One of the most exciting developments in the world of Postgres right now is work on the new Zheap storage engine 1. Recently, some sessions at a Postgres conference helped crystallize it and the optimizations it aims to make. Zheap promises to be a significant advancement in the operational aspects of Postgres, even if there’s likely to be a tradeoff that will impact some uses of the database.
You’ll have to forgive the roundabout approach to talking about Zheap here, but I thought it’d be interesting to look at it in the broader of context of databases in general, and what we use them for.
You may have heard database administrators or enthusiasts talk about OLAP and OLTP. They’re wordy terms for the uninitiated, but like many overwrought acronyms, represent relatively simple ideas, and are useful as unambiguous names for two important concepts:
OLAP is “online analytical processing” and refers to a workload of more complex and long-lived analytical queries design to glean insight from a data set, like you’d see in a normal data warehouse.
OLTP is “online transaction processing” and refers to databases tasked with handling large volumes of short-lived transactions incoming from users in real time. Think of your typical production app.
Many databases are suitable for OLAP or OLTP, but not both. For example:
FoundationDB limits transactions to 5 seconds and supports only key/value storage. That effectively makes OLAP impossible, and specializes it for OLTP specifically.
Redshift is capable of crunching huge volumes of data into useful answers, but queries to it are infamously slow (on the order of seconds or much longer), making it good for OLAP but not OLTP which requires much faster responses.
MongoDB encourages document-oriented storage (meaning data is not normalized) and uses a homegrown query language that’s a fraction as expressive as SQL. This makes it unsuitable for OLAP (and only precariously suitable for OLTP).
Some traditional RDMSes like Postgres are not as specialized, and do a good job of both OLAP and OLTP. Users can write extremely complex OLAP queries in SQL (involving joins, aggregations, CTEs, etc.) and rely on the underlying engine to find efficient ways to execute them. Inserts, updates, deletes, and simple (well-indexed) selects are consistently fast, usually finishing in milliseconds, making them great for OLTP as well.
One of Postgres’ most severe and long-standing operational weaknesses is bloat. In its MVCC (multiversion concurrency control) model, both deleted rows and old versions of updated rows are kept right alongside current rows in the heap, which is the physical storage where the contents of tables are stored. Dead rows are eventually reaped by vacuum, but only after they’re no longer needed for any running transaction (regardless of how old) and vacuum gets a chance to run (I’ve previously gone into the details of its inner workings here).
It’s an elegant implementation – easy to reason about, and making all row versions readily accessible to the transactions that need them, but it has its downsides. Relations may become “bloated” when an old transaction is forcing many old versions to be kept around, leading to greatly increased table size. Bloated tables are slower to use because transactions need to iterate through dead rows to check whether they’re visible or not. Those rows can’t be cleaned out until all transactions that could potentially see them finish.
We could say that Postgres has optimized for OLAP at the expense of OLTP (even if it was never a conscious decision). Long-lived transactions have easy access to contemporaneous row versions, but that same feature degrades the performance of current, short-lived transactions.
Zheap is a new storage engine that rethinks how MVCC in Postgres works. With Zheap, rows are often be updated in place, with old versions replaced by new ones. Instead of sticking around in the heap, old versions are moved to an “undo log” in the current page which acts a historical record. The idea is inspired by other databases that already use a similar technique, like Oracle.
If a transaction aborts, old versions from the undo log are applied until the right version is reached. Similarly, old transactions that need an old version follow the undo log back until they find the right one. Like the current heap, old versions need only be kept as long as they’re needed, and their space is reclaimed as old transactions come to an end.
In essence, Zheap shifts the balance of the tradeoff made in Postgres’ MVCC. The “old” heap put all transactions on equal footing by making access to both old and new versions of rows about the same amount of work. Zheap moves old versions out of band making current versions very easy to access by fresh transactions, but old versions more costly to access by stale ones. It optimizes a little more for OLTP at the expense of OLAP, and that will be good news for production apps that run Postgres.
The current plan for Zheap’s implementation includes
introducing “pluggable storage” that allows a storage
engine to be selected at table-level granularity (
TABLE my_table (...) WITH zheap) 2. A big reason for
this is because Zheap is too complex of a change to be
introduced wholesale and will need to be eased into the
system, but it will also give users some control over the
tradeoff they want to make.
Finally, I should also note that while Zheap will be hugely useful for addressing bloat, it will also help with other Postgres operational problems. The “write amplication” in indexes (popularized by Uber) becomes far less severe because tuples are updated in place. Indexes will only need to be updated if there was a change in a column that they cover (previously all indexes needed to be updated for every change). There’s even talk about Zheap potentially eliminating the need for vacuum because of the way it could lazily reclaim slots in the undo log. See the slides from a recent talk on the new engine for more complete details.
1 Zheap is still under very active development and is not yet slated for any upcoming Postgres release.
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