Unable to obtain > 40 RPS after migrating to our own Parse server

[sohagfan]

Our setup is as follows:

• Elastic Beanstalk
• 64bit Amazon Linux 2016.03 v2.1.1 running Node.js (v4.4.3)
• nginx proxy server
• m4.large single instance hosted in Virginia
• Parse Server (v2.2.11)
• Mongo DB driver (v2.1.18)
• mLab dedicated cluster (v3.0.10)

We are getting an average of only around 25 RPS and a peak of 40 RPS. When we exceed the peak, we see high latency and connection dropped errors in the logs.

An example error from the nginx log is as follows:

"2016/06/09 06:46:34 [error] 2684#0: *254 upstream prematurely closed connection while reading response header from upstream, client: , server: , request: "GET /1/classes/. . . host: “www.example.com""

With the same application, on the hosted Parse.com, we were able to scale to as high as required. We were able to request 70+ RPS successfully without any requests dropped.

Are there any configuration changes in any of the setup mentioned above (EB, Node.js, nginx, Parse Server, Mongo DB driver, mLab) or some other that we have not mentioned or missed to get a better performance?

If you have a better performance, what is your setup?

Any pointers / comments will be much appreciated.

[bohemima]

You might want to have a look at the parse-server logs (run with VERBOSE=1 environment variable) and also have a look at indexes and enable profiling in your mongod to pinpoint slow running queries.

[flovilmart]

You may wanna run on multiple smaller instances

[Knana]

At NodeChef we have customers performing 150+ req/sec doing complex ad-hoc queries with just two 512 MB RAM app containers before they even run into the issue you describe. Our databases run on 8 physical cores that is the equivalent of either c4.2xlarge or r3.2xlarge on AWS. We use bare metal infrastructure providing the the best performance. We also provide you with RPS stats in real-time as well so you can gauge this for yourself. We can help you get started if interested.

[flovilmart]

RAM is definitely not an issue with parse-server, as it's much more I/O bound and CPU bound, that's what I experience on my side on GAE, We'd be able to process ~100 rps on 2 instances while maintaining CPU < 50% with n1-highcpu-2 (2vCPU, 1.7Gb of ram each).

[sohagfan]

Thanks for all your comments and suggestions.

@bohemima: We have run the server with VERBOSE=1, but have had no further insight. Thanks for your suggestion of building indexes in Mongo DB to address slow queries. We have done this previously and may need to continue doing this. However, we don't feel this is the issue, as the queries that we run during the regular course of our application have optimized indexes.

@flovilmart and @Knana: Thanks for your suggestion to run on multiple smaller instances and for the performance information you have provided. In order to test the limits of a single server, we disabled autoscaling. It is good to know that you were able to achieve 150+RPS with two containers.
Question: Do you see a temporary performance hit when a new EC2 instance fires up?

Your suggestions are all good suggestions, however we suspect our problem is in the interaction between the Parse server and our application.
One more question: Do you use Node.js profiling tools? We need to gain insight into where the request-response loop is being delayed within the stack.

Thanks in advance.

[kranzky]

We are using NodeChef and currently run 10 256MB app containers. No queries to mongo take more than 20ms, and most take a tiny fraction of that, and yet we do have performance issues. The problem seems to be a rather complex cloud code function which performs a query which returns 10 objects, and then performs an additional 4 queries for each of the returned objects, resolving these 40 queries in a single big Parse.Promise.when(...).

This wouldn't be an issue, I don't think, if each of those additional queries was hitting mongo directly. Which would make sense to me; they're cloud code functions running within the server. But they don't, each additional query hits the app containers which, apart from introducing inefficiencies, means that even running this single cloud code function causes requests to our parse api to queue up. The end result is that this single cloud code function can take up to ten seconds to complete. I just don't get it.

[lpremraj]

Thanks Jason for taking the time to respond.
Much appreciated. We'll look into this and respond
Thanks Again
Prem

On Wednesday 6 July 2016, Jason Hutchens notifications@github.com wrote:

We are using NodeChef and currently run 10 256MB app containers. No
queries to mongo take more than 20ms, and most take a tiny fraction of
that, and yet we do have performance issues. The problem seems to be a
rather complex cloud code function which performs a query which returns 10
objects, and then performs an additional 4 queries for each of the returned
objects, resolving these 40 queries in a single big
{{Parse.Promise.when(...)}}.

