I have a problem that membase is being very slow on my environment.
I am running several production servers (Passenger) on rails 2.3.10 ruby 1.8.7.
Those servers communicate with 2 membase machines in a cluster.
the membase machines each have 64G of memory and a100g EBS attached to them, 1G swap.
My problem is that membase is being VERY slow in response time and is actually the slowest part right now in all of the application lifecycle.
my question is: Why?
the rails gem I am using is memcache-northscale.
the membase server is 1.7.1 (latest).
The server is doing between 2K-7K ops per second (for the cluster)
The response time from membase (based on NewRelic) is 250ms in average which is HUGE and unreasonable.
Does anybody know why is this happening?
What can I do inorder to improve this time?
It's hard to immediately say with the data at hand, but I think I have a few things you may wish to dig into to narrow down where the issue may be.
First of all, do your stats with membase show a significant number of background fetches? This is in the Web UI statistics for "disk reads per second". If so, that's the likely culprit for the higher latencies.
You can read more about the statistics and sizing in the manual, particularly the sections on statistics and cluster design considerations.
Second, you're reporting 250ms on average. Is this a sliding average, or overall? Do you have something like max 90th or max 99th latencies? Some outlying disk fetches can give you a large average, when most requests (for example, those from RAM that don't need disk fetches) are actually quite speedy.
Are your systems spread throughout availability zones? What kind of instances are you using? Are the clients and servers in the same Amazon AWS region? I suspect the answer may be "yes" to the first, which means about 1.5ms overhead when using xlarge instances from recent measurements. This can matter if you're doing a lot of fetches synchronously and in serial in a given method.
I expect it's all in one region, but it's worth double checking since those latencies sound like WAN latencies.
Finally, there is an updated Ruby gem, backwards compatible with Fauna. Couchbase, Inc. has been working to add back to Fauna upstream. If possible, you may want to try the gem referenced here:
http://www.couchbase.org/code/couchbase/ruby/2.0.0
You will also want to look at running Moxi on the client-side. By accessing Membase, you need to go through a proxy (called Moxi). By default, it's installed on the server which means you might make a request to one of the servers that doesn't actually have the key. Moxi will go get it...but then you're doubling the network traffic.
Installing Moxi on the client-side will eliminate this extra network traffic: http://www.couchbase.org/wiki/display/membase/Moxi
Perry
Related
I have a single page Angular app that makes request to a Rails API service. Both are running on a t2xlarge Ubuntu instance. I am using a Postgres database.
We had increase in traffic, and my Rails API became slow. Sometimes, I get an error saying Passenger queue full for rails application.
Auto scaling on the server is working; three more instances are created. But I cannot trace this issue. I need root access to upgrade, which I do not have. Please help me with this.
As you mentioned that you are using T2.2xlarge instance type. Firstly I want to tell you should not use T2 instance type for production environment. Cause of T2 instance uses CPU Credit. Lets take a look on this
What happens if I use all of my credits?
If your instance uses all of its CPU credit balance, performance
remains at the baseline performance level. If your instance is running
low on credits, your instance’s CPU credit consumption (and therefore
CPU performance) is gradually lowered to the base performance level
over a 15-minute interval, so you will not experience a sharp
performance drop-off when your CPU credits are depleted. If your
instance consistently uses all of its CPU credit balance, we recommend
a larger T2 size or a fixed performance instance type such as M3 or
C3.
Im not sure you won't face to the out of CPU Credit problem because you are using Xlarge type but I think you should use other fixed performance instance types. So instance's performace maybe one part of your problem. You should use cloudwatch to monitor on 2 metrics: CPUCreditUsage and CPUCreditBalance to make sure the problem.
Secondly, how about your ASG? After scale-out, did your service become stable? If so, I think you do not care about this problem any more because ASG did what it's reponsibility.
Please check the following
If you are opening a connection to Database, make sure you close it.
If you are using jquery, bootstrap, datatables, or other css libraries, use the CDN links like
<link rel="stylesheet" ref="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-select/1.12.4/css/bootstrap-select.min.css">
it will reduce a great amount of load on your server. do not copy the jquery or other external libraries on your own server when you can directly fetch it from other servers.
There are a number of factors that can cause an EC2 instance (or any system) to appear to run slowly.
CPU Usage. The higher the CPU usage the longer to process new threads and processes.
Free Memory. Your system needs free memory to process threads, create new processes, etc. How much free memory do you have?
Free Disk Space. Operating systems tend to thrash when the file systems on system drives run low on free disk space. How much free disk space do you have?
Network Bandwidth. What is the average bytes in / out for your
instance?
Database. Monitor connections, free memory, disk bandwidth, etc.
Amazon has CloudWatch which can provide you with monitoring for everything except for free disk space (you can add an agent to your instance for this metric). This will also help you quickly see what is happening with your instances.
Monitor your EC2 instances and your database.
You mention T2 instances. These are burstable CPUs which means that if you have consistenly higher CPU usage, then you will want to switch to fixed performance EC2 instances. CloudWatch should help you figure out what you need (CPU or Memory or Disk or Network performance).
This is totally independent of AWS Server. Looks like your software needs more juice (RAM, StorageIO, Network) and it is not sufficient with one machine. You need to evaluate the metric using cloudwatch and adjust software needs based on what is required for the software.
It could be memory leaks or processing leaks that may lead to this as well. You need to create clusters or server farm to handle the load.
Hope it helps.
I use rails for small applications, but I'm not at all an expert. I'm hosting them on a Digital Ocean server with 512MB ram, which seems to be insufficient.
I was wondering what are Ruby on Rails server requirements (in terms of RAM) for a single app.
Besides I can I measure if my server is able to support the number of application on my server?
