Vectorization in OLAP Databases

Vectorization in OLAP Databases(aneesh.mataroa.blog)

48 points by alterneesh 4 years ago | 18 comments

snidane 4 years ago |

Isn't the typical big data sql task IO bound?

Vectorization only works when you have a table stored in an optimized columnar format and compute an run a function over a column or to combine multiple columns.

The moment you throw in group bys or windows the data turns into rows that you read from a hash table or after a sort - at which point you lose all opportunities of vectorization.

Since group bys break vectorization, the other use case is for map or reduce (sums, counts) operations over the entire table. In absence of filters you can precompute these for each column.

Plain map or sum like operations in presence of a filter is the only real use case for vectorization in OLAP, if I'm not missing anything.

In that case you need to implement the vectorized operation to work across together with a mask, so that you don't include the filtered out values, and over compressed data, otherwise you're wasting time on bringing the data from disk closer to cpu.

Most general big data sql tasks will not gain significant improvement using vectorization, unless they specialize on map after filter, no group bys, operations, such as perhaps log processing.

Vectorization and other kinds of hardware acceleration is highly useful for small array data that fits into memory such as geo data, APL, numpy, tensors on TPU processing and similar stuff.

jandrewrogers 4 years ago | |

In a well-designed system, you will typically be limited by effective bandwidth, often memory bandwidth or efficient use thereof which is an area where vectorization can help. Modern servers have tremendous storage bandwidth if you have an I/O scheduler capable of using it. Some newer database engines explicitly reject the assumption that storage throughput is precious as a design constraint, since it has become much less true over time due to advances in hardware.

Use of page layouts highly-optimized for vectorized evaluation is common now even if the implementation isn't vectorized. You lose nothing on modern hardware (they are good layouts regardless) and it allows you to easily do vector optimizations later. As a semantic distinction, columnar and vector layouts are organized differently and optimize for somewhat different things even though they have superficially similar appearance. Classic DSM-style columnar is largely obsolete.

Vectorization, first and foremost, is about optimizing selection operations in a database, but it can provide assists in other areas like joins, sorts, and aggregates. Most queries are a composed from these primitives, so many parts of the query plan may benefit. As a heuristic, operations that GPU databases excel at are the same kinds of operations that benefit from vectorization.

Obviously you can't just throw vectorization at an arbitrary database and expect major benefits, they need to be intentionally designed for it.

alterneesh 4 years ago | |

I can't seem to understand why vectorization wouldn't help, say if you read after a sort. Irrespective of whether it fits in memory, or you perform some sort of an external sort, any operation that you want to perform on top of that sorted vector, be it an aggregation to reduce it, or an arithmetic operation with another column, you could still leverage vectorization and would end up using fewer CPU cycles, no?

lidavidm 4 years ago |

The paper "Everything you always wanted to know about compiled and vectorized queries but were afraid to ask" [1] goes into this in much more detail and also compares vectorization with an alternative approach.

[1]: https://dl.acm.org/doi/abs/10.14778/3275366.3284966

einpoklum 4 years ago |

> A recent trend

It's not at all a recent trend, it goes back at least as far as the early 2000s. Also, the obvious next step from thinking about processing a few tuples together, is transposing your view of DB tables all the way, to consider _columns_ rather than _tuples_. This makes a lot of sense when you're processing analytic queries rather than transactions, which typically modify individual tuples. And _that_ view goes back at least another decade earlier if not more.

See my own brief historical survey on the matter in the intro my monograph regarding a computational model for column-oriented analytic engines:

https://arxiv.org/abs/1904.12217

(this is a bit of a shameless self-plug because other, much more distinguished people than myself have written about this subject.)

alterneesh 4 years ago | |

Fair point about the "recentness". I guess it seemed recent to me since I learned about it only a few months back :)

Also, yes, 100%. Columnar storage fits well with vectorized execution! Thanks for sharing

pstoll 4 years ago |

And also some good articles by the CockroachDB folks on vectorization.

https://www.cockroachlabs.com/blog/vectorized-hash-joiner/

https://www.cockroachlabs.com/blog/vectorizing-the-merge-joi...

brutuscat 4 years ago |

isn't it what Apache Arrow [1], Apache CarbonData [2] and others are for?

[1] https://arrow.apache.org/docs/format/Columnar.html

[2] https://carbondata.apache.org/introduction.html

gavinray 4 years ago | |

The bit in Arrow closest to this would probably be Gandiva I think:

  > Gandiva - Vectorized processing for Apache Arrow

https://arrow.apache.org/blog/2018/12/05/gandiva-donation/

It's wicked neat tech.

profquail 4 years ago | |

I think it’s more like Gandiva or DataFusion (both from the Apache Arrow project).

> "Arulraj, J., Pavlo, A., & Menon, P. (2016). Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads. Proceedings of the 2016 International Conference on Management of Data - SIGMOD ’16. doi:10.1145/2882903.2915231 "

- Columnstore often don't support indexing at all (or have weak support for it). This means a scan is needed to find any row (with min/max or segment elimination to avoid opening up files with no matching rows at all). Even if this scan is very fast, its still going to use up more CPU then an index seek. - Most columnstores don't use compression schemes that are incremental. To grab a single row the columnstore likely decompresses many adjacent rows (could be millions of rows - depends on the particular columnstore). - Most columnstores don't support fine grained locking (row level locking). They often lock the entire table or an entire segment (millions?) of rows whenever a single row is written to. This damages concurrency. - The row reconstruction costs are high (gluing the columns back together)

> "In February 2007, the new TPC-E benchmark [7] became a TPC standard. It is designed to be a more realistic OLTP benchmark than TPC-C, e.g., incorporating realistic data skews and referential integrity constraints." > "We find that (i) TPC-E is more read intensive with a 9.7:1 I/O read to write ratio, while TPC-C sees a 1.9:1 read-to-write ratio; and (ii) although TPC-E uses pseudo-realistic data, TPC-E’s I/O access pattern is as random as TPC-C."

query JoesCompletedTodos { users(where: { name: { _eq: "Joe" } }) { id name todos(where: { completed: { _eq: true } }) { text } } }

SELECT users.id, users.name, json_arrayagg(todos) FROM users INNER JOIN todos ON todos.user_id = users.id WHERE users.name = 'Joe' AND todos.completed = true GROUP BY users.id, users.name

> "Most columnstores don't use compression schemes that are incremental. To grab a single row the columnstore likely decompresses many adjacent rows (could be millions of rows - depends on the particular columnstore)."

query JoesCompletedTodos { users(where: { name: { _eq: "Joe" } }) { id name todos(where: { completed: { _eq: true } }) { text } } }

SELECT users.id, users.name, json_arrayagg(todos) FROM users INNER JOIN todos ON todos.user_id = users.id WHERE users.name = 'Joe' AND todos.completed = true GROUP BY users.id, users.name