Reprocessing data for application evolution

When maintaining derived data, batch and stream processing are both useful. Stream processing allows changes in the input to be reflected in derived views with low delay, whereas batch processing allows large amounts of accumulated historical data to be reprocessed in order to derive new views onto an existing dataset.

In particular, reprocessing existing data provides a good mechanism for maintaining a system, evolving it to support new features and changed requirements (see Chapter 4). Without reprocessing, schema evolution is limited to simple changes like adding a new optional field to a record, or adding a new type of record. This is the case both in a schema-on-write and in a schema-on-read context (see “Schema flexibility in the document model” on page 39). On the other hand, with reprocessing it is possible to restructure a dataset into a completely different model in order to better serve new requirements.

Schema Migrations on Railways

Large-scale “schema migrations” occur in noncomputer systems as well. For example, in the early days of railway building in 19th-century England there were various competing standards for the gauge (the distance between the two rails). Trains built for one gauge couldn’t run on tracks of another gauge, which restricted the possible interconnections in the train network [9].

After a single standard gauge was finally decided upon in 1846, tracks with other gauges had to be converted—but how do you do this without shutting down the train line for months or years? The solution is to first convert the track to dual gauge or mixed gauge by adding a third rail. This conversion can be done gradually, and when it is done, trains of both gauges can run on the line, using two of the three rails. Eventually, once all trains have been converted to the standard gauge, the rail providing the nonstandard gauge can be removed.

“Reprocessing” the existing tracks in this way, and allowing the old and new versions to exist side by side, makes it possible to change the gauge gradually over the course of years. Nevertheless, it is an expensive undertaking, which is why nonstandard gauges still exist today. For example, the BART system in the San Francisco Bay Area uses a different gauge from the majority of the US.

Derived views allow gradual evolution. If you want to restructure a dataset, you do not need to perform the migration as a sudden switch. Instead, you can maintain the old schema and the new schema side by side as two independently derived views onto the same underlying data. You can then start shifting a small number of users to the new view in order to test its performance and find any bugs, while most users continue to be routed to the old view. Gradually, you can increase the proportion of users accessing the new view, and eventually you can drop the old view [10].

The beauty of such a gradual migration is that every stage of the process is easily reversible if something goes wrong: you always have a working system to go back to. By reducing the risk of irreversible damage, you can be more confident about going ahead, and thus move faster to improve your system [11].

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