I have a running application on a Linux EC-2 instance and I would like to set the CloudWatch Agent.
I would like to know what are the CloudWatch Agent using CPU/Memory/Disk to what extent in order to collect the information.
Its a minor concern but still would like to know if Agent will affect the instance performance (Is it a minimal impact?).
Thanks in advance!
Golan
Anything that runs on a computer would impact performance. It is always a trade-off between running some code and the benefit that the code provides.
The Agent only collects data at regular intervals, so it should not have a large impact on the system.
I suggest you install CloudWatch Agent and measure the impact yourself.
Related
We're running Matillion (v1.54) on an AWS EC2 instance (CentOS), based on Tomcat 8.5.
We have developped a few ETL jobs by now, and their execution takes quite a lot of time (that is, up to hours). We'd like to speed up the execution of our jobs, and I wonder how to identify the bottle neck.
What confuses me is that both the m5.2xlarge EC2 instance (8 vCPU, 32G RAM) and the database (Snowflake) don't get very busy and seem to be sort of idle most of the time (regarding CPU and RAM usage as shown by top).
Our environment is configured to use up to 16 parallel connections.
We also added JVM options -Xms20g -Xmx30g to /etc/sysconfig/tomcat8 to make sure the JVM gets enough RAM allocated.
Our Matillion jobs do transformations and loads into a lot of tables, most of which can (and should) be done in parallel. Still we see, that most of the tasks are processed in sequence.
How can we enhance this?
By default there is only one JDBC connection to Snowflake, so your transformation jobs might be getting forced serial for that reason.
You could try bumping up the number of concurrent connections under the Edit Environment dialog, like this:
There is more information here about concurrent connections.
If you do that, a couple of things to avoid are:
Transactions (begin, commit etc) will force transformation jobs to
run in serial again
If you have a parameterized transformation job,
only one instance of it can ever be running at a time. More information on that subject is here
Because the Matillion server is just generating SQL statements and running them in Snowflake, the Matillion server is not likely to be the bottleneck. You should make sure that your orchestration jobs are submitting everything to Snowflake at the same time and there are no dependencies (unless required) built into your flow.
These steps will be done in sequence:
These steps will be done in parallel (and will depend on Snowflake warehouse size to scale):
Also - try the Alter Warehouse Component with a higher concurrency level
I wish to automate runs of SQL (DML's and DML's) into the AWS redshift cluster, i.e. as soon as someone merge the SQL file into S3 bucket it should run in the configured environment say dev, preprod & prod.
Is there any way I can do this?
My investigation says that AWS codepipeline is one of the solution however, I am not sure how I will connect to the Redshift database in Codepipeline?
Another way is using Lambda function but it has its limitation of 5 minutes I guess and some of the DDL/DML might take more than 5 minutes to run.
Regards,
Shay
There are a lot of choices out there and which is best will depend on many factors including your team's skillset and your budget. I'll let the community weigh in on all the possibilities.
I would like to advise on using the AWS serverless ecosystem to perform these functions. First off the Lambda limit is now 15 min but this really isn't important. The most important development is Redshift Data API which lets you start queries in a Lambda and for other Lambdas check on completion later. See: https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html
With Redshift Data API for fire-and-forget access to Redshift and Step Functions to orchestrate the Lambda functions you can create a low cost, light weight infrastructure to perform all sort of integrations and actions. These can include triggering other tools / services as needed for you. This is not the best approach in all cases but Lambda based solutions should not be excluded due to run time limits.
Complete newbie to PigLatin, but looking to pull data from the MetOffice DataPoint API e.g.:
http://datapoint.metoffice.gov.uk/public/data/val/wxfcs/all/xml/350509?res=3hourly&key=abc123....
...into Hadoop.
My question is "Can this be undertaken using PigLatin (from within Pig View, in Ambari)"?
I've hunted round for how to format a GET request into the code, but without luck.
Am I barking up the wrong tree? Should I be looking to use a different service within the Hadoop framework to accomplish this?
It is very bad idea to make calls to external services from inside of map-reduce jobs. The reason being that when running on the cluster your jobs are very scalable whereas the external system might not be so. Modern resource managers like YARN make this situation even worse, when you swamp external system with the requests your tasks on the cluster will be mostly sleeping waiting for reply from the server. The resource manager will see that CPU is not being used by tasks and will schedule more of your tasks to run which will make even more requests to the external system, swamping it with the requests even more. I've seen modest 100 machine cluster putting out 100K requests per second.
What you really want to do is to either somehow get the bulk data from the web service or setup a system with a queue and few controlled number of workers that will pull from the external system at set rate.
As for your original question, I don't think PigLatin provides such service, but it could be easily done with UDFs either Python or Java. With Python you can use excellent requests library, which will make your UDF be about 6 lines of code. Java UDF will be little bit more verbose, but nothing terrible by Java standards.
