Hi Sijie Thanks very much for the response and interest! I will post an update about the track as soon as I have one.
Thanks Sharan On 2022/03/15 23:59:46 Sijie Guo wrote: > Sharan, > > I think it is a very great idea to have a performance engineering track. > Some committers and I are definitely interested in contributing talks to > this track. > > - Sijie > > On Fri, Mar 11, 2022 at 4:44 AM sharanf <sha...@apache.org> wrote: > > > Hi All > > > > The call for tracks for ApacheCon NA is open. There is a suggestion to > > try and run a Performance Engineering track at ApacheCon. At the end of > > the message I have included some details including a definition of what > > we mean by it and some reasoning about why it could be good to run. We > > have a list of projects that have something to do with performance > > engineering and if you take a look - you will see that this project is > > on the list! > > > > So what I need is some feedback as to whether the community thinks that > > this could be an interesting track topic to run at ApacheCon..and more > > importantly would the community be willing to submit talks for it or > > attend ApacheCon to see it. > > > > Like I say - this is just an idea at this stage. If the Performance > > Engineering track does get approval to be included at ApacheCon - do we > > have any volunteers willing to help with managing and promoting the > > track on behalf of the project? > > > > Thanks > > Sharan > > > > ----------------------------- > > > > *Performance Engineering* is the science and practice of engineering > > software with the required performance and scalability characteristics. > > Many Apache projects focus on solving hard Big Data performance and > > scalability challenges, while others provide tools for performance > > engineering - but there are few projects that don’t care about some > > aspect of software performance. > > > > This track will enable Apache projects members to share their > > experiences of performance engineering best practices, tools, > > techniques, and results, from their own communities, with the benefits > > of cross-fertilization between projects. Performance Engineering in the > > wider open source community is pervasive and includes methods and tools > > (including automation and agile approaches) for performance: > > architecting and design, benchmarking, monitoring, tracing, analysis, > > prediction, modeling and simulation, testing and reporting, regression > > testing, and source code analysis and instrumentation techniques. > > > > Performance Engineering also has wider applicability to DevOps, the > > operation of cloud platforms by managed service providers (hence some > > overlap with SRE - Site Reliability Engineering), and customer > > application performance and tuning. This track would therefore be > > applicable to the wider open source community. > > > > *SUPPORTING DETAILS* > > > > *Google Searches* > > Google “Open source performance engineering” has 4,180,000,000 results > > Google “site:apache.org<http://apache.org> performance” has 147,000 > > results > > > > *Apache Projects *which may have some interest in, or focus on, > > performance (just the top results): > > JMeter, Cassandra, Storm, Spark, Samza, Pulsar, Kafka, Log4J, SystemML, > > Drill, HTTP Server, Cayenne, ActiveMQ, Impala, Geode, Flink, Ignite, > > Impala, Lucene, TVM, Tika, YuniKorn, Solr, Iceberg, Dubbo, Hudi, > > Accumulo, Xerces, MXNet, Zookeeper > > > > *Incubator projects *which may have some interest in, or focus on, > > performance**(again just top results): > > Crail, Eagle, Nemo, Skywalking, MXnet, HAWQ, Mnemonic, CarbonData, > > Drill, ShenYu, Tephra, Sedona > > > > *References *(randomly selected to show the range of open-source > > performance engineering topics available, rather than the quality of > > articles): > > > > 1. Performance Engineering for Apache Spark and Databricks Runtime > > ETHZ, Big Data HS19 > > < > > https://archive-systems.ethz.ch/sites/default/files/courses/2019-fall/bigdata/Databricks%20ETHZ%20Big%20Data%20HS19.pdf > > > > > 2. Real time insights into LinkedIn's performance using Apache Samza > > < > > https://engineering.linkedin.com/samza/real-time-insights-linkedins-performance-using-apache-samza > > > > > 3. A day in the life of an open source performance engineering team > > <https://opensource.com/article/19/5/life-performance-engineer> > > 4. Locating Performance Regression Root Causes in the Field Operations > > of<https://ieeexplore.ieee.org/document/9629300>Web-based Systems: > > An Experience Report Published in: IEEE Transactions on Software > > Engineering (Early Access) > > <https://ieeexplore.ieee.org/document/9629300> > > 5. How to Detect Performance Changes in Software History: Performance > > Analysis of Software System Versions > > <https://dl.acm.org/doi/10.1145/3185768.3186404> > > 6. Performance-Regression Pitfalls Every Project Should Avoid > > < > > https://www.eetimes.eu/performance-regression-pitfalls-every-project-should-avoid/ > > > > > 7. How to benchmark your websites with the open source Apache Bench > > tool > > < > > https://www.techrepublic.com/article/how-to-benchmark-your-websites-with-the-open-source-apache-bench-tool/ > > > > > 8. Benchmarking Pulsar and Kafka - A More Accurate Perspective on > > Pulsar’s Performance > > < > > https://streamnative.io/blog/tech/2020-11-09-benchmark-pulsar-kafka-performance/ > > > > > 9. Performance-Analyse: Apache Cassandra 4.0.0 Release > > <https://benchant.com/blog/cassandra-4-performance> > > 10. Log4J Performance - This page compares the performance of a number > > of logging frameworks > > <https://logging.apache.org/log4j/2.x/performance.html> > > 11. SystemML Performance Testing > > <https://systemds.apache.org/docs/1.0.0/python-performance-test.html> > > > > >