This wouldn't be an issue, I don't think, if each of those additional
queries was hitting mongo directly. Which would make sense to me; they're
cloud code functions running within the server. But they don't, each
additional query hits the app containers which, apart from introducing
inefficiencies, means that even running this single cloud code function
causes requests to our parse api to queue up. The end result is that this
single cloud code function can take up to ten seconds to complete. I
just don't get it.

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[sohagfan]

@flovilmart and @Knana: We are revisiting this issue which remains a problem for us. Thanks once again for your previous helpful suggestions and insights. I had a couple of new questions for both of you.

As we know, RPS is not available from the Elastic Beanstalk Monitoring graphs - only latency, CPU utilization, network bytes in and out, network packets in and out.

My questions are:

Q1. How are you measuring RPS? Are you calculating it using some benchmark tests or are you inferring it based on what it was when you had pointed your app to hosted parse.com or something else altogether?

Q2. Are you using primarily Cloud Code functions (/functions endpoint) or are you using the /batch endpoint or /class endpoint or a mixture of some or all of these?

@jasonhutchens , this may be of interest to you: in one of our benchmark tests, using:

• Autoscaling 1->4 on
• Autoscale trigger latency >= 2 seconds
• c4.xlarge instances
• 4 instances active
• the rest of the setup being similar as mentioned in the first post,

we saw a latency of 20-25 seconds with VERBOSE environmental variable set. When we removed the VERBOSE environment variable latency dropped an order of magnitude to between 1 and 2 seconds, all though 4 instances remained active.

[kranzky]

thanks @sohagfan; I'll benchmark with verbose logging disabled. although I still don't understand why Parse queries made from cloud code functions still need to be routed back through the api layer when they could bypass a lot of that?

[sohagfan]

@jasonhutchens: sure, no problem. Hope it helps. You may be aware of this already, but in case you aren't: you have to actually delete the environmental variable for it to stop taking effect; it doesn't matter if the variable exists and its value is 1 or 0; setting the value to 0 makes no difference, it would be as though the variable is still set.

[kranzky]

@sohagfan no, we're running on NodeChef; I'll let them know about this

[Knana]

@sohagfan see below responses to your questions

1. NodeChef metrics measure request/second as well as number of connections in real-time. So we are actually inferring the RPS on a live app the customer is using in production. This information is available from our dashboard. The RPS is calculated by summing the number of request dispatched to all app containers within a second and the connections simply measure the number of open sockets for the app.
2. What we measured is typically a mixed workload scenario, direct class queries as well as cloud code making queries back to query a class and so on.

hope this helps.

[bkprabhak]

@flovilmart please let me know what tests you ran to determine you got ~100 rps with 2 n1-highcpu-2 machine? We are running tests on similar machines on AWS Elastic Beanstalk through REST API and are seeing much worse performance numbers. Should we also expect a difference in performance between Cloud Clode methods triggered using the REST API v clients (through Parse iOS and Android SDKs)?

Also, what trigger did you setup for autoscaling Latency/CPU/Networkout?

@drew-gross @hramos Happy to hear what others are doing as well. We are stuck with our load testing at the moment and it's preventing us from moving to production.

Let me know what tests you recommend for load testing our dev environment so that we can feel comfortable moving to production. Most of our load comes from clients and we are not sure how to simulate this due to the latency issues we have noticed when running benchmark tests through the REST API.

[flovilmart]

For now there should be no difference between cloud code and the client SDK's as all cloud code request go though the HTTP interface. There is a pull request that attempts to run cloud code with direct access to the JS interface instead of the HTTP one.

[reasonman]

@flovilmart I'd be interested in how you got to your 100+ RPS number as well. In my tests using a single n1-highcpu-2 on GCE and testing with Locust, I can get between 20-30 RPS before the CPUs peg. I didn't try 2 but suspect I would only get around double what I'd get with a single instance.

For reference, my load tests using Locust via REST against a clustered Parse instance(PM2: https://nodejs.org/api/cluster.html#cluster_cluster and http://pm2.keymetrics.io/docs/usage/cluster-mode/):

f1-micro-1: ~5 RPS @ ~40ms response time
n1-highcpu-2: ~30 RPS @ ~400ms response time
n1-highcpu-4: ~70 RPS @ ~600ms response time