Mirco Dotta

Come at the @gradle booth at @scaladays for breakfast! Freshly baked croissants 🥐 and Swiss chocolate 🍫, all paired with tips and tricks to improve your #sbt #Gradle #Maven #npm #Python builds!

You know where you need to be at 5pm CEST _today_ This is going to be fun and informative! #scala #JVM #GenAI #DPE

Gradle joining the #Scala Center advisory board means that we get to work more closely with @odersky and the @scala_lang team! globenewswire.com/news-release/2… p.s. Take a look at the latest #Scala builds and tests at develocity.scala-lang.org/scans

I can’t believe @scaladays is only 10 days away! 🥳

The way Datadog calculates percentiles at scale is very innovative 🔥 Usually, calculating the percentiles of large datasets is very expensive. To know the 99th percentile of a stream of values, you need to: - keep all the values - sort them - return the value whose rank matches the percentile (e.g 99th item) Datadog cannot afford to do this with the many millions of data points that come in every second - the space and CPU requirements are not practical for a company with thousands of customers. 🐾 Naturally, they opted for sketch algorithms - those should provide them with a good-enough probabilistic result while being vastly more efficient to compute. Unfortunately - they couldn’t get satisfactory results. The algorithms would produce results that were too inaccurate. ❌ Why? Many percentile sketches had guarantees in terms of *rank error*. A rank-error guarantee of 2% means that the p95 value returned by the sketch is somewhere between the p93-p97 value. But system latencies exhibit very fat tails - the difference between the p97 and p99 values can be 2-10x! So what did the dogs do? 🐶 They invented a new sketch algorithm - DDSketch. Instead of rank error guarantees, they designed it for *relative error* guarantees. If the p99 is 60s, a 2% error means the sketch would return 58.8-61.2s. The algorithm is surprisingly pretty simple: • They create buckets covering ranges of the desired error rate. (+- 2% in this case) 🪣 • Each bucket keeps a counter of the amount of data points within that range. 💯 • When processing an item (latency metric data point), increment the counter of the appropriate bucket. ➕ • To count the desired percentile, you sum up the bucket’s values until you get to the desired percentile. Whatever bucket that percentile is in - that’s your value. 🏆 In this example, the 50th percentile is 1033ms. (4th value out of our total of 8) Going by count, the 4th value is in the second bucket (b-1) and the algorithm would produce a result of 1021-1061ms. To cover the range from 1 millisecond to 1 minute, you only need 275 buckets. With 64-bit counters, that's just ~2kB of memory, regardless of the amount of input data. This is why we call sketch algorithms sublinear in space growth - memory requirements do NOT grow linearly with input. The exponential nature of the bucket distribution makes it cheap to cover an even wider range: 1 nanosecond to 1 day takes just 3x more buckets: • 802 buckets at ~6kB. As you can probably tell, this is pretty easy to parallelize. You can divide this bucket-building exercise into many parallel lightweight substreams, and then merge the results freely. 🕊 The merge operation is a simple sum of the buckets & their counters, which ensures that the accuracy is kept in the same range. It is a very scalable and performant sketch algorithm. Kudos to Datadog for inventing it. Good boy! 🫳🐕‍🦺

Whohooo!

Did you know that @gradle offers much more than a build tool? We caught up with @mircodotta at the Scala Tooling Summit where he explained that Gradle Enterprise is like a Swiss Army knife for developers' productivity. #Scala @scala_lang

Scala 3 is so damn good.

@psuter @jaguarul Thanks @psuter!

Congratulations @mircodotta & @jaguarul ! 🍾🥂

@RomanowskiKr @jaguarul Thank you @RomanowskiKr! Totally agree, #GradleEnterprise is an impressive platform!

@mircodotta @jaguarul - that is huge; congrats! Also, it sounds promising for #scala in general, as I am pretty impressed with #GradleEnterprise

@mgilbertnz @domdorn @gradle @triple_quote @scala_sbt @jaguarul Thanks Matt! 🙏 And another big thank *you* for having supported us all along in this journey. The best days are yet to come! 🤘😎

@mircodotta @domdorn @gradle @triple_quote @scala_sbt @jaguarul Congratulations Mirco! This is great news.

Is it 1st of april already? @gradle acquired @triple_quote and will now support @scala_sbt !?.. #scala

@nremond @jaguarul @etorreborre Thank you, @nremond! How about having a drink sometime soon?! 🥳🎉

@mircodotta @jaguarul @etorreborre Congratulations guys! This is a brilliant piece of news

Super happy to see this press release today: . gradle.com/press-media/gr…. Well done @mircodotta and @jaguarul!

@jaguarul @etorreborre What @jaguarul said - @etorreborre thank you for having been the first one to believe in us and our work! 🙏

@etorreborre @mircodotta I’ll be forever grateful for trusting us when even I was doubting how doable it was. 🙏

For everyone who has asked about the Safer Exceptions paper, here's a link that should work: infoscience.epfl.ch/record/290885?… It seems there is a way to get open access to ACM papers but you have to go through some other link first.

@mariofusco Have you heard of checked exceptions in Scala 3? >> contributors.scala-lang.org/t/pre-sip-chec… I haven’t been following, so I don’t know whether the idea has been pushed forward or not. But I guess we would all love to listen to a talk on the subject. For sure it will be polarizing! 😅

This is a Tyrell P34. No other F1 manufacturer produced a 6 wheeled car before and AFTER it. And indeed it was a terrible idea. Java has checked exceptions. No other programming language introduced this feature before and AFTER it.

Abbiamo novità! scala-italy.it #scala #italia #ScalaItaly

@afaranwide @Livex EP demand for this chateau is far exceeding availability for the retailers I am a client of. Based on the few data points I have, the free market is proving them right.

@Livex How can they justify such a whopping increase on last year, which was rated higher by Neal Martin? And the '19 is available for less than the release price (£746/£813, Liv-ex data). Can't see why anyone would buy it. #Bordeaux2020

#Bdx20 release: Smith Haut Lafitte 2020 - €96 p/b ex-negociant, up 48% on 2019 (€64.80). Analysis coming soon. Compare previous Smith Haut Lafitte release prices while you wait: bit.ly/bdx20prices

@Mads_Hartmann @honeycombio @LaunchDarkly Would love to see it live 🍿🥤😛

Can we please just pretend I somehow found time to turn these notes into a wonderful well written blog post about feature flags? With a complimentary demo app that uses @honeycombio and @LaunchDarkly of course 😅 github.com/mads-hartmann/…