"Can this be undertaken using PigLatin (from within Pig View, in
Ambari)"?
No, by default Pig load from HDFS storage, unless you write your own loader.
And i share same point with #Vlad, that this is not a good idea, you have many other other components used for data ingestion, but this not a use case of Pig !
I've been trying to find out ways to improve our nservicebus code performance. I searched and stumbled on these profiles that you can set upon running/installing the nservicebus host.
Currently we're running the nservicebus host as-is, and I read that by default we are using the "Lite" version of the available profiles. I've also learnt from this link:
http://docs.particular.net/nservicebus/hosting/nservicebus-host/profiles
that there are Integrated and Production profiles. The documentation does not say much - has anyone tried the Production profiles and noticed an improvement in nservicebus performance? Specifically affecting the speed in consuming messages from the queues?
One major difference between the NSB profiles is how they handle storage of subscriptions.
The lite, integration and production profiles allow NSB to configure how reliable it is. For example, the lite profile uses in-memory subscription storage for all pub/sub registrations. This is a concern because in order to register a subscriber in the lite profile, the publisher has to already be running (so the publisher can store the subscriber list in memory). What this means is that if the publisher crashes for any reason (or is taken offline), all the subscription information is lost (until each subscriber is restarted).
So, the lite profile is good if you are running on a developer machine and want to quickly test how your services interact. However, it is just not suitable to other environments.
The integration profile stores subscription information on a local queue. This can be good for simple environments (like QA etc.). However, in a highly distributed environment holding the subscription information in a database is best, hence the production profile.
So, to answer your question, I don't think that by changing profiles you will see a performance gain. If anything, changing from the lite profile to one of the other profiles is likely to decrease performance (because you incur the cost of accessing queue or database storage).
Unless you tuned the logging yourself, we've seen large improvements based on reduced logging. The performance from reading off the queues is same all around. Since the queues are local, you won't gain much from the transport. I would take a look at tuning your handlers and the underlying infrastructure. You may want to check out tuning MSMQ and look at the disk you are using etc. Another spot would be to look at how distributed transactions are working assuming you are using a remote database that requires them.
Another option to increase processing time is to increase the number of threads consuming the queue. This will require a license. If a license is not an option you can have multiple instances of a single threaded endpoint running. This requires you shard your work based on message type or something else.
Continuing up the scale you can then get into using the Distributor to load balance work. Again this will require a license, but you'll be able to add more nodes as necessary. All of the opportunities above also apply to this topology.
There's alot of information out there on setting up LAMP stacks on a single box, or perhaps moving MySQL onto it's own box, but growing beyond that doesn't seem to be very well documented.
My current web environment is having capacity issues, and so I'm looking for best-practices regarding configuration tuning, determining bottlenecks, security, etc.
I presently host around 400 sites, with a fair need for redundany and security, and so I've grown beyond the single-box solution - but am not at the level of a full ISP or dedicated web-hosting company.
Can anyone point me in the direction of some good expertise on setting up a great apache web-farm with a view to security and future expansion?
My web environment consists of 2 redundant MySQL servers, 2 redundant web-content servers, 2 load balancing front-end apache servers that mount the content via nfs and share apache config and sessions directories between them, and a single "developer's" server which also mounts the web-content via nfs, and contains all the developer accounts.
I'm pretty happy with alot of this setup, but it seems to be choking on the load prematurely.
Thanks!!
--UPDATE--
Turns out the "choking on the load" is related to mod_log_sql, which I use to send my apache logs to a mysql database. By re-configuring the webservers to write their sql statements to a disk file, and then creating a separate process to submit those to the database it allows the webservers to free up their threads much quicker, and handle a much greater load.
You need to be able to identify bottlenecks and test improvements.
To identify bottlenecks, you need to use your system's reporting tools. Some examples:
MySQL has a slow query log.
Linux provides stats like load average, iostat, vmstat, netstat, etc.
Apache has the access log and the server-status page.
Programming languages have profilers, like Pear Benchmark.
Use these tools to identifyy the slowest/biggest offenders and concentrate on them. Try an improvement and measure to see if it actually improves performance.
This becomes a never ending loop for two reasons: there's always something in a complex system that can be faster and as your system grows, different functions will start slowing down.
Based on the description of your system, my first hunch would be disk io and network io on the NFS servers, then I'd look at MySQL query times. I'd also check the performance of the shared sessions.
The schoolbook way of doing it would be to identify the bottlenecks with real empirical data.
Is it the database, apache, network, cpu, memory,io? Do you need more ram, sharding(+), is the DiskIO, the NFS network load, cpu for doing full table scans?
When you find out where the problem is you might run into the problem that its not enough to scale the infrastructure, because of the way the code works, and you end up with the need to either just create more instances of you current setup or make the code different.
I would also recommend as a first step in terms of scalability, off-load your content to a CDN like Edgecast. Use your current two content servers as additional web servers.