Transactions make programs easier to reason about.
- Transactions are atomic
Changes made in a transaction are either saved in their entirety or not at all.
This makes error handling a lot easier. If you have an error, you just abort the current transaction. You don’t have to worry about undoing previous database changes.
- Transactions provide isolation
Transactions allow multiple logical threads (threads or processes) to access databases and the database prevents the threads from making conflicting changes.
This allows you to scale your application across multiple threads, processes or machines without having to use low-level locking primitives.
You still have to deal with concurrency on some level. For timestamp-based systems like ZODB, you may have to retry conflicting transactions. With locking-based systems, you have to deal with possible deadlocks.
- Transactions affect multiple objects
- Most NoSQL databases don’t have transactions. Their notions of consistency are much weaker, typically applying to single documents. There can be good reasons to use NoSQL databases for their extreme scalability, but otherwise, think hard about giving up the benefits of transactions.
ZODB transaction support:
Other notable ZODB features¶
- Database caching with invalidation
Every database connection has a cache that is a consistent partial database replica. When accessing database objects, data already in the cache is accessed without any database interactions. When data are modified, invalidations are sent to clients causing cached objects to be invalidated. The next time invalidated objects are accessed they’ll be loaded from the database.
Applications don’t have to invalidate cache entries. The database invalidates cache entries automatically.
- Pluggable layered storage
- ZODB has a pluggable storage architecture. This allows a variety of storage schemes including memory-based, file-based and distributed (client-server) storage. Through storage layering, storage components provide compression, encryption, replication and more.
- Easy testing
Because application code rarely has database logic, it can usually be unit tested without a database.
ZODB provides in-memory storage implementations as well as copy-on-write layered “demo storage” implementations that make testing database-related code very easy.
- Garbage collection
- Removal of unused objects is automatic, so application developers don’t have to worry about referential integrity.
- Binary large objects, Blobs
- ZODB blobs are database-managed files. This can be especially useful when serving media. If you use AWS, there’s a Blob implementation that stores blobs in S3 and caches them on disk.
- Time travel
- ZODB storages typically add new records on write and remove old records on “pack” operations. This allows limited time travel, back to the last pack time. This can be very useful for forensic analysis.
When should you use ZODB?¶
- You want to focus on your application without writing a lot of database code.
- ZODB provides highly transparent persistence.
- Your application has complex relationships and data structures.
In relational databases you have to join tables to model complex data structures and these joins can be tedious and expensive. You can mitigate this to some extent in databases like Postgres by using more powerful data types like arrays and JSON columns, but when relationships extend across rows, you still have to do joins.
In NoSQL databases, you can model complex data structures with documents, but if you have relationships across documents, then you have to do joins and join capabilities in NoSQL databases are typically far less powerful and transactional semantics typically don’t cross documents, if they exist at all.
In ZODB, you can make objects as complex as you want and cross object relationships are handled with Python object references.
- You access data through object attributes and methods.
If your primary object access is search, then other database technologies might be a better fit.
ZODB has no query language other than Python. It’s primary support for search is through mapping objects called BTrees. People have build higher-level search APIs on top of ZODB. These work well enough to support some search.
- You read data a lot more than you write it.
ZODB caches aggressively, and if your working set fits (or mostly fits) in memory, performance is very good because it rarely has to touch the database server.
If your application is very write heavy (e.g. logging), then you’re better off using something else. Sometimes, you can use a database suitable for heavy writes in combination with ZODB.
- Need to test logic that uses your database.
ZODB has a number of storage implementations, including layered in-memory implementations that make testing very easy.
A database without an in-memory storage option can make testing very complicated.
When should you not use ZODB?¶
You have very high write volume.
ZODB can commit thousands of transactions per second with suitable storage configuration and without conflicting changes.
Internal search indexes can lead to lots of conflicts, and can therefore limit write capacity. If you need high write volume and search beyond mapping access, consider using external indexes.
You need to use non-Python tools to access your database.
especially tools designed to work with relational databases
Newt DB addresses these issues to a significant degree. See http://newtdb.org.
How does ZODB scale?¶
Not as well as many technologies, but some fairly large applications have been built on ZODB.
At Zope Corporation, several hundred newspaper content-management systems and web sites were hosted using a multi-database configuration with most data in a main database and a catalog database. The databases had several hundred gigabytes of ordinary database records plus multiple terabytes of blob data.