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Performance analysis using pyArango Part I
This is Part I of Performance analysis using pyArango blog series. Please refer here for: Part II (cluster) and Part III (measuring system capacity).
Usually, your application will persist of a set of queries on ArangoDB for one scenario (i.e. displaying your user’s account information etc.) When you want to make your application scale, you’d fire requests on it, and see how it behaves. Depending on internal processes execution times of these scenarios vary a bit.
We will take intervals of 10 seconds, and graph the values we will get there:
- average – all times measured during the interval, divided by the count.
- minimum – fastest requests
- maximum – slowest requests
- the time “most” aka 95% of your users may expect an answer within – this is called 95% percentile
Sorting number strings numerically
Recently I gave a talk about ArangoDB in front of a community of mathematicians. I advertised that nearly arbitrary data can “easily” be stored in a JSON based document store. The moment I had uttered the word “easily”, one of them asked about long integers. And if a mathematician says “long integer” they do not mean 64bit but “properly long”. He actually wanted to store orders of finite groups. I said one should use a JSON UTF-8 string for this but I should have seen the next question coming because he then wanted that a sorted index would actually sort the documents by the numerical value stored in the string. But most databases – and ArangoDB is no exception here – will compare UTF-8 strings lexicographically (dictionary order). Read more
Webinar: Use ArangoDB Agency as fault-tolerant persistent data store
Join our Sr Distributed System Engineer, Kaveh Vahedipour, to learn more about ArangoDB Agency on September 19th, 2017 (6PM CEST/12PM ET/ 9AM PT) – View the Recording.
Distributed systems have become the standard topology on which modern appliances live. While the advantages of distributing workload for both performance as well as fault-tolerance are obvious, the runtime flexible configuration of such deployment becomes non-trivial.
ArangoDB clusters are no different in that regard. A potentially large database cluster’s configuration is manipulated at runtime by addition, alteration and removal of collections, indexes, and even servers. All servers need to trust in a fault-tolerant centralized configuration tree, which we call “the agency” in arango-speak. Read more
ArangoDB | VelocyStream Async Binary Protocol – Data Streaming
With the 3.2 release, ArangoDB comes with version 1.1 of the binary protocol VelocyStream. VelocyStream is a bi-directional async binary protocol which supports sending messages pipelined, multiplexed, uni-directional or bi-directional. The messages themselves are VelocyPack objects. Read more
ArangoDB | Pronto Move Shard – Multi-Model NoSQL Database
In July Adobe announced that they plan the End-of-Life for flash at around 2020.
As HTML5 progressed and due to a long history of critical security vulnerabilities this is – technologically speaking – certainly the right decision. However I tended to also become a bit sad.
Flash was the first technology that brought interactivity to the web. We tend to forget how static the web was in the early 2000s. Flash brought life to the web and there were plenty of stupid trash games and animations which I really enjoyed at the time. As a homage to the age of trashy flash games I created a game which resembles the games of this era: Read more
ArangoDB Webinar: Apps with ArangoDB & KeyLines
Wednesday, September 6th (5PM CEST/11AM ET/8AM PT) – Join the webinar here
As data gets bigger, faster and more complex, you need to arm yourself with the best tools. In this webinar we’ll see how KeyLines and ArangoDB combine to create powerful and intuitive data analysis platforms. Read more
ArangoDB | Thank You for Your Interest in ArangoDB!
“By developers for developers” has been our internal motto since the first lines of ArangoDB code. Building an open-source project at such level of complexity and at a market competitive standard, undoubtedly puts a lot of pressure and almost solely relies on the support and trust of the community.
Every victory counts, be it small appreciation or big success – it’s what gives us inspiration and keeps us going forward. A while ago we’ve been having one of those rainy gray days here in Cologne. Receiving over 10 stars put a smile on faces of our whole team, motivating us to hack harder, brainstorm, bug fix, build, release…
ArangoDB 3.2: RocksDB, Pregel, Fault Tolerant Foxx, Satellite Collections
We are pleased to announce the release of ArangoDB 3.2. Get it here. After an unusually long hackathon, we eliminated two large roadblocks, added a long overdue feature and integrated an interesting new one into this release. Furthermore, we’re proud to report that we increased performance of ArangoDB on average by 35%, while at the same time reduced the memory footprint compared to version 3.1. In combination with a greatly improved cluster management, we think ArangoDB 3.2 is by far our best work. (see release notes for more details)
One key goal of ArangoDB has always been to provide a rock solid platform for building ideas. Our users should always feel safe to try new things with minimal effort by relying on ArangoDB. Todays 3.2 release is an important milestone towards this goal. We’re excited to release such an outstanding product today. Read more
The new SatelliteCollections Feature of ArangoDB
With the new Version 3.2 we have introduced a new feature called SatelliteCollections. This post explains what this is all about, how it can help you, and explains a concrete use case for which it is essential.
Background and Overview
Join operations are very useful but can be troublesome in a distributed database. This is because quite often, a join operation has to bring together different pieces of your data that reside on different machines. This leads to cluster internal communication and can easily ruin query performance. As in many contexts nowadays, data locality is very important to avoid such headaches. There is no silver bullet, because there will be many cases in which one cannot do much to improve data locality.
One particular case in which one can achieve something, is if you need a join operation between a very large collection (sharded across your cluster) and a small one, because then one can afford to replicate the small collection to every server, and all join operations can be executed without network communications.
ArangoDB Finalizes 4.2 Million Euro Investment Led by Target Partners
Funding to accelerate and strengthen the company’s US-based presence
ArangoDB, the company behind one of the fastest growing next generation databases, closed the final tranche of a 4.2 million Euro investment led by Munich-based venture capital firm Target Partners.
The company is developing the open-source NoSQL database ArangoDB, which combines three major data models (graph, key/value, JSON-documents) in one database and with one query language. ArangoDB allows startups and enterprises to speed up innovation cycles, simplify technology stacks and increase on-time and on-budget delivery of software projects. Read more
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