We're streaming a high volume of data server-side into BigQuery using the google-api-php-client library. The streaming works fine apart from the performance.
Our load testing is giving us an average time of 1000ms (1 sec) to stream one row into BigQuery. We can't have the client waiting for more than 200ms. We've tested with smaller payloads and the time remains the same. Async calls on the client side is not an option for us.
The 'bottleneck' line of code is:
$service->tabledata->insertAll(PROJECT_NUMBER, DATA_SET, TABLE, $request);
Having looked under the hood of the library the call to insert the row is simply a cURL request (Curl.php in the library).
Is there any way to modify the insertAll() to make it faster? We don't care about the result so a fire-and-forget would work for us. We've tried setting CURLOPT_CONNECTTIMEOUT_MS and CURLOPT_TIMEOUT_MS in the underlying cCURL request but it does not work.
Reading all your comments, and side notes. The approach you've chosen does not scale, and won't scale. You need to rethink the approach with async processes.
Processing in background IO bound or cpu bound tasks is now a common practice in most web applications. There's plenty of software to help build background jobs, some based on a messaging system like Beanstalkd.
Basically, you needed to distribute insert jobs across a closed network, to prioritize them, and consume(run) them. Well, that's exactly what Beanstalkd provides.
Beanstalkd gives the possibility to organize jobs in tubes, each tube corresponding to a job type.
You need an API/producer which can put jobs on a tube, let's say a json representation of the row. This was a killer feature for our use case. So we have an API which gets the rows, and places them on tube, this takes just a few milliseconds, so you could achieve fast response time.
On the other part, you have now a bunch of jobs on some tubes. You need an agent. An agent/consumer can reserve a job.
It helps you also with job management and retries: When a job is successfully processed, a consumer can delete the job from the tube. In the case of failure, the consumer can bury the job. This job will not be pushed back to the tube, but will be available for further inspection.
A consumer can release a job, Beanstalkd will push this job back in the tube, and make it available for another client.
Beanstalkd clients can be found in most common languages, a web interface can be useful for debugging.
Related
I want to replace my cron job with apache kafka using php.
Is this possible?
Now my cron does work as updation on databases. Also sending email, sms depend on conditions. Also periodically updation in databases.
And daily backup of database.
Is it possible to implement this using kafka
You need to design your entire environment in terms of events rather than "batch time slots", but yes, it's possible in theory. As a shim, you can start with a Kafka process in Cron that will read messages for a configurable amount of time (or max amount of messages), and then process that chunk.
As for what you have asked for, you can make a CDC / changelog topic for database events (if you make this a compacted topic, you remove the need for a daily backup, as every database event is persistent in Kafka from the beginning of your DB history - look at the Debezium project for a starting point), and you can derive corresponding emails or sms topics which you have consumers polling and firing off SMTP, SMS, or GCM/APNs messages as you're probably already doing if this is the system you are migrating from.
None of this necessarily needs to be in PHP either (or really Kafka over another pub-sub system, for that matter). I would implore you to consider a microservices based approach that uses a client library and technology that makes most sense for your use cases. For example, AWS can integrate Kinesis (or MSK)+SNS+SES and you have an equivalent Kafka+SMS+Email solution with no infrastructure to maintain yourself.
Before you can go down this path, though, you need to stop batching your data into slices for Cron to process, and rather publish the data event-by-event, and do continuous, rolling aggregations as necessary over some time windows
I'm working on a composer package for PHP apps. The goal is to send some data after requests, queue jobs, other actions that are taken. My initial (and working) idea is to use register_shutdown_function to do it. There are a couple of issues with this approach, firstly, this increases the page response time, meaning that there's the overhead of computing the request, plus sending the data via my API. Another issue is that long-running processes, such as queue workers, do not execute this method for a long time, therefore there might be massive gaps between when the data was created and when it's sent and processed.
My thought is that I could use some sort of temporary storage to store the data and have a cronjob to send it every minute. The only issue I can see with this approach is managing concurrency on hight IO. Because many processes will be writing to the file every (n) ms, there's an issue with reading the file and removing lines that had been already sent.
