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Vectorized Executor


15 June 2016

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Vectorized Executor

I interned at Citus Data this summer, and implemented a vectorized executor for PostgreSQL. We observed performance improvements of 3-4x for simple SELECT queries with vectorized execution, and decided to open source my project as a proof of concept.

This readme first describes the motivation behind my internship, and my journey with PostgreSQL, database execution engines, and GProf. If you’d like to skip that, you can also jump to build instructions.


I’m a second year student at Bogazici University in Istanbul, and I interned at Citus. When I started my internship, my mentor Metin described to me a common question they were hearing from customers: “I can fit my working set into memory, thanks to cheaper RAM, columnar stores, or scaling out of data to multiple machines. Now my analytic queries are bottlenecked on CPU. Can these queries go faster?”

Since this question’s scope was too broad, we decided to pick a simple, yet useful and representative query. My goal was to go crazy with this (class of) query’s performance. Ideally, my changes would also apply to other queries.

postgres=# SELECT l_returnflag,
	       	      sum(l_quantity) as sum_qty,
		          count(*) as count_order
	       FROM lineitem
	       GROUP BY l_returnflag;

Technical Details

I started my challenge by compiling PostgreSQL for performance profiling, and running it with a CPU-profiler called GProf. I then ran our example SELECT query 25 times to make sure GProf collected enough samples, and looked at the profile output.

In particular, I was looking for any “hot spot” functions whose behavior I could understand and change without impacting correctness. For this, I digged down the GProf call graph, and found the top three functions whose behavior looked self-contained enough for me to understand.

index  %time    self  children    called      name
[7]     43.3    0.77    17.20    150030375    LookupTupleHashEntry [7]
[9]     24.0    1.37     8.60    150030375    advance_aggregates [9]
[17]    12.0    0.26     4.73    150030400    SeqNext [17]

These numbers discouraged me in three ways. First, I was hoping to find a single function that was the performance bottleneck. Instead, PostgreSQL was spending a proportional amount of time scanning over the lineitem table’s tuples [17], projecting relevant columns from each tuple and grouping them [7], and applying aggregate functions on grouped values [9].

Second, I read the code for these functions, and found that they were already optimized. I also found out through profile results that Postgres introduced a per-tuple overhead. For each tuple, it stored and cleared tuples, dispatched to relevant executor functions, performed MVCC checks, and so forth.

Third, I understood at a higher level that PostgreSQL was scanning tuples, projecting columns, and computing aggregates. What I didn’t understand was the dependencies between the thousand other functions involved in query execution. In fact, whenever I made a change, I spent more time crashing and debugging the database than the change itself.

To mitigate these issues, we decided to redefine the problem one month into my internship. To simplify the problem of understanding many internal PostgreSQL functions, we decided to apply my performance optimizations on the columnar store extension. This decision had the additional benefit of slashing CPU usage related to column projections [7].

Then, to speed up tuple scans and aggregate computations, and also to reduce the per-tuple CPU overhead, we decided to try an interesting idea called vectorized execution.

Vectorized execution was popularized by the MonetDB/X100 team. This idea is based on the observation that most database engines follow an iterator-based execution model, where each database operator implements a next() method. Each call to next() produces one new tuple that may in turn be passed to other operators. This “tuple at a time” model introduces an interpretation overhead and also adversely affects high performance features in modern CPUs. Vectorized execution reduces these overheads by using bulk processing. In this new model, rather than producing one tuple on each call, next() operates on and produces a batch of tuples (usually 100-10K).

With the vectorization idea in mind, I started looking into cstore_fdw to see how I could implement aggregate functions. Sadly, PostgreSQL didn’t yet have aggregate push downs for foreign tables. On the bright side, it provided these powerful hooks that enabled developers to intercept query planning and execution logic however they liked.

I started simple this time. I initially overlooked groupings, and implemented vectorized versions of simple sum(), avg(), and count() on common data types. I then grabbed the execution hook, and routed any relevant queries to my vectorized functions.

Next, I generated TPC-H data with a scale factor of 1, loaded the data into the database, and made sure that data was always in-memory. I then ran simple “Select sum(l_quantity) From lineitem” type queries, and compared the regular executor to the vectorized version.

