ClickHouse is an open source, columnar-oriented database.
It has a sweet spot where 100s of analysts can query non-rolled-up / cubed data quickly, even when tens of billions of new records a day are introduced.
The infrastructure costs supporting such a system can come under $100K / year, and potentially half of that if usage permits.
Yandex Metricas ClickHouse installation at one point had 10s of trillions of records.
Beyond Yandex, ClickHouse has also seen success recently at Bloomberg and CloudFlare.
Two years ago I benchmarked the database using a single machine and it came out as the fastest free database software Id seen complete the benchmark.
Since then, theyve continued to add features including support for Kafka, HDFS and ZStandard compression.
Last year they added support for stacking compression methods so that delta-of-delta compression became possible.
When compressing time series data, gauge values can compress well with delta encoding but counters will do better with delta-of-delta encoding.
Good compression has been a key to ClickHouses performance.
ClickHouse is made up of 170K lines of C++ code when excluding 3rd-party libraries and is one of the smaller distributed database codebases.
For contrast, SQLite doesnt support distribution and has 235K lines of C code.
As of this writing, 207 engineers have contributed to ClickHouse and the rate of commits has been accelerating for some time.
In March of 2017, ClickHouse began maintaining a CHANGELOG as an easy way to keep track of developments.
Theyve also broken up the monolithic documentation file into a hierarchy of Markdown-based files.
Issues and features for the software are tracked via GitHub and overall this software has become much more approachable in the past few years.
In this post Im going to take a look at ClickHouses clustered performance on AWS EC2 using 36-core CPUs and NVMe storage.
Launching an AWS EC2 Cluster Ill be using three c5d.
9xlarge EC2 instances for this post.
They each contain 36 vCPUs, 72 GB of RAM, 900 GB of NVMe SSD storage and support 10 Gigabit networking.
They cost $1.
962 / hour each in eu-west-1 when launched on-demand.
Ill be using Ubuntu Server 16.
04 LTS for the operating system.
The firewall is setup so each machine can communicate between one another without restrictions but only my IPv4 address is white-listed to SSH into the cluster.
NVMe Storage, Up and Running On each of the servers Ill create an EXT4-formatted file system on the NVMe storage for ClickHouse to work off of.
$ sudo mkfs -t ext4 /dev/nvme1n1 $ sudo mkdir /ch $ sudo mount /dev/nvme1n1 /ch Once thats setup you can see its mount point and that 783 GB of capacity is available on each of the systems.
$ lsblk NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT loop0 7:0 0 87.
9M 1 loop /snap/core/5742 loop1 7:1 0 16.
5M 1 loop /snap/amazon-ssm-agent/784 nvme0n1 259:1 0 8G 0 disk └─nvme0n1p1 259:2 0 8G 0 part / nvme1n1 259:0 0 838.
2G 0 disk /ch $ df -h Filesystem Size Used Avail Use% Mounted on udev 35G 0 35G 0% /dev tmpfs 6.
9G 8.
8M 6.
9G 1% /run /dev/nvme0n1p1 7.
7G 967M 6.
8G 13% / tmpfs 35G 0 35G 0% /dev/shm tmpfs 5.
0M 0 5.
0M 0% /run/lock tmpfs 35G 0 35G 0% /sys/fs/cgroup /dev/loop0 88M 88M 0 100% /snap/core/5742 /dev/loop1 17M 17M 0 100% /snap/amazon-ssm-agent/784 tmpfs 6.
9G 0 6.
9G 0% /run/user/1000 /dev/nvme1n1 825G 73M 783G 1% /ch The dataset Ill be using in this benchmark is a data dump Ive produced of 1.
1 billion taxi trips conducted in New York City over a six year period.
The Billion Taxi Rides in Redshift blog post goes into detail on how I put this dataset together.
Theyre stored on AWS S3 so Ill configure the AWS CLI with my access and secret keys.
$ sudo apt update $ sudo apt install awscli $ aws configure Ill set the clients concurrent requests limit to 100 so the files download quicker than they would with stock settings.