Another option which I'm trying to desperately avoid is using the client database. This could potentially cause performance issues.
What would be the preferred way to do this?
Edit: the package is essentially a monitoring agent.
There are a couple of issues with this approach, firstly, this increases the page response time, meaning that there's the overhead of computing the request, plus sending the data via my API
I'm not sure you can get around this, there will be additional overhead to doing more work within the context of a web request. I feel like using a job-queue based/asynchronous system is minimizing this for the client. Whether you choose a local file system write, or a socket write you'll have that extra overhead, but you'll be able to return to the client immediately and not block on the processing of that request.
Another issue is that long-running processes, such as queue workers, do not execute this method for a long time, therefore there might be massive gaps between when the data was created and when it's sent and processed.
Isn't this the whole point?? :p To return to your client immediately, and then asynchronously complete the job at some point in the future? Using a job queue allows you to decouple and scale your worker pool and webserver separately. Your webservers can be pretty lean because heavy lifting is deferred to the workers.
My thought is that I could use some sort of temporary storage to store the data and have a cronjob to send it every minute.
I would def recommend looking at a job queue opposed to rolling your own. This is pretty much solved and there are many extremely popular open source projects to handle this (any of the MQs) Will the minute cron job be doing the computation for the client? How do you scale that? If a file has 1000 entries, or you scale 10x and has 10000 will you be able to do all those computations in less than a minute? What happens if a server dies? How do you recover? Inter-process concurrency? Will you need to manage locks for each process? Will you use a separate file for each process and each minute? To bucket events? What happens if you want less than 1 minute runs?
Durability Guarantees
What sort of guarantees are you offering your clients? If a request returns can the client be sure that the job is persisted and it will be completed at sometime in the future?
I would def recommend choosing a worker queue, and having your webserver processes write to it. It's an extremely popular problem with so many resources on how to scale it, and with clear durability and performance guarantees.
I'm trying to wrap my head around the message queue model and jobs that I want to implement in a PHP app:
My goal is to offload messages / data that needs to be sent to multiple third party APIs, so accessing them doesnt slow down the client. So sending the data to a message queue is ideal.
I considered using just Gearman to hold the MQ/Jobs, but I wanted to use a Cloud Queue service like SQS or Rackspace Cloud Queues so i wouldnt have to manage the messages.
Here's a diagram of what I think I should do:
Questions:
My workers, would be written in PHP they all have to be polling the cloud queue service? that could get expensive especially when you have a lot of workers.
I was thinking maybe have 1 worker just for polling the queue, and if there are messages, notify the other workers that they have jobs, i just have to keep this 1 worker online using supervisord perhaps? is this polling method better than using a MQ that can notify? How should I poll the MQ, once every second or as fast as it can poll? and then increase the polling workers if I see it slowing down?
I was also thinking of having a single queue for all the messages, then the worker monitoring that distributes the messages to other cloud MQs depending on where they need to be processed, since 1 message might need to be processed by 2 diff workers.
Would I still need gearman to manage my workers or can I just use supervisord to spin workers up and down?
Isn't it more effective and faster to also send a notification to the main worker whenever a message is sent vs polling the MQ? I assume I would the need to use gearman to notify my main worker that the MQ has a message, so it can start checking it. or if I have 300 messages per second, this would generate 300 jobs to check the MQ?
Basically how could I check the MQ as efficiently and as effectively as possible?
Suggestions or corrections to my architecture?
My suggestions basically boil down to: Keep it simple!
With that in mind my first suggestion is to drop the DispatcherWorker. From my current understanding, the sole purpose of the worker is to listen to the MAIN queue and forward messages to the different task queues. Your application should take care of enqueuing the right message onto the right queue (or topic).
Answering your questions:
My workers, would be written in PHP they all have to be polling the cloud queue service? that could get expensive especially when you have a lot of workers.
Yes, there is no free lunch. Of course you could adapt and optimize your worker poll rate by application usage (when more messages arrive increase poll rate) by day/week time (if your users are active at specific times), and so on. Keep in mind that engineering costs might soon be higher than unoptimized polling.
