Berlin Buzzwords 2015 – Day 2

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It’s All Fun And Games Until…: A Tale of Repetitive Stress Injury (Eric Evans)

Basically, watch out for yourself. If it hurts, you are doing something wrong. It’s good to be reminded about that from time to time. So, watch out for yourself:

 

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A complete Tweet index on Apache Lucene (Michael Busch)

Michael Busch has given this talk in one version or the other for a couple of years now. Unfortunately it got more shallow now, not so many technical details about how they optimized Lucene for Twitter. Numbers are great, they have two billion queries per day and about 500 million tweets per day. One thing that he didn’t mention in his earlier talks is that they actually figured that Earlybird does scale to Twitter level requests due to the Earthquake in Japan when they had to emergency shutdown the caching layer in front (which was Ruby on Rails and did not scale that well). Nowadays they have all tweets in a pretty vanilla Lucene with some additions (going to be open sourced soon) and use a Mesos cluster in case they have to reindex all the data.

And BTW: Tweet IDs encode the timestamp the tweet was send at.

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Automating Cassandra Repairs (Radovan Zvoncek)

First time speaker Radovan did a pretty good job. Apparently there are several ways to get to the “consistency” of the “eventual consistency” in Cassandra, which are Read Repair, Hinted Handoff and full blown anit-entropy repairs. The latter ones apparently can lead to a lot of problems if done improperly, so Spotify build something to manage that: Reaper.  The problems are usually due to disk IO limits, network saturation or just plain full disks. Spotify Reaper orchestrates anti-entropy repairs to make them reliable.

I’m still somewhat confused that one aparently has to spend a lot of time repairing Casandra clusters. I always thought that was what Cassandra was doing.

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Diving into Elasticsearch Discovery (Shikhar Bhushan)

For all the people who forgot, like me, how ES clustering works this was a good reminder. Plus I learned that discovery is pluggable, so you can write your own plugin to provide the clustering part for ES. He apparently did and wrote Eskka, an Akka based clustering approach. Writing your own apparently isn’t that much fun because APIs change all the time. Just in case you forgot, Zen is the default way ES clusters.

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Change Data Capture: The Magic Wand We Forgot (Martin Kleppmann)

We all know the problem Martin was describing: same data in different form, like in your database, in your cache, in your search engine. He went back to the “Change Data Capture” principle, which basically says “save once, distribute everywhere”. So in order to realize that he wrote a PostgreSQL plugin “Bottled Water” which gets the changes from Postgres and posts them to a Kafka topic. Yay for the best project name this year in the category: will never find that on google.

His implementation and idea is solid, the problem is that it is a Kafka topic per table, so you actually loose the transaction when reading from Kafka. Otherwise it is transaction save, messages are only sent when the transaction in Postgres commits. He uses Avro on the wire and transforms the Postgres DDL Schema to an Avro schema.

If you want to get your transaction back you would need a stream processor (Storm/Spark) downstream to reassemble your transactions. Might be a good idea if you already have a Postgres DB or rely on some special properties of a centralized Datastore, otherwise it is OK if your microservices write directly to Kafka.

Has someone actually coined the word “nanoservices” yet for designs that basically do just one thing? Like take the request, write it to a queue (Kafka) and all other processing taking place by consumers down the queue that do just one thing as well.

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Designing Concurrent Distributed Sequence Numbers for Elasticsearch (Boaz Leskes)

Elasticsearch is rewriting the way they do distributed indexing based on the Raft Consensus Algorithm. Sounds great, they are mitigating a lot of problems they do have right now.

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Apache Lucene 5 – New Features and Improvements for Apache Solr and Elasticsearch (Uwe Schindler)

Apparently, Lucene 4 broke a lot of indexes due to it’s build in backward compatibility to Lucene <=3. With two big companies actually relying on Lucene, that kind of amazes me.

Lucene 5 gets rid of all this legacy stuff and drops support for older indexes. Plus it adds a lot of data safety features when it comes to on-disk indices like checksums and sequence numbers. So, Solr and Elasticsearch should finally be production ready …. ;).

JDK seems to keep breaking Lucene (remember that the initial JDK 7 release broke Lucene?), apparently one should not use G1 GC with Lucene (es? Solr?).

And Lucene 5 uses a lot of the “new” JDK 7 APIs for IO to finally get the index safely to disk.

 

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Real-Time Monitoring of Distributed Systems (Tobias Kuhn)

Less distributed, more of Real-Time monitoring. Apparently they build their own system for analyzing their loggs for anomaly detection, punnily named Anna Molly, which was open sourced now.