$ aws configure set default.
s3.
max_concurrent_requests 100 Ill download taxi ride dataset off of AWS S3 and store it on the NVMe drive on the first server.
This dataset is ~104 GB when in GZIP-compressed, CSV format.
$ sudo mkdir -p /ch/csv $ sudo chown -R ubuntu /ch/csv $ aws s3 sync s3://<bucket>/csv /ch/csv Installing ClickHouse Ill first install a few software installation utilities for Java 8.
$ sudo apt install software-properties-common python-software-properties Ill then install Oracles Java 8 distribution as its needed to run Apache ZooKeeper, a prerequisite of a distributed ClickHouse setup.
$ sudo add-apt-repository ppa:webupd8team/java $ sudo apt update $ sudo apt install oracle-java8-installer Ill then use Ubuntus package management to install ClickHouse 18.
16.
1 and ZooKeeper on all three machines.
$ sudo apt-key adv –keyserver hkp://keyserver.
ubuntu.
com:80 –recv E0C56BD4 $ echo "deb http://repo.
yandex.
ru/clickhouse/deb/stable/ main/" | sudo tee /etc/apt/sources.
list.
d/clickhouse.
list $ sudo apt-get update $ sudo apt install clickhouse-client clickhouse-server zookeeperd Ill create a data directory for ClickHouse as well as some configuration overrides on all three servers.
$ sudo mkdir /ch/clickhouse $ sudo chown -R clickhouse /ch/clickhouse $ sudo mkdir -p /etc/clickhouse-server/conf.
d $ sudo vi /etc/clickhouse-server/conf.
d/taxis.
conf These are the configuration overrides Ill be using.
<?xml version="1.
0"?> <yandex> <listen_host>0.
0.
0.
0</listen_host> <path>/ch/clickhouse/</path> <remote_servers> <perftest_1shards_3replicas> <shard> <replica> <host>172.
30.
2.
200</host> <port>9000</port> </replica> <replica> <host>172.
30.
2.
214</host> <port>9000</port> </replica> <replica> <host>172.
30.
2.
174</host> <port>9000</port> </replica> </shard> </perftest_1shards_3replicas> </remote_servers> <zookeeper-servers> <node> <host>172.
30.
2.
200</host> <port>2181</port> </node> <node> <host>172.
30.
2.
214</host> <port>2181</port> </node> <node> <host>172.
30.
2.
174</host> <port>2181</port> </node> </zookeeper-servers> <macros> <shard>01</shard> <replica>01</replica> </macros> </yandex> Ill then launch ZooKeeper and the ClickHouse Server on all three machines.
$ sudo /etc/init.
d/zookeeper start $ sudo service clickhouse-server start Loading Data into ClickHouse On the first server Ill create a trips table that will hold the taxi trips dataset using the Log engine.
$ clickhouse-client –host=0.
0.
0.
0 CREATE TABLE trips ( trip_id UInt32, vendor_id String, pickup_datetime DateTime, dropoff_datetime Nullable(DateTime), store_and_fwd_flag Nullable(FixedString(1)), rate_code_id Nullable(UInt8), pickup_longitude Nullable(Float64), pickup_latitude Nullable(Float64), dropoff_longitude Nullable(Float64), dropoff_latitude Nullable(Float64), passenger_count Nullable(UInt8), trip_distance Nullable(Float64), fare_amount Nullable(Float32), extra Nullable(Float32), mta_tax Nullable(Float32), tip_amount Nullable(Float32), tolls_amount Nullable(Float32), ehail_fee Nullable(Float32), improvement_surcharge Nullable(Float32), total_amount Nullable(Float32), payment_type Nullable(String), trip_type Nullable(UInt8), pickup Nullable(String), dropoff Nullable(String), cab_type Nullable(String), precipitation Nullable(Int8), snow_depth Nullable(Int8), snowfall Nullable(Int8), max_temperature Nullable(Int8), min_temperature Nullable(Int8), average_wind_speed Nullable(Int8), pickup_nyct2010_gid Nullable(Int8), pickup_ctlabel Nullable(String), pickup_borocode Nullable(Int8), pickup_boroname Nullable(String), pickup_ct2010 Nullable(String), pickup_boroct2010 Nullable(String), pickup_cdeligibil Nullable(FixedString(1)), pickup_ntacode Nullable(String), pickup_ntaname Nullable(String), pickup_puma Nullable(String), dropoff_nyct2010_gid Nullable(UInt8), dropoff_ctlabel Nullable(String), dropoff_borocode Nullable(UInt8), dropoff_boroname Nullable(String), dropoff_ct2010 Nullable(String), dropoff_boroct2010 Nullable(String), dropoff_cdeligibil Nullable(String), dropoff_ntacode Nullable(String), dropoff_ntaname Nullable(String), dropoff_puma Nullable(String) ) ENGINE = Log; Ill then decompress and load each of the CSV files into the trips table.
