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Originally forked from: https://github.com/Percona-Lab/clickhousedb_fdw. Differences:
- remove ODBC, use HTTP or binary protocols
- fix JOINS
- push down CASE WHEN
- support coalesce, date_trunc and other specific functions
- support query cancelation
- use cmake for Makefile generation (for out for source builds and proper dependency checks)
- support arrays and ANY, ALL functions
- support CollapsingMergeTree engine (with sign column) in aggregations
- support AggregatingMergeTree engine (add Merge prefix)
- add infrastructure for adding custom behaviours in the code (for custom types)
- code cleanup
- many other improvements
The clickhouse_fdw is open-source. It is a Foreign Data Wrapper (FDW) for one of the fastest column store databases; "Clickhouse". This FDW allows you to SELECT from, and INSERT into, a ClickHouse database from within a PostgreSQL v11 server.
The FDW supports advanced features like aggregate pushdown and joins pushdown. These significantly improve performance by utilizing the remote server’s resources for these resource intensive operations.
PostgreSQL 11-12
The clickhouse_fdw uses an HTTP interface provided by ClickHouse. libcurl and uuid libraries should be installed in the system. Make sure that ClickHouse uses UTC timezone.
git clone https://github.com/adjust/clickhouse_fdw.git cd clickhouse_fdw mkdir build && cd build cmake .. make && make install
You should be using modern compiler, available in devtoolset-7. Once installed, activate it with the command:
source scl_source enable devtoolset-7Minimal libcurl compatible with clickhouse-fdw is 7.43.0. It is not available in the official Centos repo. You can download recent RPMs from here
You can upgrade libcurl with command:
sudo rpm -Uvh *curl*rpm --nodeps
sudo yum install libmetalinkuuid can be installed with:
yum install libuuid-develYou need to set up the sample database and tables in the ClickHouse database. Here we create a sample database name test_database and two sample tables tax_bills_nyc and tax_bills:
CREATE DATABASE test_database;
USE test_database;
CREATE TABLE tax_bills_nyc
(
bbl Int64,
owner_name String,
address String,
tax_class String,
tax_rate String,
emv Float64,
tbea Float64,
bav Float64,
tba String,
property_tax String,
condonumber String,
condo String,
insertion_date DateTime MATERIALIZED now()
)
ENGINE = MergeTree PARTITION BY tax_class ORDER BY (owner_name)
CREATE TABLE tax_bills
(
bbl Int64,
owner_name String
)
ENGINE = MergeTree
PARTITION BY bbl
ORDER BY bbl;
Download the sample data from the taxbills.nyc website and put the data in the table:
curl -X GET 'http://taxbills.nyc/tax_bills_june15_bbls.csv' | \ clickhouse-client --input_format_allow_errors_num=10 \ --query="INSERT INTO test_database.tax_bills_nyc FORMAT CSV"
Now the data is ready in the ClickHouse, the next step is to set up the PostgreSQL side. First we need to create a FDW extension and a ClickHouse foreign server:
CREATE EXTENSION clickhouse_fdw; CREATE SERVER clickhouse_svr FOREIGN DATA WRAPPER clickhouse_fdw OPTIONS(dbname 'test_database');
By default the server will use http protocol. But we could use binary protocol:
CREATE SERVER clickhouse_svr FOREIGN DATA WRAPPER clickhouse_fdw OPTIONS(dbname 'test_database', driver 'binary');
Available parameters:
* dbname * host * port * driver
Now create user mapping and foreign tables:
CREATE USER MAPPING FOR CURRENT_USER SERVER clickhouse_svr
OPTIONS (user 'default', password '');
IMPORT FOREIGN SCHEMA "kk" FROM SERVER clickhouse_svr INTO public;
SELECT bbl,tbea,bav,insertion_date FROM tax_bills_nyc LIMIT 5;
bbl | tbea | bav | insertion_date
------------+-------+--------+---------------------
1001200009 | 72190 | 648900 | 2019-08-03 11:04:38
4000620001 | 8961 | 80550 | 2019-08-03 11:04:38
4157860094 | 13317 | 119700 | 2019-08-03 11:04:38
4123850237 | 50 | 450 | 2019-08-03 11:04:38
4103150163 | 2053 | 18450 | 2019-08-03 11:04:38
INSERT INTO tax_bills SELECT bbl, tbea from tax_bills_nyc LIMIT 100;
EXPLAIN VERBOSE SELECT bbl,tbea,bav,insertion_date FROM tax_bills_nyc LIMIT 5;
QUERY PLAN
--------------------------------------------------------------------------------------------
Limit (cost=0.00..0.00 rows=1 width=32)
Output: bbl, tbea, bav, insertion_date
-> Foreign Scan on public.tax_bills_nyc (cost=0.00..0.00 rows=0 width=32)
Output: bbl, tbea, bav, insertion_date
Remote SQL: SELECT bbl, tbea, bav, insertion_date FROM test_database.tax_bills_nyc
(5 rows)
Aggregate pushdown is a new feature of PostgreSQL FDW. There are currently very few foreign data wrappers that support aggregate pushdown: clickhouse_fdw is one of them. Planner decides which aggregate to pushdown or not. Here is an example for both cases.
postgres=# EXPLAIN VERBOSE SELECT count(bbl) FROM tax_bills_nyc LIMIT 5;
QUERY PLAN
---------------------------------------------------------------------------
Limit (cost=0.00..0.01 rows=1 width=8)
Output: (count(bbl))
-> Aggregate (cost=0.00..0.01 rows=1 width=8)
Output: count(bbl)
-> Foreign Scan on public.tax_bills_nyc (cost=0.00..0.00 rows=0 width=8)
Output: bbl, owner_name, address, tax_class, tax_rate, emv, tbea, bav, tba, property_tax, condonumber, condo, insertion_date
Remote SQL: SELECT bbl FROM test_database.tax_bills_nyc
(7 rows)
EXPLAIN VERBOSE SELECT count(bbl) FROM tax_bills_nyc;
QUERY PLAN
------------------------------------------------------------------
Foreign Scan (cost=1.00..-1.00 rows=1000 width=8)
Output: (count(bbl))
Relations: Aggregate on (tax_bills_nyc)
Remote SQL: SELECT count(bbl) FROM test_database.tax_bills_nyc
(4 rows)
Join pushdown is also a very new feature of PostgreSQL FDW’s. The clickhouse_fdw also supports join pushdown.
EXPLAIN VERBOSE SELECT t2.bbl, t2.owner_name, t1.bav FROM tax_bills_nyc t1 RIGHT OUTER JOIN tax_bills t2 ON (t1.bbl = t2.bbl);
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------
----
Foreign Scan (cost=1.00..-1.00 rows=1000 width=50)
Output: t2.bbl, t2.owner_name, t1.bav
Relations: (tax_bills t2) LEFT JOIN (tax_bills_nyc t1)
Remote SQL: SELECT r2.bbl, r2.owner_name, r1.bav FROM test_database.tax_bills r2 ALL LEFT JOIN test_database.tax_bills_nyc r1 ON (((r1.bbl = r2.bbl
)))
(4 rows)
For columns where you need Merge prefix in aggregations just add AggregateFunction option with aggregation function name. Example:
ALTER TABLE table ALTER COLUMN b OPTIONS (SET AggregateFunction 'count');
Or use IMPORT SCHEMA for automatic definitions.