jm + tuning   41

USE Method: Linux Performance Checklist
Really late in bookmarking this, but has some up-to-date sample commandlines for sar, mpstat and iostat on linux
linux  sar  iostat  mpstat  cli  ops  sysadmin  performance  tuning  use  metrics 
june 2016 by jm
Squeezing blood from a stone: small-memory JVM techniques for microservice sidecars
Reducing service memory usage from 500MB to 105MB:
We found two specific techniques to be the most beneficial: turning off one of the two JIT compilers enabled by default (the “C2” compiler), and using a 32-bit, rather than a 64-bit, JVM.
32bit  jvm  java  ops  memory  tuning  jit  linkerd 
june 2016 by jm
Open Sourcing Dr. Elephant: Self-Serve Performance Tuning for Hadoop and Spark
[LinkedIn] are proud to announce today that we are open sourcing Dr. Elephant, a powerful tool that helps users of Hadoop and Spark understand, analyze, and improve the performance of their flows.


neat, although I've been bitten too many times by LinkedIn OSS release quality at this point to jump in....
linkedin  oss  hadoop  spark  performance  tuning  ops 
april 2016 by jm
Semisupervised Intern Descent
Also known as "Graduate Student Descent", a common approach to hyperparameter tuning in machine learning -- ie. get an intern to sit there tweaking parameters until they find something approximating optimal performance
optimization  funny  terms  terminology  graduate-student-descent  hyperparameters  semisupervised-intern-descent  tuning  interns 
february 2016 by jm
Migration to, Expectations, and Advanced Tuning of G1GC
Bookmarking for future reference. recommended by one of the GC experts, I can't recall exactly who ;)
gc  g1gc  jvm  java  tuning  performance  ops  migration 
may 2015 by jm
Cassandra moving to using G1 as the default recommended GC implementation
This is a big indicator that G1 is ready for primetime. CMS has long been the go-to GC for production usage, but requires careful, complex hand-tuning -- if G1 is getting to a stage where it's just a case of giving it enough RAM, that'd be great.

Also, looks like it'll be the JDK9 default: https://twitter.com/shipilev/status/593175793255219200
cassandra  tuning  ops  g1gc  cms  gc  java  jvm  production  performance  memory 
april 2015 by jm
Optimizing Java CMS garbage collections, its difficulties, and using JTune as a solution | LinkedIn Engineering
I like the sound of this -- automated Java CMS GC tuning, kind of like a free version of JClarity's Censum (via Miguel Ángel Pastor)
java  jvm  tuning  gc  cms  linkedin  performance  ops 
april 2015 by jm
Gil Tene's "usual suspects" to reduce system-level hiccups/latency jitters in a Linux system
Based on empirical evidence (across many tens of sites thus far) and note-comparing with others, I use a list of "usual suspects" that I blame whenever they are not set to my liking and system-level hiccups are detected. Getting these settings right from the start often saves a bunch of playing around (and no, there is no "priority" to this - you should set them all right before looking for more advice...).
performance  latency  hiccups  gil-tene  tuning  mechanical-sympathy  hyperthreading  linux  ops 
april 2015 by jm
JClarity's Illuminate
Performance-diagnosis-as-a-service. Cool.
Users download and install an Illuminate Daemon using a simple installer which starts up a small stand alone Java process. The Daemon sits quietly unless it is asked to start gathering SLA data and/or to trigger a diagnosis. Users can set SLA’s via the dashboard and can opt to collect latency measurements of their transactions manually (using our library) or by asking Illuminate to automatically instrument their code (Servlet and JDBC based transactions are currently supported).

SLA latency data for transactions is collected on a short cycle. When the moving average of latency measurements goes above the SLA value (e.g. 150ms), a diagnosis is triggered. The diagnosis is very quick, gathering key data from O/S, JVM(s), virtualisation and other areas of the system. The data is then run through the machine learned algorithm which will quickly narrow down the possible causes and gather a little extra data if needed.

Once Illuminate has determined the root cause of the performance problem, the diagnosis report is sent back to the dashboard and an alert is sent to the user. That alert contains a link to the result of the diagnosis which the user can share with colleagues. Illuminate has all sorts of backoff strategies to ensure that users don’t get too many alerts of the same type in rapid succession!
illuminate  jclarity  java  jvm  scala  latency  gc  tuning  performance 
february 2015 by jm
TL;DR: Cassandra Java Huge Pages
Al Tobey does some trial runs of -XX:+AlwaysPreTouch and -XX:+UseHugePages
jvm  performance  tuning  huge-pages  vm  ops  cassandra  java 
february 2015 by jm
Maintaining performance in distributed systems [slides]
Great slide deck from Elasticsearch on JVM/dist-sys performance optimization
performance  elasticsearch  java  jvm  ops  tuning 
january 2015 by jm
Latest EBS tuning tips
from yesterday's AWS Summit in NYC:

Cheat sheet of EBS-optimized instances. http://t.co/vmTlhUtpWk
Optimize your queue depth to achieve lower latency & highest IOPS. http://t.co/EO48oa0D6X
When configuring your RAID, use a stripe size of 128KB or 256KB. http://t.co/N0ldtFJ4t6
Use larger block size to speed up the pre-warming process. http://t.co/8UoIeWE2px
ebs  aws  amazon  iops  raid  ops  tuning 
july 2014 by jm
AWS SDK for Java Client Configuration
turns out the AWS SDK has lots of tuning knobs: region selection, socket buffer sizes, and debug logging (including wire logging).
aws  sdk  java  logging  ec2  s3  dynamodb  sockets  tuning 
june 2014 by jm
Scaling Realtime at DISQUS
Disqus' realtime architecture -- nginx PushStream module doing the heavy lifting, basically. See https://gist.github.com/dctrwatson/0b3b52050254e273ff11 for the production nginx configs they use. I am very impressed that push-stream has grown to be so solid; it's a great way to deal with push from the sounds of it.

http://blog.disqus.com/post/51155103801/trying-out-this-go-thing now notes that some of the realtime backends are in Go.

https://speakerdeck.com/dctrwatson/c1m-and-nginx ("C1M and Nginx") is a more up to date presentation. It notes that PushStream supports "EventSource, WebSocket, Long Polling, and forever iframe". More sysctls and nginx tuning in that prez.
sysctl  nginx  tuning  go  disqus  realtime  push  eventsource  websockets  long-polling  iframe  python 
april 2014 by jm
Garbage Collection Optimization for High-Throughput and Low-Latency Java Applications
LinkedIn talk about the GC opts they used to optimize the Feed. good detail
performance  optimization  linkedin  java  jvm  gc  tuning 
april 2014 by jm
Micro jitter, busy waiting and binding CPUs
pinning threads to CPUs to reduce jitter and latency. Lots of graphs and measurements from Peter Lawrey
pinning  threads  performance  latency  jitter  tuning 
march 2014 by jm
Impact of large primitive arrays (BLOBS) on JVM Garbage Collection
some nice graphs and data on CMS performance, with/without -XX:ParGCCardsPerStrideChunk
cms  java  jvm  performance  optimization  tuning  off-heap-storage  memory 
march 2014 by jm
Migrating from MongoDB to Cassandra
Interesting side-effect of using LUKS for full-disk encryption: 'For every disk read, we were pulling in 3MB of data (RA is sectors, SSZ is sector size, 6144*512=3145728 bytes) into cache. Oops. Not only were we doing tons of extra work, but we were trashing our page cache too. The default for the device-mapper used by LUKS under Ubuntu 12.04LTS is incredibly sub-optimal for database usage, especially our usage of Cassandra (more small random reads vs. large rows). We turned this down to 128 sectors — 64KB.'
cassandra  luks  raid  linux  tuning  ops  blockdev  disks  sdd 
february 2014 by jm
Cassandra: tuning the JVM for read heavy workloads
The cluster we tuned is hosted on AWS and is comprised of 6 hi1.4xlarge EC2 instances, with 2 1TB SSDs raided together in a raid 0 configuration. The cluster’s dataset is growing steadily. At the time of this writing, our dataset is 341GB, up from less than 200GB a few months ago, and is growing by 2-3GB per day. The workload on this cluster is very read heavy, with quorum reads making up 99% of all operations.


Some careful GC tuning here. Probably not applicable to anyone else, but good approach in general.
java  performance  jvm  scaling  gc  tuning  cassandra  ops 
january 2014 by jm
Chartbeat's Lessons learned tuning TCP and Nginx in EC2
a good writeup of basic sysctl tuning for an internet-facing HTTP proxy fleet running in EC2. Nothing groundbreaking here, but it's well-written
nginx  amazon  ec2  tcp  ip  tuning  sysctl  linux  c10k  ssl  http 
january 2014 by jm
Netflix: Your Linux AMI: optimization and performance [slides]
a fantastic bunch of low-level kernel tweaks and tunables which Netflix have found useful in production to maximise productivity of their fleet. Interesting use of SCHED_BATCH process scheduler class for batch processes, in particular. Also, great docs on their experience with perf and SystemTap. Perf really looks like a tool I need to get to grips with...
netflix  aws  tuning  ami  perf  systemtap  tunables  sched_batch  batch  hadoop  optimization  performance 
december 2013 by jm
Response to "Optimizing Linux Memory Management..."
A follow up to the LinkedIn VM-tuning blog post at http://engineering.linkedin.com/performance/optimizing-linux-memory-management-low-latency-high-throughput-databases --
Do not read in to this article too much, especially for trying to understand how the Linux VM or the kernel works.  The authors misread the "global spinlock on the zone" source code and the interpretation in the article is dead wrong.
linux  tuning  vm  kernel  linkedin  memory  numa 
october 2013 by jm
What drives JVM full GC duration
Interesting empirical results using JDK 7u21:
Full GC duration depends on the number of objects allocated and the locality of their references. It does not depend that much on actual heap size.