The following completed in 55 minutes and 10 seconds.
The data directory was 134 GB in size following this operation.
$ time (for FILENAME in /ch/csv/trips_x*.
csv.
gz; do gunzip -c $FILENAME | clickhouse-client –host=0.
0.
0.
0 –query="INSERT INTO trips FORMAT CSV" done) The import rate was 155 MB/s of uncompressed CSV content.
I suspect this was due to a bottleneck with GZIP decompression.
It might have been quicker to decompress all the gzip files in parallel using xargs and then load in the decompressed data.
Below is what glances was reporting during the CSV import process.
$ sudo apt install glances $ sudo glances ip-172-30-2-200 (Ubuntu 16.
04 64bit / Linux 4.
4.
0-1072-aws) Uptime: 0:11:42 CPU 8.
2% nice: 0.
0% LOAD 36-core MEM 9.
8% active: 5.
20G SWAP 0.
0% user: 6.
0% irq: 0.
0% 1 min: 2.
24 total: 68.
7G inactive: 61.
0G total: 0 system: 0.
9% iowait: 1.
3% 5 min: 1.
83 used: 6.
71G buffers: 66.
4M used: 0 idle: 91.
8% steal: 0.
0% 15 min: 1.
01 free: 62.
0G cached: 61.
6G free: 0 NETWORK Rx/s Tx/s TASKS 370 (507 thr), 2 run, 368 slp, 0 oth sorted automatically by cpu_percent, flat view ens5 136b 2Kb lo 343Mb 343Mb CPU% MEM% VIRT RES PID USER NI S TIME+ IOR/s IOW/s Command 100.
4 1.
5 1.
65G 1.
06G 9909 ubuntu 0 S 1:01.
33 0 0 clickhouse-client –host=0.
0.
0.
0 –query=INSERT INTO trips FORMAT CSV DISK I/O R/s W/s 85.
1 0.
0 4.
65M 708K 9908 ubuntu 0 R 0:50.
60 32M 0 gzip -d -c /ch/csv/trips_xac.
csv.
gz loop0 0 0 54.
9 5.
1 8.
14G 3.
49G 8091 clickhous 0 S 1:44.
23 0 45M /usr/bin/clickhouse-server –config=/etc/clickhouse-server/config.
xml loop1 0 0 4.
5 0.
0 0 0 319 root 0 S 0:07.
50 1K 0 kworker/u72:2 nvme0n1 0 3K 2.
3 0.
0 91.
1M 28.
9M 9912 root 0 R 0:01.
56 0 0 /usr/bin/python3 /usr/bin/glances nvme0n1p1 0 3K 0.
3 0.
0 0 0 960 root -20 S 0:00.
10 0 0 kworker/28:1H nvme1n1 32.
1M 495M 0.
3 0.
0 0 0 1058 root -20 S 0:00.
90 0 0 kworker/23:1H Ill first free up some space on the NVMe drive by removing the source CSV files before continuing.
$ sudo rm -fr /ch/csv Converting into Columnar Form ClickHouses Log engine will store data in a row-centric format.
In order to query the data faster Ill convert it into a columnar-centric format using the MergeTree engine.
$ clickhouse-client –host=0.
0.
0.