Run-times for simple aggregates

The results looked cheerful. Vectorized functions showed performance benefits of 4-6x across different aggregate functions and data types. The simplest of these functions also had the greatest benefits, plain count(*). This wasn’t all that surprising. The standard executor was calling count(*) on one new tuple, count(*) was incrementing a counter, and then onto the next tuple. The vectorized version was instead a simple for() loop over a group of values.

From there, I started looking into queries that aggregated and grouped their results on one dimension. This time, I made changes to implement vectorized “hash aggregates”, and again routed any relevant queries to my vectorized functions. I then compared the two executors for simple group bys with aggregates.

Run-times for group by aggregates

These results showed performance benefits of around 3x. These improvements were small in comparison to plain aggregate vectorization because of the generic hash function’s computational overhead. Also, I only had time to implement hash vectorization for Postgres’ pass-by-value data types. In fact, it took me quite a while to understand that Postgres even had pass-by-value types, and that those differed from pass-by-reference ones.

If I had the time, I’d look into making my hash aggregate logic more generic. I’d also try an alternate hashing function that’s more specialized, one that only operates on 1-2 columns, and as a result, that goes faster.


In the end, I feel that I learned a lot during my internship at Citus. My first takeaway was that reading code takes much more time than writing it. In fact, only after two months of reading code and asking questions, did I start to understand how the PostgreSQL aggregate logic worked.

Second, it’s exciting to try out new ideas in databases. For beginners, the best way to start is to carve out a specific piece of functionality, find the related PostgreSQL extension API, and start implementing against it.

Finally, I’m happy that we’re open sourcing my work. I realize that the code isn’t as robust or generic as PostgreSQL is. That said, I know a lot more about PostgreSQL and love it, and I can only hope that the ideas in here will stimulate others.


The vectorized execution logic builds on the cstore_fdw extension. Therefore, the dependencies and build steps are exactly the same between the two extensions. The difference is that cstore_fdw reduces the amount of disk I/O by only reading relevant columns and compression data. This extension helps more when the working set fits into memory. In that case, it reduces the CPU overhead by vectorizing simple SELECT queries.

My primary goal with this extension was to test vectorization’s potential benefits. As such, we’d love for you to try this out and give us any feedback. At the same time, please don’t consider this extension as generic and production-ready database code. PostgreSQL does set a high bar there.

cstore_fdw depends on protobuf-c for serializing and deserializing table metadata. So we need to install these packages first:

# Fedora 17+, CentOS, and Amazon Linux
sudo yum install protobuf-c-devel

# Ubuntu 10.4+
sudo apt-get install protobuf-c-compiler
sudo apt-get install libprotobuf-c0-dev

# Mac OS X
brew install protobuf-c

Note. In CentOS 5 and 6, you may need to install or update EPEL 5 or EPEL 6 repositories. See [this page] ( for instructions.

Note. In Amazon Linux, EPEL 6 repository is installed by default, but it is not enabled. See these instructions for how to enable it.

Once you have protobuf-c installed on your machine, you are ready to build cstore_fdw. For this, you need to include the pg_config directory path in your make command. This path is typically the same as your PostgreSQL installation’s bin/ directory path. For example:

PATH=/usr/local/pgsql/bin/:$PATH make
sudo PATH=/usr/local/pgsql/bin/:$PATH make install

Note. postgres_vectorization_test requires PostgreSQL 9.3. It doesn’t support other versions of PostgreSQL.

Before using cstore_fdw, you also need to add it to shared_preload_libraries in your postgresql.conf and restart Postgres:

shared_preload_libraries = 'cstore_fdw'    # (change requires restart)

You can use PostgreSQL’s COPY command to load or append data into the table. You can use PostgreSQL’s ANALYZE table_name command to collect statistics about the table. These statistics help the query planner to help determine the most efficient execution plan for each query.


As an example, we demonstrate loading and querying data to/from a column store table from scratch here. Let’s start with downloading and decompressing the data files.


gzip -d customer_reviews_1998.csv.gz
gzip -d customer_reviews_1999.csv.gz

Then, let’s log into Postgres, and run the following commands to create a column store foreign table:

-- load extension first time after install

-- create server object
CREATE SERVER cstore_server FOREIGN DATA WRAPPER cstore_fdw;

-- create foreign table
CREATE FOREIGN TABLE customer_reviews
    customer_id TEXT,
    review_date DATE,
    review_rating INTEGER,
    review_votes INTEGER,
    review_helpful_votes INTEGER,
    product_id CHAR(10),
    product_title TEXT,
    product_sales_rank BIGINT,
    product_group TEXT,
    product_category TEXT,
    product_subcategory TEXT,
    similar_product_ids CHAR(10)[]
SERVER cstore_server;

Next, we load data into the table:

COPY customer_reviews FROM '/home/user/customer_reviews_1998.csv' WITH CSV;
COPY customer_reviews FROM '/home/user/customer_reviews_1999.csv' WITH CSV;

Note. If you are getting ERROR: cannot copy to foreign table "customer_reviews" when trying to run the COPY commands, double check that you have added cstore_fdw to shared_preload_libraries in postgresql.conf and restarted Postgres.

Next, we collect data distribution statistics about the table. This is optional, but usually very helpful:

ANALYZE customer_reviews;

Finally, let’s run some simple SQL queries and see how vectorized execution performs. We also encourage you to load the same data into a regular PostgreSQL table, and compare query performance differences.

-- Number of customer reviews
SELECT count(*) FROM customer_reviews;

-- Average and total votes for all customer reviews
SELECT avg(review_votes), sum(review_votes) FROM customer_reviews;

-- Total number of helpful votes per product category
    product_group, sum(review_helpful_votes) AS total_helpful_votes

-- Number of reviews by date (year, month, day)
    review_date, count(review_date) AS review_count


The vectorized executor intercepts PostgreSQL’s query execution hook. If the extension can’t process the current query, it hands the query over to the standard Postgres executor. Therefore, if your query isn’t going any faster, then we currently don’t support vectorization for it.

The current set of vectorized queries are limited to simple aggregates (sum, count, avg) and aggregates with group bys. The next set of changes I wanted to incorporate into the vectorized executor are: filter clauses, functions or expressions, expressions within aggregate functions, groups by that support multiple columns or aggregates, and passing vectorized tuples from groupings to order by clauses.

I think all except the last one are relatively easy, but I didn’t have the time to work on them. The last one is harder as PostgreSQL’s query planner follows a recursive pull model. In this model, each relational operator is called recursively to traverse the operator tree from the root downwards, with the result tuples being pulled upwards. Such a recursion occurs with the aggregate operator, and I could intercept all operators that are below the aggregate operator in the query plan tree. If there was an operator on top of the aggregate operator, such as an order by, I may have had to copy code from the executor to properly intercept the recursion.

Usage with CitusDB

The example above illustrated how to load data into a PostgreSQL database running on a single host. However, sometimes your data is too large to analyze effectively on a single host. CitusDB is a product built by Citus Data that allows you to run a distributed PostgreSQL database to analyze your data using the power of multiple hosts. CitusDB is based on a modern PostgreSQL version and allows you to easily install PostgreSQL extensions and foreign data wrappers, including cstore_fdw. For an example of how to use cstore_fdw with CitusDB see the [CitusDB documentation][citus-cstore-docs].

Uninstalling cstore_fdw

Before uninstalling the extension, first you need to drop all the cstore tables:

postgres=# DROP FOREIGN TABLE cstore_table_1;
postgres=# DROP FOREIGN TABLE cstore_table_n;

Then, you should drop the cstore server and extension:

postgres=# DROP SERVER cstore_server;
postgres=# DROP EXTENSION cstore_fdw;

cstore_fdw automatically creates some directories inside the PostgreSQL’s data directory to store its files. To remove them, you can run:

$ rm -rf $PGDATA/cstore_fdw

Then, you should remove cstore_fdw from shared_preload_libraries in your postgresql.conf:

shared_preload_libraries = ''    # (change requires restart)

Finally, to uninstall the extension you can run the following command in the extension’s source code directory. This will clean up all the files copied during the installation:

$ sudo PATH=/usr/local/pgsql/bin/:$PATH make uninstall

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