I need you help regarding to Telegraf monitoring of influxDB instance and a behavior I cannot explain.
The configuration is the following:
Two independent instances of InfluxDB v1.7.10 are running on seperate servers, say server A and server B
Two telegraf services v1.13.4 are running with the same configuration:
One output being a "monitoring" database created in the influx database
Several inputs (system, disk, ping, ...)
Grafana is used on both server to explore Telegraf stored values
On server A, which is running fine, the monitoring shard size and cardinality are quite regular. On server B on the other hand, the monitoring shard size and cardinality are much more important (by a factor 10).
I cannot explain this difference and I have already checked:
tag and field cardinality of the inputs used on both server
telegraf configuration on both server
Any idea about where to look to explain this behavior ?
Tanks for your help !
Related
we are using Telegraf, Influxdb and Grafana for Monitoring our environment. We have two datacenters dc1 and dc2. Each datacenter has one pod of Influxdb running. we want some approach to replicate the data between two influxdb instances running across two datacenters. So, if dc1 goes down we can have the data of both datacenters(dc1 and dc2) in dc2. We are using opensource Influxdb so can anyone please suggest some approaches to achieve this?
Tried to follow Replication during ingest approach where we configure two influxdb urls of both datacenters in telegraf.conf as per this https://www.influxdata.com/blog/multiple-data-center-replication-influxdb/ documentation but, what if one of the influxdb is down? and also after it's recovery both influxdb will have different data so, we do not want to follow this approach.
I have a minor bosun setup, and its collecting metrics from numerous services, and we are planning to scale these services on the cloud.
This will mean more data coming into bosun and hence, the load/efficiency/scale of bosun is affected.
I am afraid of losing data, due to network overhead, and in case of failures.
I am looking for any performance benchmark reports for bosun, or any inputs on benchmarking/testing bosun for scale and HA.
Also, any inputs on good practices to be followed to scale bosun will be helpful.
My current thinking is to run numerous bosun binaries as a cluster, backed by a distributed opentsdb setup.
Also, I am thinking is it worthwhile to run some bosun executors as plain 'collectors' of scollector data (with bosun -n command), and some to just calculate the alerts.
The problem with this approach is it that same alerts might be triggered from multiple bosun instances (running without option -n). Is there a better way to de-duplicate the alerts?
The current best practices are:
Use https://godoc.org/bosun.org/cmd/tsdbrelay to forward metrics to opentsdb. This gets the bosun binary out of the "critical path". It should also forward the metrics to bosun for indexing, and can duplicate the metric stream to multiple data centers for DR/Backups.
Make sure your hadoop/opentsdb cluster has at least 5 nodes. You can't do live maintenance on a 3 node cluster, and hadoop usually runs on a dozen or more nodes. We use Cloudera Manager to manage the hadoop cluster, and others have recommended Apache Ambari.
Use a load balancer like HAProxy to split the /api/put write traffic across multiple instances of tsdbrelay in an active/passive mode. We run one instance on each node (with tsdbrelay forwarding to the local opentsdb instance) and direct all write traffic at a primary write node (with multiple secondary/backup nodes).
Split the /api/query traffic across the remaining nodes pointed directly at opentsdb (no need to go thru the relay) in an active/active mode (aka round robin or hash based routing). This improves query performance by balancing them across the non-write nodes.
We only run a single bosun instance in each datacenter, with the DR site using the read only flag (any failover would be manual). It really isn't designed for HA yet, but in the future may allow two nodes to share a redis instance and allow active/active or active/passive HA.
By using tsdbrelay to duplicate the metric streams you don't have to deal with opentsdb/hbase replication and instead can setup multiple isolated monitoring systems in each datacenter and duplicate the metrics to whichever sites are appropriate. We have a primary and a DR site, and choose to duplicate all metrics to both data centers. I actually use the DR site daily for Grafana queries since it is closer to where I live.
You can find more details about production setups at http://bosun.org/resources including copies of all of the haproxy/tsdbrelay/etc configuration files we use at Stack Overflow.
I'm going to use influxdb to store a lot of iot data from sensors.
As the last cluster version of influxdbv0.11 is not ready to use in production, and the Relay HA is too young too, is there another way to scale-out influxdb?
eg:
What are the maturity of the last cluster version of influxdb v0.11? Should I customize v0.11 or try other cost-saving way.
How about use kafka infront of influxdb to buffer data when influxdb got down?
How about sharding?Is there any detailed document about sharding in influxdb( https://influxdata.com/high-availability/)?
Any way, I just want to find a free, cluster working influxdb.
Other than InfluxDB Relay there isn't a free way to scale out InfluxDB.
Sorry I just started to learn docker. My question may seem stupid for some of you.
In fact, I would like to know if there is a way to collect performance metrics from "CAdvisor" container (not from cgroup) at runtime ? I mean, extract performance values from the curves designed by cadvisor like memory usage or network traffic.
I need to record this values and save them in a database so that, I can perform a statistic analyzes upon these generated values (like comparing memory consumption for two docker containers at t=50s).
Thanks in advance.
