I finished my tattoo last night. If you like puzzles, here’s a primer for the language, and the design itself. You’ll need some basic algebra for the primer, and a little domain knowledge–or a few Google queries–for the tattoo proper.
These are unpolished thoughts. I started playing again for sources and to refine these ideas, but the game crashes so often that I’m giving up. Still think some folks might find this interesting. Spoilers everywhere.
In the opening, Davey notes that the CounterStrike level appears to be a desert town, but Coda has scattered these floating boxes and out-of-place, brightly-colored cubes in the level: a reminder that the game is not exactly what it purports to be. “Calling cards”, he calls them. A reminder that the game was created by a real person. “They are all going to give us access to their creator. I want to see past the games themselves. I want to know who the real person is.”
In the last Jepsen post, we found that RethinkDB could lose data when a network partition occurred during cluster reconfiguration. In this analysis, we’ll show that although VoltDB 6.3 claims strict serializability, internal optimizations and bugs lead to stale reads, dirty reads, and even lost updates; fixes are now available in version 6.4. This work was funded by VoltDB, and conducted in accordance with the Jepsen ethics policy.
VoltDB is a distributed SQL database intended for high-throughput transactional workloads on datasets which fit entirely in memory. All data is stored in RAM, but backed by periodic disk snapshots and an on-disk recovery log for crash durability. Data is replicated to at least
k+1 nodes to tolerate
k failures. Tables may be replicated to every node for fast local reads, or sharded for linear storage scalability.
In the last Jepsen analysis, we saw that RethinkDB 2.2.3 could encounter spectacular failure modes due to cluster reconfiguration during a partition. In this analysis, we’ll talk about Crate, and find out just how many versions a row’s version identifies.
Crate is a shared-nothing, “infinitely scalable”, eventually-consistent SQL database built on Elasticsearch.
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 stale reads, dirty reads, lost updates, node crashes, and table unavailability requiring an unsafe emergency repair. Versions 2.2.4 and 2.1.6, released last week, address this issue.
Until now, Jepsen tests have used a stable cluster membership throughout the test. We typically run the system being tested on five nodes, and although the network topology between the nodes may change, processes may crash and restart, and the system may elect new nodes as leaders, we do not introduce or remove nodes from the system while it is running. Thus far, we haven’t had to go that far to uncover concurrency errors.
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 hierarchical, dynamically typed, schemaless objects. Each document is uniquely identified by an
id key within a table, which in turn is scoped to a DB. On top of this key-value structure, a composable query language allows users to operate on data within documents, or across multiple documents–performing joins, aggregations, etc. However, only operations on a single document are atomic–queries which access multiple keys may read and write inconsistent data.
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 address Percona’s documentation.
I didn’t use the default isolation levels
Previously, on Jepsen, we saw Chronos fail to run jobs after a network partition. In this post, we’ll see MariaDB Galera Cluster allow transactions to read partially committed state.
Galera Cluster extends MySQL (and MySQL’s fork, MariaDB) to clusters of machines, all of which support reads and writes. It uses a group communication system to broadcast writesets and certify each for use. Unlike most Postgres replication systems, it handles the failure and recovery of all nodes automatically, and unlike MySQL Cluster, it has only one (as opposed to three) types of node. The MariaDB Galera packages are particularly easy to install and configure.
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 avoid connotations of, well, slavery, but the master nodes themselves form a cluster with leaders and followers, and terms like “executor” have other meanings in Mesos, so I’m going to use the Mesos terms here.
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.
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 220.127.116.11
Previously, on Jepsen, we demonstrated stale and dirty reads in MongoDB. In this post, we return to Elasticsearch, which loses data when the network fails, nodes pause, or processes crash.
Nine months ago, in June 2014, we saw Elasticsearch lose both updates and inserted documents during transitive, nontransitive, and even single-node network partitions. Since then, folks continue to refer to the post, often asking whether the problems it discussed are still issues in Elasticsearch. The response from Elastic employees is often something like this: