you start druid service in every node by execute start-cluster script.
Cloud platforms provide a wealth of benefits for the organizations that use them. However, there’s no denying that adopting the cloud can put strains on DevOps teams. Developers must use microservices to architect for portability, meanwhile operators are managing extremely large hybrid and multi-cloud deployments. Istio lets you connect, secure, control, and observe services.
First, download Istio release version, unzip the package and enter the directory.
Second, verify installation environment
bin/istioctl verify-install
Next, deploy Istio and select the demo profile which enable many features like tracing/kiali/grafana
bin/istioctl manifest apply --set profile=demo
Then, check Istio pods’ status, make sure all the related pods are running
Istio Commands
authn: Interact with Istio authentication policies
authz: (authz is experimental. Use istioctl experimental authz)
convert-ingress: Convert Ingress configuration into Istio VirtualService configuration
Rencetly I found some k8s nodes became “NotReady”, I checked disk and memory, they both seems fine.
[xxx@xxx-xxx ~]# kubectl describe node xxx-xxx
...
...
Conditions:
Type Status LastHeartbeatTime LastTransitionTime Reason Message
---- ------ ----------------- ------------------ ------ -------
...
PIDPressure False Fri, 10 May 2019 09:24:43 +0900 Fri, 10 May 2018 00:10:12 +0900 KubeletHasSufficientPID kubelet has sufficient PID available
...
Then I restarted kubelet on server and checked logs, I found:
[xxx@xxx-xxx ~]# systemctl status kubelet
● kubelet.service - Kubernetes Kubelet Server
Loaded: loaded (/etc/systemd/system/kubelet.service; enabled; vendor preset: disabled)
...
May 10 12:30:30 xxx-xxx kubelet[16776]: F0322 12:30:30.810434 16776 server.go:233] failed to run Kubelet: Running with swap on is not supported, plea...
...
So I checked server’s status and turn off swap, then I restarted kubelet and the nodes went well.
[xxx@xxx-xxx ~]# swapoff -a
[xxx@xxx-xxx ~]# systemctl restart kubelet
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