0 The following completed in 43 minutes and 56 seconds.
The data directory was 347 GB in size following this operation.
CREATE TABLE trips_mergetree ENGINE = MergeTree(pickup_date, pickup_datetime, 8192) AS SELECT trip_id, CAST(vendor_id AS Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14)) AS vendor_id, toDate(pickup_datetime) AS pickup_date, ifNull(pickup_datetime, toDateTime(0)) AS pickup_datetime, toDate(dropoff_datetime) AS dropoff_date, ifNull(dropoff_datetime, toDateTime(0)) AS dropoff_datetime, assumeNotNull(store_and_fwd_flag) AS store_and_fwd_flag, assumeNotNull(rate_code_id) AS rate_code_id, assumeNotNull(pickup_longitude) AS pickup_longitude, assumeNotNull(pickup_latitude) AS pickup_latitude, assumeNotNull(dropoff_longitude) AS dropoff_longitude, assumeNotNull(dropoff_latitude) AS dropoff_latitude, assumeNotNull(passenger_count) AS passenger_count, assumeNotNull(trip_distance) AS trip_distance, assumeNotNull(fare_amount) AS fare_amount, assumeNotNull(extra) AS extra, assumeNotNull(mta_tax) AS mta_tax, assumeNotNull(tip_amount) AS tip_amount, assumeNotNull(tolls_amount) AS tolls_amount, assumeNotNull(ehail_fee) AS ehail_fee, assumeNotNull(improvement_surcharge) AS improvement_surcharge, assumeNotNull(total_amount) AS total_amount, assumeNotNull(payment_type) AS payment_type_, assumeNotNull(trip_type) AS trip_type, pickup AS pickup, pickup AS dropoff, CAST(assumeNotNull(cab_type) AS Enum8('yellow' = 1, 'green' = 2)) AS cab_type, precipitation AS precipitation, snow_depth AS snow_depth, snowfall AS snowfall, max_temperature AS max_temperature, min_temperature AS min_temperature, average_wind_speed AS average_wind_speed, pickup_nyct2010_gid AS pickup_nyct2010_gid, pickup_ctlabel AS pickup_ctlabel, pickup_borocode AS pickup_borocode, pickup_boroname AS pickup_boroname, pickup_ct2010 AS pickup_ct2010, pickup_boroct2010 AS pickup_boroct2010, pickup_cdeligibil AS pickup_cdeligibil, pickup_ntacode AS pickup_ntacode, pickup_ntaname AS pickup_ntaname, pickup_puma AS pickup_puma, dropoff_nyct2010_gid AS dropoff_nyct2010_gid, dropoff_ctlabel AS dropoff_ctlabel, dropoff_borocode AS dropoff_borocode, dropoff_boroname AS dropoff_boroname, dropoff_ct2010 AS dropoff_ct2010, dropoff_boroct2010 AS dropoff_boroct2010, dropoff_cdeligibil AS dropoff_cdeligibil, dropoff_ntacode AS dropoff_ntacode, dropoff_ntaname AS dropoff_ntaname, dropoff_puma AS dropoff_puma FROM trips; This is what glances looked like during the operation: ip-172-30-2-200 (Ubuntu 16.
04 64bit / Linux 4.
4.
0-1072-aws) Uptime: 1:06:09 CPU 10.
3% nice: 0.
0% LOAD 36-core MEM 16.
1% active: 13.
3G SWAP 0.
0% user: 7.
9% irq: 0.
0% 1 min: 1.
87 total: 68.
7G inactive: 52.
8G total: 0 system: 1.
6% iowait: 0.
8% 5 min: 1.
76 used: 11.
1G buffers: 71.
8M used: 0 idle: 89.
7% steal: 0.
0% 15 min: 1.
95 free: 57.
6G cached: 57.
2G free: 0 NETWORK Rx/s Tx/s TASKS 367 (523 thr), 1 run, 366 slp, 0 oth sorted automatically by cpu_percent, flat view ens5 1Kb 8Kb lo 2Kb 2Kb CPU% MEM% VIRT RES PID USER NI S TIME+ IOR/s IOW/s Command 241.