Instead, you might consider push queues (see below).
I was thinking maybe have 1 worker just for polling the queue, and if there are messages, notify the other workers that they have jobs, i just have to keep this 1 worker online using supervisord perhaps? is this polling method better than using a MQ that can notify? How should I poll the MQ, once every second or as fast as it can poll? and then increase the polling workers if I see it slowing down?
This sounds too complicated. Communication is unreliable, there are reliable message queues however. If you don't want to loose data, stick to the message queues and don't invent custom protocols.
I was also thinking of having a single queue for all the messages, then the worker monitoring that distributes the messages to other cloud MQs depending on where they need to be processed, since 1 message might need to be processed by 2 diff workers.
As already mentioned, the application should enqueue your message to multiple queues as needed. This keeps things simple and in place.
Would I still need gearman to manage my workers or can I just use supervisord to spin workers up and down?
There are so many message queues and even more ways to use them. In general, if you are using poll queues you'll need to keep your workers alive by yourself. If however you are using push queues, the queue service will call an endpoint specified by you. Thus you'll just need to make sure your workers are available.
Basically how could I check the MQ as efficiently and as effectively as possible?
This depends on your business requirements and the job your workers do. What time spans are critical? Seconds, Minutes, Hours, Days? If you use workers to send emails, it shouldn't take hours, ideally a couple of seconds. Is there a difference (for the user) between polling every 3 seconds or every 15 seconds?
Solving your problem (with push queues):
My goal is to offload messages / data that needs to be sent to multiple third party APIs, so accessing them doesnt slow down the client. So sending the data to a message queue is ideal. I considered using just Gearman to hold the MQ/Jobs, but I wanted to use a Cloud Queue service like SQS or Rackspace Cloud Queues so i wouldnt have to manage the messages.
Indeed the scenario you describe is a good fit for message queues.
As you mentioned you don't want to manage the message queue itself, maybe you do not want to manage the workers either? This is where push queues pop in.
Push queues basically call your worker. For example, Amazon ElasticBeanstalk Worker Environments do the heavy lifting (polling) in the background and simply call your application with an HTTP request containing the queue message (refer to the docs for details). I have personally used the AWS push queues and have been happy with how easy they are. Note, that there are other push queue providers like Iron.io.
As you mentioned you are using PHP, there is the QPush Bundle for Symfony, which handles incoming message requests. You may have a look at the code to roll your own solution.
I would recommend a different route, and that would be to use sockets. ZMQ is an example of a socket based library already written. With sockets you can create a Q and manage what to do with messages as they come in. The machine will be in stand-by mode and use minimal resources while waiting for a message to come in.
I've a problem which is giving me some hard time trying to figure it out the ideal solution and, to better explain it, I'm going to expose my scenario here.
I've a server that will receive orders
from several clients. Each client will
submit a set of recurring tasks that
should be executed at some specified
intervals, eg.: client A submits task
AA that should be executed every
minute between 2009-12-31 and
2010-12-31; so if my math is right
that's about 525 600 operations in a
year, given more clients and tasks
it would be infeasible to let the server process all these tasks so I
came up with the idea of worker
machines. The server will be developed
on PHP.
Worker machines are just regular cheap
Windows-based computers that I'll
host on my home or at my workplace,
each worker will have a dedicated
Internet connection (with dynamic IPs)
and a UPS to avoid power outages. Each
worker will also query the server every
30 seconds or so via web service calls,
fetch the next pending job and process it.
Once the job is completed the worker will
submit the output to the server and request
a new job and so on ad infinitum. If
there is a need to scale the system I
should just set up a new worker and the
whole thing should run seamlessly.
The worker client will be developed
in PHP or Python.
At any given time my clients should be
able to log on to the server and check
the status of the tasks they ordered.
Now here is where the tricky part kicks in:
I must be able to reconstruct the
already processed tasks if for some
reason the server goes down.
The workers are not client-specific,
one worker should process jobs for
any given number of clients.
I've some doubts regarding the general database design and which technologies to use.