Many thanks
It depends on how much traffic you think you need to handle. We have two machines (a 32GB RAM, usage see below) with 32 unicorn workers two serve one app with loads of traffic and we have one machine with loads of 2 worker apps that have very few traffic.
We also have to consider the database (which needs the most RAM by far in our case due to big caches we granted it). And on top of that all we have *nix which caches the filesystem in unused RAM.
Conclusion: It is very hard to tell without you telling us what sort of traffic you expect.
Our memory usage on one of the two servers for the big app: https://gist.github.com/2called-chaos/bc2710744374f6e4a8e9b2d8c45b91cf
The output is from a little ruby script I made called unistat: https://gist.github.com/2called-chaos/50fc5412b34aea335fe9
We're planning to evaluate and eventually potentially purchase perfino. I went quickly through the docs and cannot find the system requirements for the installation. Also I cannot find it's compatibility with JBoss 7.1. Can you provide details please?
There are no hard system requirements for disk space, it depends on the amount of business transactions that you're recording. All data will be consolidated, so the database reaches a maximum size after a while, but it's not possible to say what that size will be. Consolidation times can be configured in the general settings.
There are also no hard system requirements for CPU and physical memory. A low-end machine will have no problems monitoring 100 JVMs, but the exact details again depend on the amount of monitored business transactions.
JBoss 7.1 is supported. "Supported" means that web service and EJB calls can be tracked between JVMs, otherwise all application servers work with perfino.
I haven't found any official system requirements, but this is what we figured out experimentally.
We collect about 10,000 transactions a minute from 8 JVMs. We have a lot of distinct and long SQL queries. We use AWS machine with 2 VCPUs and 8GB RAM.
When the Perfino GUI is not being used, the CPU load is low. However, for the GUI to work properly, we had to modify perfino_service.vmoptions:
-Xmx6000m. Before that we had experienced multiple OutOfMemoryError in Perfino when filtering in the transactions view. After changing the memory settings, the GUI is running fine.
This means that you need a machine with about 8GB RAM. I guess this depends on the number of distinct transactions you collect. Our limit is high, at 30,000.
After 6 weeks of usage, there's 7GB of files in the perfino directory. Perfino can clear old recordings after a configurable time.
We operate two dual-node brokers, each broker having quite different queues and workloads. Each box has 24 cores (H/T) worth of Xeon E5645 # 2.4GHz with 48GB RAM, connected by Gigabit LAN with ~150μs latency, running RHEL 5.6, RabbitMQ 3.1, Erlang R16B with HiPE off. We've tried with HiPE on but it made no noticeable performance impact, and was very crashy.
We appear to have hit a ceiling for our message rates of between 1,000/s and 1,400/s both in and out. This is broker-wide, not per-queue. Adding more consumers doesn't improve throughput overall, just gives that particular queue a bigger slice of this apparent "pool" of resource.
Every queue is mirrored across the two nodes that make up the broker. Our publishers and consumers connect equally to both nodes in a persistant way. We notice an ADSL-like asymmetry in the rates too; if we manage to publish a high rate of messages the deliver rate drops to high double digits. Testing with an un-mirrored queue has much higher throughput, as expected. Queues and Exchanges are durable, messages are not persistent.
We'd like to know what we can do to improve the situation. The CPU on the box is fine, beam takes a core and a half for 1 process, then another 80% each of two cores for another couple of processes. The rest of the box is essentially idle. We are using ~20GB of RAM in userland with system cache filling the rest. IO rates are fine. Network is fine.
Is there any Erlang/OTP tuning we can do? delegate_count is the default 16, could someone explain what this does in a bit more detail please?
This is difficult to answer without knowing more about how your producers and consumers are configured, which client library you're using and so on. As discussed on irc (http://dev.rabbitmq.com/irclog/index.php?date=2013-05-22) a minute ago, I'd suggest you attempt to reproduce the topology using the MulticastMain java load test tool that ships with the RabbitMQ java client. You can configure multiple producers/consumers, message sizes and so on. I can certainly get 5Khz out of a two-node cluster with HA on my desktop, so this may be a client (or application code) related issue.
Can I set up a replica set in MongoDB 1.8 using servers with different amounts of RAM?
server1: 5gb
server2: 2gb
server3: 4gb
If yes, what are the pros and cons?
No, you do not need equal RAM. (Yes, you could set up a replica set as described.)
MongoDB uses memory-mapped files for all caching, which means that cache paging is handled by the operating system. The replicas with more memory will keep more of the database in memory; those with less will page more to disk.
MongoDB will eventually bring the entire database into memory if it can. If you're using two replicas for reads and one for writes, you might want to use the 5gb and 4gb machines for reads, so they are more likely to be hitting RAM.
Yes, you can configure a replica set this way.
If yes, what are the pros and cons?
Here's a doc explaining the major features of replica sets. Let's take a look at these in light of the RAM differences.
Pros:
More computers means better data redundancy. Having that 2GB node at least means that you have one more copy of the data.
Having a full 3 nodes on a replica set makes it easier to take one down for maintenance.
Cons:
Having servers of different sizes isn't great for automated failover. Let's say that your 5GB server is the primary. What happens when it goes down and the 2GB server wins the election? You still have automated fail-over, but your performance has probably dropped dramatically.
Read scaling may not work very well. Depending on your read patterns, sending reads to the 2GB server may result in lots of extra disk hits and slower performance.
So, the big problem here, is really one of performance. If you're just doing this for a dev setup, then it will basically work. But in production you run the risk of completely tanking your app. If your app is used to living on 4GB+ of RAM and then suddenly drops to 2GB, it may become unusable.
Most production setups want to fail over to another "equally-powered" computer.