9 12.
8 20.
7G 8.
78G 8091 clickhous 0 S 30:36.
73 34M 125M /usr/bin/clickhouse-server –config=/etc/clickhouse-server/config.
xml DISK I/O R/s W/s 2.
6 0.
0 90.
4M 28.
3M 9948 root 0 R 1:18.
53 0 0 /usr/bin/python3 /usr/bin/glances loop0 0 0 1.
3 0.
0 0 0 203 root 0 S 0:09.
82 0 0 kswapd0 loop1 0 0 0.
3 0.
1 315M 61.
3M 15701 ubuntu 0 S 0:00.
40 0 0 clickhouse-client –host=0.
0.
0.
0 nvme0n1 0 3K 0.
3 0.
0 0 0 7 root 0 S 0:00.
83 0 0 rcu_sched nvme0n1p1 0 3K 0.
0 0.
0 0 0 142 root 0 S 0:00.
22 0 0 migration/27 nvme1n1 25.
8M 330M 0.
0 0.
0 59.
7M 1.
79M 2764 ubuntu 0 S 0:00.
00 0 0 (sd-pam) In the last benchmark several columns were cast and re-computed.
I found a number of those functions no longer worked properly on this dataset.
In order to get around this I removed the offending functions and loaded in the data without casting into more granular data types.
Distributing Data Across the Cluster Ill be distributing the data across all three nodes in the cluster.
To start, Ill create the table below on all three machines.
$ clickhouse-client –host=0.
0.
0.
0 CREATE TABLE trips_mergetree_third ( trip_id UInt32, vendor_id String, pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime Nullable(DateTime), store_and_fwd_flag Nullable(FixedString(1)), rate_code_id Nullable(UInt8), pickup_longitude Nullable(Float64), pickup_latitude Nullable(Float64), dropoff_longitude Nullable(Float64), dropoff_latitude Nullable(Float64), passenger_count Nullable(UInt8), trip_distance Nullable(Float64), fare_amount Nullable(Float32), extra Nullable(Float32), mta_tax Nullable(Float32), tip_amount Nullable(Float32), tolls_amount Nullable(Float32), ehail_fee Nullable(Float32), improvement_surcharge Nullable(Float32), total_amount Nullable(Float32), payment_type Nullable(String), trip_type Nullable(UInt8), pickup Nullable(String), dropoff Nullable(String), cab_type Nullable(String), precipitation Nullable(Int8), snow_depth Nullable(Int8), snowfall Nullable(Int8), max_temperature Nullable(Int8), min_temperature Nullable(Int8), average_wind_speed Nullable(Int8), pickup_nyct2010_gid Nullable(Int8), pickup_ctlabel Nullable(String), pickup_borocode Nullable(Int8), pickup_boroname Nullable(String), pickup_ct2010 Nullable(String), pickup_boroct2010 Nullable(String), pickup_cdeligibil Nullable(FixedString(1)), pickup_ntacode Nullable(String), pickup_ntaname Nullable(String), pickup_puma Nullable(String), dropoff_nyct2010_gid Nullable(UInt8), dropoff_ctlabel Nullable(String), dropoff_borocode Nullable(UInt8), dropoff_boroname Nullable(String), dropoff_ct2010 Nullable(String), dropoff_boroct2010 Nullable(String), dropoff_cdeligibil Nullable(String), dropoff_ntacode Nullable(String), dropoff_ntaname Nullable(String), dropoff_puma Nullable(String) ) ENGINE = MergeTree(pickup_date, pickup_datetime, 8192); Ill then make sure the first server can see all three nodes in the cluster.
SELECT * FROM system.
clusters WHERE cluster = 'perftest_1shards_3replicas' FORMAT Vertical; Row 1: ────── cluster: perftest_1shards_3replicas shard_num: 1 shard_weight: 1 replica_num: 1 host_name: 172.
30.
2.
200 host_address: 172.
30.
2.