As other answers mention, cAdvisor doesn't provide its own performance data API, instead it exposes metrics which are typically handled in a separate database if one wants to derive performance data beyond "real time". For example, cAdvisor exports Prometheus metrics natively:
http://prometheus.io/docs/instrumenting/exporters/
The Prometheus metric types:
http://prometheus.io/docs/concepts/metric_types/
Prometheus supports a fairly rich functional expression language that can be used for querying and visualization:
http://prometheus.io/docs/querying/basics/
cAdvisor does provide a rest endpoint to get any stats in real time. By default, it keeps latest two minute of data. You can configure it to keep more or less. It also supports a storage backend to keep dumping stats to an influxdb database.
REST Api:
eg. /api/v1.3/containers
doc: https://github.com/google/cadvisor/blob/master/docs/api.md
Doc on setting up InfluxDB:
https://github.com/google/cadvisor/blob/master/docs/influxdb.md
I think you could use https://github.com/tutumcloud/container-metrics for this. Basically what that would be doing is using influxdb http://influxdb.com/ as a time series data store.
There is some more information available here: http://blog.tutum.co/2014/08/25/panamax-docker-application-template-with-cadvisor-elasticsearch-grafana-and-influxdb/
A couple of people seemed to be looking into the ELK stack (Elastic Search, Logstash, Kibana) for visualising some of this data here: https://github.com/google/cadvisor/issues/634
I am thinking about the best strategy to scale with a cluster of servers. I know there is no hard and fast rules, but I am curious what people think about these scenarios:
cluster of combination app/db servers that are round robin (with failover) balanced using dnsmadeeasy. the db's are synced using replication. Has the advantage that capacity can be augmented easily by adding another server to the cluster, and it is naturally failsafe.
cluster of app servers, again round robin load balanced (with failover) using dnsmadeeasy, all reporting to a big DB server in the back. easy to add app servers, but the single db server creates a single failure point. Could possible add a hot standby with replication.
cluster of app servers (as above) using two databases, one handling reads only, and one handling writes only.
Also, if you have additional ideas, please make suggestions. The data is mostly denormalized and non relational, and the DBs are 50/50 read-write.
Take 2 physical machines and make them Xen servers
A. Xen Base alpha
B. Xen Base beta
In each one do three virtual machines:
"web" server for statics(css,jpg,js...) + load balanced proxy for dynamic request (apache+mod-proxy-balancer,nginx+fair)
"app" server (mongrel,thin,passenger) for dynamic requests
"db" server (mySQL, PostgreSQL...)
Then your distribution of functions can be like this:
A1 owns your public ip and handle requests to A2 and B2
B1 pings A1 and takes over if ping fails
A2 and B2 take dynamic request querying A3 for data
A3 is your dedicated data server
B3 backups A3 second to second and offer readonly access to make copies, backups etc.
B3 pings A3 and become master if A3 becomes unreachable
Hope this can help you some way, or at least give you some ideas.
It really depends on your application.
I've spent a bit of time with various techniques for my company and what we've settled on (for now) is to run a reverse proxy/loadbalancer in front of a cluster of web servers that all point to a single master DB. Ideally, we'd like a solution where the DB is setup in a master/slave config and we can promote the slave to master if there are any issues.
So option 2, but with a slave DB. Also for high availability, two reverse proxies that are DNS round robin would be good. I recommend using a load balancer that has a "fair" algorithm instead of simple round robin; you will get better throughput.
There are even solutions to load balance your DB but those can get somewhat complicated and I would avoid them until you need it.
Rightscale has some good documentation about this sort of stuff available here: http://wiki.rightscale.com/
They provide these types of services for the cloud hosting solutions.
Particularly useful I think are these two entries with the pictures to give you a nice visual representation.
The "simple" setup:
http://wiki.rightscale.com/1._Tutorials/02-AWS/02-Website_Edition/2._Deployment_Setup
The "advanced" setup:
http://wiki.rightscale.com/1._Tutorials/02-AWS/02-Website_Edition/How_do_I_set_up_Autoscaling%3f
I'm only going to comment on the database side:
With a normal RDBMS a 50/50 read/write load for the DB will make replication "expensive" in terms of overhead. For almost all cases having a simple failover solution is less costly than implementing a replicating active/active DB setup. Both in terms of administration/maintenance and licensing cost (if applicable).
Since your data is "mostly denormalized and non relational" you could take a look at HBase which is an OSS implementation of Google Bigtable, a column based key/value database system. HBase again is built on top of Hadoop which is an OSS implementation of Google GFS.
Which solution to go with depends on your expected capacity growth where Hadoop is meant to scale to potentially 1000s of nodes, but should run on a lot less as well.
I've managed active/active replicated DBs, single-write/many-read DBs and simple failover clusters. Going beyond a simple failover cluster opens up a new dimension of potential issues you'll never see in a failover setup.
If you are going for a traditional SQL RDBMS I would suggest a relatively "big iron" server with lots of memory and make it a failover cluster. If your write ratio shrinks you could go with a failover write cluster and a farm of read-only servers.
The answer lies in the details. Is your application CPU or I/O bound? Will you require terabytes of storage or only a few GB?