Originally I thought of using several SQLite databases and joining them all on the server but I can't figure out how I would group by clients to generate the job reports.
I've never actually worked with any of the following technologies: memcached, CouchDB, Hadoop and all the like, but I would like to know if any of these is suitable for my problem, and if yes which do you recommend for a newbie is "distributed computing" (or is this parallel?) like me. Please keep in mind that the workers have dynamic IPs.
Like I said before I'm also having trouble with the general database design, partly because I still haven't chosen any particular R(D)DBMS but one issue that I've and I think it's agnostic to the DBMS I choose is related to the queuing system... Should I precalculate all the absolute timestamps to a specific job and have a large set of timestamps, execute and flag them as complete in ascending order or should I have a more clever system like "when timestamp modulus 60 == 0 -> execute". The problem with this "clever" system is that some jobs will not be executed in order they should be because some workers could be waiting doing nothing while others are overloaded. What do you suggest?
PS: I'm not sure if the title and tags of this question properly reflect my problem and what I'm trying to do; if not please edit accordingly.
Thanks for your input!
#timdev:
The input will be a very small JSON encoded string, the output will also be a JSON enconded string but a bit larger (in the order of 1-5 KB).
The output will be computed using several available resources from the Web so the main bottleneck will probably be the bandwidth. Database writes may also be one - depending on the R(D)DBMS.
It looks like you're on the verge of recreating Gearman. Here's the introduction for Gearman:
Gearman provides a generic application
framework to farm out work to other
machines or processes that are better
suited to do the work. It allows you
to do work in parallel, to load
balance processing, and to call
functions between languages. It can be
used in a variety of applications,
from high-availability web sites to
the transport of database replication
events. In other words, it is the
nervous system for how distributed
processing communicates.
You can write both your client and the back-end worker code in PHP.
Re your question about a Gearman Server compiled for Windows: I don't think it's available in a neat package pre-built for Windows. Gearman is still a fairly young project and they may not have matured to the point of producing ready-to-run distributions for Windows.
Sun/MySQL employees Eric Day and Brian Aker gave a tutorial for Gearman at OSCON in July 2009, but their slides mention only Linux packages.
Here's a link to the Perl CPAN Testers project, that indicates that Gearman-Server can be built on Win32 using the Microsoft C compiler (cl.exe), and it passes tests: http://www.nntp.perl.org/group/perl.cpan.testers/2009/10/msg5521569.html But I'd guess you have to download source code and build it yourself.
Gearman seems like the perfect candidate for this scenario, you might even want to virtualize you windows machines to multiple worker nodes per machine depending on how much computing power you need.
Also the persistent queue system in gearman prevents jobs getting lost when a worker or the gearman server crashes. After a service restart the queue just continues where it has left off before crash/reboot, you don't have to take care of all this in your application and that is a big advantage and saves alot of time/code
Working out a custom solution might work but the advantages of gearman especially the persistent queue seem to me that this might very well be the best solution for you at the moment. I don't know about a windows binary for gearman though but i think it should be possible.
A simpler solution would be to have a single database with multiple php-nodes connected. If you use a proper RDBMS (MSql + InnoDB will do), you can have one table act as a queue. Each worker will then pull tasks from that to work on and write it back into the database upon completion, using transactions and locking to synchronise. This depends a bit on the size of input/output data. If it's large, this may not be the best scheme.
I would avoid sqlite for this sort of task, although it is a very wonderful database for small apps, it does not handle concurrency very well, it has only one locking strategey which is to lock the entire database and keep it locked until a sinlge transaction is complete.
Consider Postgres which has industrial strength concurrency and lock management and can handle multiple simultanious transactions very nicely.
Also this sounds like a job for queuing! If you were in hte Java world I would recommend a JMS based archictecture for your solution. There is a 'dropr' project to do something similar in php but its all fairly new so it might not be suitable for your project.
Whichever technoligy you use you should go for a "free market" solution where the worker threads consume available "jobs" as fast as they can, rather than a "command economy" where a central process allocates tasks to choosen workers.
The setup of a master server and several workers looks right in your case.
