To make this atomic and durable, a database uses a log to write out information about the records they will be modifying, before applying the changes to all the various data structures it maintains. The usage in databases has to do with keeping in sync the variety of data structures and indexes in the presence of crashes. It is present as early as IBM's System R. I don't know where the log concept originated-probably it is one of those things like binary search that is too simple for the inventor to realize it was an invention. This approach quickly becomes an unmanageable strategy when many services and servers are involved and the purpose of logs quickly becomes as an input to queries and graphs to understand behavior across many machines-something for which english text in files is not nearly as appropriate as the kind structured log described here.) Logs in databases (Actually, if you think about it, the idea of humans reading through logs on individual machines is something of an anachronism. The biggest difference is that text logs are meant to be primarily for humans to read and the "journal" or "data logs" I'm describing are built for programmatic access. The application log is a degenerative form of the log concept I am describing. For clarity I will call this "application logging". Every programmer is familiar with another definition of logging-the unstructured error messages or trace info an application might write out to a local file using syslog or log4j. For distributed data systems this is, in many ways, the very heart of the problem.īut before we get too far let me clarify something that is a bit confusing. A file is an array of bytes, a table is an array of records, and a log is really just a kind of table or file where the records are sorted by time.Īt this point you might be wondering why it is worth talking about something so simple? How is a append-only sequence of records in any way related to data systems? The answer is that logs have a specific purpose: they record what happened and when. So, a log is not all that different from a file or a table. Also, we can't just keep adding records to the log as we'll eventually run out of space.
The contents and format of the records aren't important for the purposes of this discussion. This property will turn out to be essential as we get to distributed systems. Describing this ordering as a notion of time seems a bit odd at first, but it has the convenient property that it is decoupled from any particular physical clock. The log entry number can be thought of as the "timestamp" of the entry. The ordering of records defines a notion of "time" since entries to the left are defined to be older then entries to the right. Each entry is assigned a unique sequential log entry number. Records are appended to the end of the log, and reads proceed left-to-right. It is an append-only, totally-ordered sequence of records ordered by time. Part One: What Is a Log? A log is perhaps the simplest possible storage abstraction.
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In this post, I'll walk you through everything you need to know about logs, including what is log and how to use logs for data integration, real time processing, and system building.
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You can't fully understand databases, NoSQL stores, key value stores, replication, paxos, hadoop, version control, or almost any software system without understanding logs and yet, most software engineers are not familiar with them. Sometimes called write-ahead logs or commit logs or transaction logs, logs have been around almost as long as computers and are at the heart of many distributed data systems and real-time application architectures. One of the most useful things I learned in all this was that many of the things we were building had a very simple concept at their heart: the log. This has been an interesting experience: we built, deployed, and run to this day a distributed graph database, a distributed search backend, a Hadoop installation, and a first and second generation key-value store. We were just beginning to run up against the limits of our monolithic, centralized database and needed to start the transition to a portfolio of specialized distributed systems. I joined LinkedIn about six years ago at a particularly interesting time.
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