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Chapter 5. Encoding and Evolution

Everything changes and nothing stands still.

​ — Heraclitus of Ephesus, as quoted by Plato in Cratylus (360 BCE)

Applications inevitably change over time. Features are added or modified as new products are launched, user requirements become better understood, or business cir‐ cumstances change. In Chapter 1 we introduced the idea of evolvability: we should aim to build systems that make it easy to adapt to change (see “Evolvability: Making Change Easy”).

In most cases, a change to an application’s features also requires a change to data that it stores: perhaps a new field or record type needs to be captured, or perhaps existing data needs to be presented in a new way.

The data models we discussed in Chapter 2 have different ways of coping with such change. Relational databases generally assume that all data in the database conforms to one schema: although that schema can be changed (through schema migrations; i.e., ALTER statements), there is exactly one schema in force at any one point in time. By contrast, schema-on-read (“schemaless”) databases don’t enforce a schema, so the database can contain a mixture of older and newer data formats written at different times (see “Schema flexibility in the document model”).

When a data format or schema changes, a corresponding change to application code often needs to happen (for example, you add a new field to a record, and the applica‐ tion code starts reading and writing that field). However, in a large application, code changes often cannot happen instantaneously:

  • With server-side applications you may want to perform a rolling upgrade (also known as a staged rollout), deploying the new version to a few nodes at a time, checking whether the new version is running smoothly, and gradually working your way through all the nodes. This allows new versions to be deployed without service downtime, and thus encourages more frequent releases and better evolva‐ bility.
  • With client-side applications you’re at the mercy of the user, who may not install the update for some time.

This means that old and new versions of the code, and old and new data formats, may potentially all coexist in the system at the same time. In order for the system to continue running smoothly, we need to maintain compatibility in both directions:

Backward compatibility

Newer code can read data that was written by older code.

Forward compatibility

Older code can read data that was written by newer code.

Backward compatibility is normally not hard to achieve: as author of the newer code, you know the format of data written by older code, and so you can explicitly handle it (if necessary by simply keeping the old code to read the old data). Forward compati‐ bility can be trickier, because it requires older code to ignore additions made by a newer version of the code.

In this chapter we will look at several formats for encoding data, including JSON, XML, Protocol Buffers, Thrift, and Avro. In particular, we will look at how they han‐ dle schema changes and how they support systems where old and new data and code need to coexist. We will then discuss how those formats are used for data storage and for communication: in web services, Representational State Transfer (REST), and remote procedure calls (RPC), as well as message-passing systems such as actors and message queues.

……

Summary

In this chapter we looked at several ways of turning data structures into bytes on the network or bytes on disk. We saw how the details of these encodings affect not only their efficiency, but more importantly also the architecture of applications and your options for deploying them.

In particular, many services need to support rolling upgrades, where a new version of a service is gradually deployed to a few nodes at a time, rather than deploying to all nodes simultaneously. Rolling upgrades allow new versions of a service to be released without downtime (thus encouraging frequent small releases over rare big releases) and make deployments less risky (allowing faulty releases to be detected and rolled back before they affect a large number of users). These properties are hugely benefi‐ cial for evolvability, the ease of making changes to an application.

During rolling upgrades, or for various other reasons, we must assume that different nodes are running the different versions of our application’s code. Thus, it is impor‐ tant that all data flowing around the system is encoded in a way that provides back‐ ward compatibility (new code can read old data) and forward compatibility (old code can read new data).

We discussed several data encoding formats and their compatibility properties:

  • Programming language–specific encodings are restricted to a single program‐ ming language and often fail to provide forward and backward compatibility.
  • Textual formats like JSON, XML, and CSV are widespread, and their compatibil‐ ity depends on how you use them. They have optional schema languages, which are sometimes helpful and sometimes a hindrance. These formats are somewhat vague about datatypes, so you have to be careful with things like numbers and binary strings.
  • Binary schema–driven formats like Thrift, Protocol Buffers, and Avro allow compact, efficient encoding with clearly defined forward and backward compati‐ bility semantics. The schemas can be useful for documentation and code genera‐ tion in statically typed languages. However, they have the downside that data needs to be decoded before it is human-readable.

We also discussed several modes of dataflow, illustrating different scenarios in which data encodings are important:

  • Databases, where the process writing to the database encodes the data and the process reading from the database decodes it
  • RPC and REST APIs, where the client encodes a request, the server decodes the request and encodes a response, and the client finally decodes the response
  • Asynchronous message passing (using message brokers or actors), where nodes communicate by sending each other messages that are encoded by the sender and decoded by the recipient

We can conclude that with a bit of care, backward/forward compatibility and rolling upgrades are quite achievable. May your application’s evolution be rapid and your deployments be frequent.

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