On the master server I would install MySQL (Percona InnoDB version is stable and fast) in master-master replication so you won't have a single point of failure.
The master server will host an API which the workers will pull at every N seconds. The master will check if there is a job available, if so it has to flag that the job has been assigned to the worker X and return the appropriate input to the worker (all of this via HTTP).
Also, here you can store all the script files of the workers.
On the workers, I would strongly suggest you to install a Linux distro. On Linux it's easier to set up scheduled tasks and in general I think it's more appropriate for the job.
With Linux you can even create a live cd or iso image with a perfectly configured worker and install it fast and easy on all the machines you want.
Then set up a cron job that will RSync with the master server to update/modify the scripts. In this way you will change the files in just one place (the master server) and all the workers will get the updates.
In this configuration you don't care of the IPs or the number of workers because the workers are connecting to the master, not vice-versa.
The worker job is pretty easy: ask the API for a job, do it, send back the result via API. Rinse and repeat :-)
Rather than re-inventing the queuing wheel via SQL, you could use a messaging system like RabbitMQ or ActiveMQ as the core of your system. Each of these systems provides the AMQP protocol and has hard-disk backed queues. On the server you have one application that pushes new jobs into a "worker" queue according to your schedule and another that writes results from a "result" queue into the database (or acts on it some other way).
All the workers connect to RabbitMQ or ActiveMQ. They pop the work off the work queue, do the job and put the response into another queue. After they have done that, they ACK the original job request to say "its done". If a worker drops its connection, the job will be restored to the queue so another worker can do it.
Everything other than the queues (job descriptions, client details, completed work) can be stored in the database. But anything realtime should be put somewhere else. In my own work I'm streaming live power usage data and having many people hitting the database to poll it is a bad idea. I've written about live data in my system.
I think you're going in the right direction with a master job distributor and workers. I would have them communicate via HTTP.
I would choose C, C++, or Java to be clients, as they have capabilities to run scripts (execvp in C, System.Desktop.something in Java). Jobs could just be the name of a script and arguments to that script. You can have the clients return a status on the jobs. If the jobs failed, you could retry them. You can have the clients poll for jobs every minute (or every x seconds and make the server sort out the jobs)
PHP would work for the server.
MySQL would work fine for the database. I would just make two timestamps: start and end. On the server, I would look for WHEN SECONDS==0
I'm working an image processing website, instead of having lengthy jobs hold up the users browser I want all commands to return fast with a job id and have a background task do the actual work. The id could then be used to check for status and results (ie a url of the processed image). I've found a lot of distributed queue managers for ruby, java and python but I don't know nearly enough of any of those languages to be able to use them.
My own tests have been with shared mysql database to queue jobs, lock them to a worker, and mark them as completed (saving the return data in the db). It was just a messy prototype, and the entire time I felt as if I was reinventing the wheel (and not very elegantly). Does something exist in php (or that I can talk to RESTfully?) that I could use?
Reading around a bit more, I've found that what I'm looking for is a queuing system that has a php api, it doesn't have to be written in php. I've only found classes for use with Amazon's SQS, but not only is that not free, it's also quite latent sometimes (over a minute for a message to show up).
Have you tried ActiveMQ? It makes mention of supporting PHP via the Stomp protocol. Details are available on the activemq site.
I've gotten a lot of mileage out of the database approach your describing though, so I wouldn't worry too much about it.
Do you have full control over server?
MySQL queue could be fine in such case. Have a PHP script that is running constantly (in endless while loop), querying the MySQL database for new "tasks" and sleep()ing in between to reduce the load in idle time.
When each task is completed, mark it in the database and move to the next one.
To prevent that whole thing stops if your script crashes/exists (PHP memory overflow, etc.) you can, for example, place it in inittab (if you use Linux as a server) and init will restart it automatically.
Zend_Framework has a queue class, with a number of implementations of Mysql-backed, SQS and some other back-ends.
Personally, I've had excellent results with BeanstalkD recently, which also has a PHP client. I'm just serialising some data with JSON to throw into it, which gets decoded and run on the worker(s).