We are using hibernate dynamic queries in our application and observed that some performance issues when some load is put on the server. From a monitoring tool we saw that the time for hibernate queries is around 3 times the execution of JDBC queries executed under the hood.
Breakdown: total(ms) count
http request 22767 1
servlet dispatch 22648 1
jsp render 21578 1
hibernate queries 19760 32
jdbc queries 5989 32
We are using Oracle database server and observed high CPU usage. My understanding is queries are spending more time on CPU that's why it is higher. But not able to understand this huge time difference between hibernate and jdbc timings
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Any recommendation on how to make superset faster?
Cache seems to load full data from the cache, I thought it load only old data from the cache, and real-time data from the database, isn't it like this?
What about some parallel processing?
This answer is valid as of Superset 0.37.0.
At the moment, dashboard performance is affected by a few different factors. I'll enumerate them below along with methods to improve performance:
Database concurrency limits can have an impact on dashboard performance. Dashboards load their information in parallel via concurrent web requests. Make sure that the database user provided allows enough concurrency that queries aren't being queued at the database layer.
Cache performance your caching layer should be able to return multiple results, if not in parallel, extremely quickly. We've had success leveraging S3 for our cache.
Cache hit percentage Superset will hit the cache only for queries that exactly match one that has been run recently. Otherwise the full query will fall through to the underlying analytical DB (Druid in this case). You can reduce the query load on Druid by using a less granular resolution on your dashboard - if it's possible to have it update less frequently, say a couple of times a day rather than in real-time, this can hit cache for all requests other than the first request in the new period under consideration.
Python Web Process Concurrency Limits make sure that your web application server can handle enough parallel requests. The browser will request multiple charts' data at the same time, and the system will need to be able to handle these requests in parallel.
Chart Query Performance As data is frequently requested, especially for real-time data from a database like Druid, optimizing the queries run by the charts can be very useful. I'd take a look at any virtual datasources that are being leveraged to see if they can be materialized or made more efficient.
Web browser concurrent request limits By default most web browsers limit the number of concurrent requests that can be made to the same FQDN. If you have more than 6 charts on the same dashboard, it can be helpful to balance requests across multiple FQDNs running Superset to get around this browser limitation. There's more information on the approach to that in the issue history on Github, but Superset does support this type of configuration.
The community is very interested in improving performance over time, and as such there have been recommendations to move all analytical queries to Celery as well as making other architectural changes to improve performance. I hope this description helps and that something in here will help you track down the issue!
I am using django and tastypie for REST API.
For profiling, I am using django-silk and below is a summary of requests:
How do I profile the complete flow? Time taken except for database queries is (382 - 147) ms on average. How do I figure out the bottleneck and optimize/scale? I did use #silk_profile() for the get_object_list method for this resource, but even this method doesn't seem to be bottleneck.
I used caching for decreasing response time, but that didn't help much, what are the other options?
When testing using loader.io, the peak the server can handle is 1000 requests per 30 secs (which seems very low). Other than caching (which I already tried) what might help?
Here's a bunch of suggestions:
bring the query per request at least below 5 per request (34 per request is really bad)
install django toolbar and have a look where the time is spent
use gunicorn or uwsgi behind a reverse proxy (NGINX)
You have too much queries, even if they are relatively fast you spend
some time to reach database etc. Also if you have external cache
storage (for example, redis) it could take some time to connect
there.
To investigate slow parts of the code you have two options:
Use a profiler - profiling at local PC could make no sense if you have distributed system deployed to several machines
Add tracing points to your code that will record some message and current time (something like https://gist.github.com/dbf256/0f1d5d7d2c9aa70bce89). Deploy this patched code and test it with your load-testing tool and check logs.
On a django application at my job, we develop locally using postgres but our dev/test/prod servers all use Oracle.
Using essentially the same data (loaded via fixtures), this command:
list(Person.objects.all())
runs about 10x slower against oracle databases as compared to postgres. I checked django.db.connection.queries, and the time it takes to actually query the oracle database does not account for the difference in time.
Is this cx_oracle? Is it avoidable? How do I track down this problem? I will hardcode all of the SQL I have to to get it up to a reasonable speed. It's taking something like 5 seconds vs .5 seconds.
I'm considering caching web services results in a in-memory database (Hibernate) and run queries on the cache to perform some filtering and joins (on data from multiple web services.)
Is this a good idea (esp. from a performance point of view given that I've to return a response back to a client) ? Has anyone done something similar? Are there any other alternatives ?
Thanks.
I need some guidance in the realm of server architecture for Django.
My current Django-based web app stats (reached in two weeks - run on one VPS w/ Apache, mod_wsgi, mysql):
10,000 users total
20 avg requests/user/day
200,000 requests/day
8,000 users access site daily
Where the app could reach (where I'd be panicking - this assumes approx linear growth):
200,000 users total
20 avg requests/user/day
4,000,000 requests/day
160,000 users access site daily
The issue here is really just handling page requests. I only store short strings of text-based data, so DB size shouldn't be an issue.
What sort of server architecture should I be setting up from a hardware and software perspective? I need to think about caching, load balancing, multiple processing servers, multiple DB servers, etc, but don't know where to start.
You're projected growth of ~45 / requests per second really isn't that intensive. I think using a standard nginx load balancer in front of your web servers will handle everything. If your DB access isn't very intense you will probably do fine with just 1 DB machine.
I really think the most important thing is not to do any premature optimization. Deal with issues as they come, or else you may end up wasting a lot of time.
There are tons of caching, multiple server configurations, and load balancing tutorials.
Google is a good place to start.
Growing traffic is a standard problem, there are no lack of tutorials on these things.