200 port: 9000 is_local: 1 user: default default_database: Row 2: ────── cluster: perftest_1shards_3replicas shard_num: 1 shard_weight: 1 replica_num: 2 host_name: 172.
30.
2.
214 host_address: 172.
30.
2.
214 port: 9000 is_local: 1 user: default default_database: Row 3: ────── cluster: perftest_1shards_3replicas shard_num: 1 shard_weight: 1 replica_num: 3 host_name: 172.
30.
2.
174 host_address: 172.
30.
2.
174 port: 9000 is_local: 1 user: default default_database: Ill then define a new table on the first server thats based on the trips_mergetree_third schema and uses the Distributed engine.
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest_1shards_3replicas, default, trips_mergetree_third, rand()); Ill then copy the data out of the MergeTree-based table and onto all three servers.
The following completed in 58 minutes and 52 seconds.
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree; Following the above operation I gave ClickHouse 15 minutes to recede from its storage high-water mark.
The data directories ended up being 421 GB, 144 GB and 144 GB in size respectively on each of the three servers.
ClickHouse Cluster Benchmark The following were the fastest times I saw after running each query multiple times on the trips_mergetree_x3 table.
$ clickhouse-client –host=0.
0.
0.
0 The following completed in 2.
502 seconds.
SELECT cab_type, count(*) FROM trips_mergetree_x3 GROUP BY cab_type; The following completed in 1.
880 seconds.
SELECT passenger_count, avg(total_amount) FROM trips_mergetree_x3 GROUP BY passenger_count; The following completed in 1.
609 seconds.
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree_x3 GROUP BY passenger_count, year; The following completed in 2.
681 seconds.
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*) FROM trips_mergetree_x3 GROUP BY passenger_count, year, distance ORDER BY year, count(*) DESC; While running query 1 I could see the first servers CPU was at ~50% utilisation while the other machines remained somewhat idle.
This is a snippet from Glances: CPU% MEM% VIRT RES PID USER NI S TIME+ IOR/s IOW/s Command 1508.
2 1.
9 50.
7G 1.
32G 8091 clickhous 0 S 0:54.
98 0 3K /usr/bin/clickhouse-server –config=/etc/clickhouse-server/config.
xml The above struck me as odd.
I was expecting a single query to speed up as I scaled out horizontally.
To add to that I was expecting each queries to take longer than their previous siblings.
I decided to run the same queries on the MergeTree-based table which sits solely on the first server.
ClickHouse Single-Node Benchmark The following were the fastest times I saw after running each query multiple times on the trips_mergetree table.
The following completed in 0.
241 seconds.
SELECT cab_type, count(*) FROM trips_mergetree GROUP BY cab_type; The following completed in 0.
826 seconds.
SELECT passenger_count, avg(total_amount) FROM trips_mergetree GROUP BY passenger_count; The following completed in 1.
209 seconds.
SELECT passenger_count, toYear(pickup_date) AS year, count(*) FROM trips_mergetree GROUP BY passenger_count, year; The following completed in 1.
781 seconds.
SELECT passenger_count, toYear(pickup_date) AS year, round(trip_distance) AS distance, count(*) FROM trips_mergetree GROUP BY passenger_count, year, distance ORDER BY year, count(*) DESC; Thoughts on the Results This is the first time a free CPU-based database has managed to out-perform a GPU-based database in my benchmarks.
That GPU database has since undergone two revisions but nonetheless, the performance ClickHouse has found on a single node is very impressive.
That being said, there is an order of magnitude of overhead when running Query 1 on the distributed engine.
Im hoping Ive missed something in my research for this post because it would be good to see query times drop as I add more nodes to the cluster.
Ive come across many setups where a single query wont be able to consume an entire clusters resources but can run well concurrently with other queries.
Given the single-node performance Id consider ClickHouse for this sort of workload.
It would be nice to see ClickHouse evolve in such a way that storage and compute could somehow be disconnected so that they could scale independently.
The HDFS support that has been added in the last year could be a step towards this.
On the compute side, if a single query can be sped up as more nodes are added to the cluster then the future for this software will be very bright.
.