They made pretty clear that thresholds are not enough if you have a highly dynamic system that can change on multiple dimensions any time. Seasonality of your date makes it even harder to define useful thresholds. There are a couple of algorithms which can be used for anomaly detection, namely Tukey’s outlier detection and seasonal trend decomposition. And T-digest comes to the rescue of course.

For monitoring they actually use a cascade of statsd and carbon.

 

To sum up bbuzz 2015:

 

 

 

 

Berlin Buzzwords 2015 – Day 1

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Analytics in the age of the Internet of Things (Ludwine Probst)

Basically a talk about analyzing a demo dataset from sports activity via Spark. Not so much new stuff in there, but beautifully manually illustrated slides.

Real time analytics with Apache Cassandra and Apache Spark (Christopher Batey)

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Good speaker, awesome talk. Some takeaways:

  • One should read the dynamo paper
  • You can (mis)use the datacenter awareness of Cassandra for isolating workloads if you run spark on top of it.
  • 500ms is the lowest usefull microbatch length

Application performance management with open source tools (Tudor Golubenco, Monica Sarbu)

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Packetbeat, which apparently just joined Elastic, is a TCP Layer application monitoring solution. So what they basically do is understand your protocol (HTTP, Redis, Postgres) and give you metrics directly from the traffic (how long did my HTTP request take?). Sound really interesting, as it doesn’t need any integration. Will be integrated into the ELK-Stack and get some more data providers. I really like the idea.

Practical t-digest Applications (Ted Dunning)

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t-digest is an algorithm to get realtime quantiles/percentiles out of your data. That comes in handy if you want to have the data always at your fingertips and/or want to identify outliers. It is blazingly fast and needs constant memory, so you actually want to have it wherever you have numbers. Of course there is a Java-Library and an integration into Elasticsearch. Awesome speaker as well.

The Do’s and Don’ts of Elasticsearch Scalability and Performance (Patrick Peschlow)

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Basically a long reminder to RTFM. Know what data you need, now the pitfalls, disable features you don’t need and make sure that your cluster setup fits your requirements.

Detecting Events on the Web with Java, Kafka and ZooKeeper (James Stanier)

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Good speaker, I think they build quite interesting stuff at Brandwatch. It was not clear to me till the end why the built all that stuff themselves, but I think they co-evolved with Storm/Spark and just made their existing software cluster aware rather than rewriting the stuff.

Reminded me that there is Apache Curator, a set of high level abstractions for Zookeeper services (https://curator.apache.org/)

Analyzing and Searching Streams of Social Media at Scale using Spark, Kafka and Elasticsearch (Markus Lorch)

IBM is using Spark. Basically they got a pretty standard setup to get a lot of data from Twitter and enrich/augment that with some of their proprietary tech (mood detection etc.). Nothing special here.

Predictive Insights for IT Operations (Omer Trajman)

Actually a pretty good speaker, but I didn’t really get the whole point. He basically explained that you should use the same big data techniques for analyzing the data that comes out of your operations measurements (and btw, he has a company specializing on that). But it wasn’t a sales talk. So basically, yes, analyze all the data.

 

code.talks 2014 – Tag 2

Machine Learning mit künstlichen Neuronalen Netzen und Clojure
Stefan Richter
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Recruiting @fdc 😉
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Datengetriebene Analyse und Verbesserung von Code
Andreas Dewes
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Die Jungs fahren einen interessanten Ansatz, leider aktuell nur für Python.

Code und Gesellschaft – macht was draus!
Nico Lumma
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Guerrilla software design: doing it wrong and getting it right
Marco Cecconi
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Predictive Analytics zum Schutze der Liebe
Mario Selk
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Die haben bei Parship das interessante Problem der Scammer. War ein sehr unterhaltsamer Talk.

Handgranaten für die Developer
Nils Lauk
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code.talks 2014 – Tag 1

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Elasticsearch Lessons Learned
Patrick Peschlow

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Marvel is a management and monitoring product for Elasticsearch. Daraus das Sense UI als Elasticsearch Client.

Percolator sind gespeicherte Suchen.

Immer Aliase für Indexe benutzen.

Plugins kennen.

https://blog.codecentric.de
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WebAPI – expand what the Web can do today
Carsten Sandtner
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XTags (?) Mozillas Implementierung auf Web Components, wie Polymer
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Contacts API
Settings API
Vibration API
Alarm API
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Modern Web Application (In-)Security
Fabian Beterke, Felix Schmidt
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Im Westen nichts neues. Bei PHP auch nicht.