Reference locality has a surprisingly high effect.
java  jvm  data  gc  tuning  performance  cms  g1 
october 2013 by jm
Voldemort on Solid State Drives [paper]
'This paper and talk was given by the LinkedIn Voldemort Team at the Workshop on Big Data Benchmarking (WBDB May 2012).'

With SSD, we find that garbage collection will become a very significant bottleneck, especially for systems which have little control over the storage layer and rely on Java memory management. Big heapsizes make the cost of garbage collection expensive, especially the single threaded CMS Initial mark. We believe that data systems must revisit their caching strategies with SSDs. In this regard, SSD has provided an efficient solution for handling fragmentation and moving towards predictable multitenancy.
voldemort  storage  ssd  disk  linkedin  big-data  jvm  tuning  ops  gc 
september 2013 by jm
 Censum
[JVM] GC is a difficult, specialised area that can be very frustrating for busy developers or devops folks to deal with. The JVM has a number of Garbage Collectors and a bewildering array of switches that can alter the behaviour of each collector. Censum does all of the parsing, number crunching and statistical analysis for you, so you don't have to go and get that PhD in Computer Science in order to solve your GC performance problem. Censum gives you straight answers as opposed to a ton of raw data. can eat any GC log you care to throw at it. is easy to install and use.


Commercial software, UKP 495 per license.
censum  gc  tuning  ops  java  jvm  commercial 
july 2013 by jm
Tuning and benchmarking Java 7's Garbage Collectors: Default, CMS and G1
Rudiger Moller runs through a typical GC-tuning session, in exhaustive detail
java  gc  tuning  jvm  cms  g1  ops 
july 2013 by jm
Java Garbage Collection Distilled
a great summary of the state of JVM garbage collection from Martin Thompson
jvm  java  gc  garbage-collection  tuning  memory  performance  martin-thompson 
july 2013 by jm
Making sense out of BDB-JE fast stats
good info on the system metrics recorded by BDB-JE's EnvironmentStats code, particularly where cache and cleaner activity are concerned. Particularly useful for Voldemort
voldemort  caching  bdb  bdb-je  storage  tuning  ops  metrics  reference 
may 2013 by jm
TCP Tune
These notes are intended to help users and system administrators maximize TCP/IP performance on their computer systems. They summarize all of the end-system (computer system) network tuning issues including a tutorial on TCP tuning, easy configuration checks for non-experts, and a repository of operating system specific instructions for getting the best possible network performance on these platforms.


Some tips for maximizing HPC network performance for the intra-DC case; recommended by the LinkedIn Kafka operations page.
tuning  network  tcp  sysadmin  performance  ops  kafka  ec2 
april 2013 by jm
IOS TCP wifi optimizer
Basically, tweaking a few suboptimal sysctls to optimize for 802.11b/n; requires a Jailbroken IOS device. I'm surprised that Apple defaulted segment size to 512 to be honest, and disabling delayed ACKs sounds like it might be useful (see also http://www.stuartcheshire.org/papers/NagleDelayedAck/).
TCP optimizer modifies a few settings inside iOS, including increasing the TCP receive buffer from 131072 to 292000, disabling TCP delayed ACK’s, allowing a maximum of 16 un-ACK’d packets instead of 8 and set the default packet size to 1460 instead of 512. These changes won’t only speed up your YouTube videos, they’ll also improve your internet connection’s performance overall, including Wi-Fi network connectivity.
tcp  performance  tuning  ios  apple  wifi  wireless  802.11n  sysctl  ip 
february 2013 by jm
Everything I Ever Learned About JVM Performance Tuning @Twitter
presentation by Attila Szegedi of Twitter from last year. Some good tips here, well-presented
tuning  jvm  java  gc  cms  presentations  slides  twitter 
december 2012 by jm
Expensive lessons in Python performance tuning
some good advice for large-scale Python performance: prun and guppy for profiling, namedtuples for memory efficiency, and picloud for trivial EC2-based scale-out. (via Nelson)
picloud  prun  guppy  namedtuples  python  optimization  performance  tuning  profiling 
july 2012 by jm
The MySQL “swap insanity” problem and the effects of the NUMA architecture
very interesting; modern multicore x86 architectures use a NUMA memory architecture, which can cause a dip into swap, even when there appears to be plenty of free RAM available
linux  memory  mysql  optimization  performance  swap  tuning  vm  numa  swap-insanity  swapping  from delicious
september 2010 by jm
Mindblowing Python GIL
'presentation about how the Python GIL actually works and why it's even worse than most people even imagine.' A good chunk btw could be rephrased as 'pthreads is worse than most people even imagine'. pretty awful data, though
python  gil  locking  synchronization  ouch  performance  tuning  coding  interpreters  threads  pthreads  from delicious
february 2010 by jm

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