Hexing the technical interview
Previously: Reversing the technical interview. Long ago, on Svalbard, when you were a young witch of forty-three, your mother took your unscarred wrists in her hands, and spoke: Vidrun, born of the sea-wind through the spruce Vidrun, green-tinged offshoot of my bough, joy and burden of my life Vidrun, fierce and clever, may our clan’s wisdom be yours: Never read Hacker News But Hacker News has rea
Asynchronous replication with failover
In response to my earlier post on Redis inconsistency, Antirez was kind enough to help clarify some points about Redis Sentinel’s design. First, I’d like to reiterate my respect for Redis. I’ve used Redis extensively in the past with good results. It’s delightfully fast, simple to operate, and offers some of the best documentation in the field. Redis is operationally predictable. Data structures a
Jepsen: Riak
Previously in Jepsen, we discussed MongoDB. Today, we’ll see how last-write-wins in Riak can lead to unbounded data loss. So far we’ve examined systems which aimed for the CP side of the CAP theorem, both with and without failover. We learned that primary-secondary failover is difficult to implement safely (though it can be done; see, for example, ZAB or Raft). Now I’d like to talk about a very di
Jepsen: On the perils of network partitions
This article is part of Jepsen, a series on network partitions. We’re going to learn about distributed consensus, discuss the CAP theorem’s implications, and demonstrate how different databases behave under partition. Modern software systems are composed of dozens of components which communicate over an asynchronous, unreliable network. Understanding the reliability of a distributed system’s dynam
Jepsen: Aerospike
Previously, on Jepsen, we reviewed Elasticsearch’s progress in addressing data-loss bugs during network partitions. Today, we’ll see Aerospike 3.5.4, an “ACID database”, react violently to a basic partition. [Update, 2018-03-07] See the followup analysis of 3.99.0.3 Aerospike is a high-performance, distributed, schema-less, KV store, often deployed in caching, analytics, or ad tech environments. I
Jepsen: Elasticsearch
This post covers Elasticsearch 1.1.0. In the months since its publication, Elasticsearch has added a comprehensive overview of correctness issues and their progress towards fixing some of these bugs. Previously, on Jepsen, we saw RabbitMQ throw away a staggering volume of data. In this post, we’ll explore Elasticsearch’s behavior under various types of network failure. Elasticsearch is a distribut
Jepsen: final thoughts
Previously in Jepsen, we discussed Riak. Now we’ll review and integrate our findings. This was a capstone post for the first four Jepsen posts; it is not the last post in the series. I’ve continued this work in the years since and produced several more posts. We started this series with an open problem. Notorious computer expert Joe Damato explains: “Literally no one knows.” We’ve pushed the bound
Clojure from the ground up: welcome
This guide aims to introduce newcomers and experienced programmers alike to the beauty of functional programming, starting with the simplest building blocks of software. You’ll need a computer, basic proficiency in the command line, a text editor, and an internet connection. By the end of this series, you’ll have a thorough command of the Clojure programming language. Who is this guide for? Scienc
Tag: Jepsen
People keep asking why Jepsen is written in Clojure, so I figure it’s worth having a referencable answer. I’ve programmed in something like twenty languages. Why choose a Weird Lisp? Jepsen is built for testing concurrent systems–mostly databases. Because it tests concurrent systems, the language itself needs good support for concurrency. Clojure’s immutable, persistent data structures make it eas
Aphyr - Posts
I’m in the process of replacing a old radiator system with a centrally-ducted, air-source heat pump system with electric resistive backup heat. I’ve found that the default ecobee algorithm seems to behave surprisingly poorly for this system, and wanted to write up some of the settings that I’ve found yield better behavior. A disclaimer. I’m not an HVAC professional. I have two decades in software
Comments on "You Do it Too"
In response to You Do It Too: Forfeiting Partition Tolerance in Distributed Systems, I’d like to remind folks of a few things around CAP. Partition intolerance does not mean that partitions cannot happen, it means partitions are not supported. Specifically, partition-intolerant systems must sacrifice invariants when partitions occur. Which invariants? By Gilbert & Lynch, either the system allows n
Jepsen: Chronos
Chronos is a distributed task scheduler (cf. cron) for the Mesos cluster management system. In this edition of Jepsen, we’ll see how simple network interruptions can permanently disrupt a Chronos+Mesos cluster Chronos relies on Mesos, which has two flavors of node: master nodes, and slave nodes. Ordinarily in Jepsen we’d refer to these as “primary” and “secondary” or “leader” and “follower” to avo
Jepsen: MariaDB Galera Cluster
There’s a neat kind of symmetry here: P1 and P2 are duals of each other, preventing a read from seeing an uncommitted write, and preventing a write from clobbering an uncommitted read, respectively. P0 prevents two writes from stepping on each other, and we could imagine its dual r1(x) … r2(x)–but since reads don’t change the value of x they commute, and we don’t need to prevent them from interlea
Strong consistency models
Update, 2018-08-24: For a more complete, formal discussion of consistency models, see jepsen.io. Network partitions are going to happen. Switches, NICs, host hardware, operating systems, disks, virtualization layers, and language runtimes, not to mention program semantics themselves, all conspire to delay, drop, duplicate, or reorder our messages. In an uncertain world, we want our software to mai
Jepsen: RethinkDB 2.1.5
In this Jepsen report, we’ll verify RethinkDB’s support for linearizable operations using majority reads and writes, and explore assorted read and write anomalies when consistency levels are relaxed. This work was funded by RethinkDB, and conducted in accordance with the Jepsen ethics policy. RethinkDB is an open-source, horizontally scalable document store. Similar to MongoDB, documents are hiera
Jepsen: Percona XtraDB Cluster
Percona’s CTO Vadim Tkachenko wrote a response to my Galera Snapshot Isolation post last week. I think Tkachenko may have misunderstood some of my results, and I’d like to clear those up now. I’ve ported the MariaDB tests to Percona XtraDB Cluster, and would like to confirm that using exclusive write locks on all reads, as Tkachenko recommends, can recover serializable histories. Finally, we’ll ad
Tag: Jepsen
People keep asking why Jepsen is written in Clojure, so I figure it’s worth having a referencable answer. I’ve programmed in something like twenty languages. Why choose a Weird Lisp? Jepsen is built for testing concurrent systems–mostly databases. Because it tests concurrent systems, the language itself needs good support for concurrency. Clojure’s immutable, persistent data structures make it eas
Jepsen: RethinkDB 2.2.3 reconfiguration
In the previous Jepsen analysis of RethinkDB, we tested single-document reads, writes, and conditional writes, under network partitions and process pauses. RethinkDB did not exhibit any nonlinearizable histories in those tests. However, testing with more aggressive failure modes, on both 2.1.5 and 2.2.3, has uncovered a subtle error in Rethink’s cluster membership system. This error can lead to st
The trouble with timestamps
Some folks have asked whether Cassandra or Riak in last-write-wins mode are monotonically consistent, or whether they can guarantee read-your-writes, and so on. This is a fascinating question, and leads to all sorts of interesting properties about clocks and causality. There are two families of clocks in distributed systems. The first are often termed wall clocks, which correspond roughly to the t
Jepsen: RabbitMQ
RabbitMQ is a distributed message queue, and is probably the most popular open-source implementation of the AMQP messaging protocol. It supports a wealth of durability, routing, and fanout strategies, and combines excellent documentation with well-designed protocol extensions. I’d like to set all these wonderful properties aside for a few minutes, however, to talk about using your queue as a lock
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