Introduction to CoreOS
Timo Derstappen
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Github ist eine Public SSH Key Registry.
etcd, flannel, fleet, locksmith for update management

Hamburg Geekettes – Lightning Talks
Diana Knodel, Uygar Gomez, Lisa Junger, Tina Egolf, Inga Halpin, Eshani Sarma, Tina Umlandt
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http://www.vivoie.com – Plattform for part-time entrepreneurs

Schluss mit Copy & Paste! Design Pattern automatisieren mit Xtend
Sebastian Zarnekow, Sven Efftinge
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Active Annotations – write code that writes code. Nice. Ich hab echt Lust bekommen noch mal Xtend eine Chance zu geben. Ich mag halt kein Scala.

Distributed Ad hoc Real-Time Stream Processing
Christian Kreutzfeldt
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OTTO hatte ein Problem dem sie mit Kafka, Storm und Stanza nicht beikommen konnten und haben sich selbst was gebaut. Das heißt wohl ASAP. Das wird wohl bald (asap) Open Source.

Developer Conference Hamburg 2013 – Day 2

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Man sollte immer einmal im Jahr was über Sicherheit hören. Der Saal war voll, der Speaker hat extra dunkle Folien gemacht damit wir nach der Party nicht so geblendet werden. Trotzdem was gelernt. Beef Project ist wohl ein XSS Toolkit mit dem man sich mal anschauen kann was so geht. Versioneye hält Bibliotheken und deren Version im Auge und sagt bescheid wenn es was neues gibt.

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Der Kollege sprach selbst für meinen Zustand etwas zu monoton. War auch eher ein Einsteigervortrag, daher nichts neues an der Front.

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Guter Überblick darüber wie man E-Commerce richtig macht. Technisch jetzt keine großen Besonderheiten.
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Johannes hat ein wenig was über Nerds und Manager erzählt und das agile Methoden eigentlich die Lösung für alles sind.

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TypeScript ist eine Microsoft Erweiterung für JS die Typen erlaubt. Sieht gut aus, würde ich benutzen, vor allem nachdem das was ich bis jetzt von Coffeescript gesehen habe nicht so überzeugend war.

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Guter Einsteigervortrag von einem Cloudera Menschen. Den Teil kannte ich aber leider schon.

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Dann hat Christian was über CDNs erzählt.

Fazit: Super Konferenz, nächstes Jahr wieder.

Developer Conference Hamburg 2013 – Day 1 – Ganz großes Kino

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Heute begann die dritte Developer Conference in Hamburg. Aus nicht näher zu bestimmenden Gründen hab ich die letzten beiden verpasst, dieses Jahr konnte ich dann endlich mal teilnehmen. Als Location diente das Cinemaxx am Dammtor, ich bevorzuge ja wenn Events in fußläufiger Entfernung zu meiner Wohnung stattfinden.
Der Untertitel Klassentreffen war schon mal sehr passend, die üblichen verdächtigen der Hamburger IT-Szene konnte man schon morgens alle begrüßen.
Die Location an sich war schon mal sehr großartig, Talks in Kinosesseln zu hören ist auf jeden Fall sehr gemütlich.

Die Talks
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Die G+J Digital Leute hauen mal raus was sie so machen und wie sie so arbeiten. An sich interessant, wenn man stern.de aber alle drei Jahre neu baut ist das wohl tatsächlich ein besonderer Fall. Da kann man bei der Qualität schon mal Kompromisse eingehen.

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Schönes großes System: 560.000.000 PIs in 30 Tagen, 25 Server, Elasticsearch, 384 GB RAM auf den DB Servern, 32 GB auf den Appservern, .NET, Redis.
Lustiges live staging über meta.stackoverflow.com, wo die Entwicklungsversion läuft. Fünf Deployments pro Tag.
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Schönes Caching, die cachen sogar ihre UI Objekte um die GC der CLR zu entlasten.
Schöner interessanter Talk.

Angular.js (Hannes Finck)
Super Code basierter Talk, ich muss dringend mal was mit Angular machen. Vor allem muss ich slid.es und Plunker mal testen.

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Dann hat Steff halt was über Go erzählt 🙂

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Neo4j
Graphdatenbanken sind klasse wenn man stark verbundene Daten hat, die man mit Attributen an Nodes und Edges abbilden kann. Nervige Relationstabellen die man in relationalen DBs braucht fallen weg. Daher auch keine Joins und das ist wohl ziemlich schnell. Transaktionen kann es aber auch. Implementiert in Java und spricht REST, kann man aber auch embedden.

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Dann hat Steff was über Clojure erzählt.
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elasticsearch
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Crate ist eine OpenSource Erweiterung für Elasticsearch, erlaubt SQL abfragen. Quasi eine Big Data DB auf Basis von Lucene. Die Jungs haben das seit drei Jahren im produktiven Einsatz. Kommt demnächst.

Fazit: erster Tag war sehr gut, viel Code gesehen, viele Ideen gehört und viele viele Leute getroffen. Jetzt geht es noch kurz zum Social Event.