A Look at ClickHouse: A New Open Source Columnar Database
ClickHouse is an open source columnar database that promises fast scans that can be used for real-time queries. See how it works, complete with benchmarks against Spark.
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Join For FreeI’ve decided to try ClickHouse, an open source column-oriented database management system developed by Yandex (it currently powers Yandex.Metrica, the world’s second-largest web analytics platform).
In my previous set of posts, I tested Apache Spark for big data analysis and used Wikipedia page statistics as a data source. I’ve used the same data as in the Apache Spark blog post: Wikipedia Page Counts. This allows me to compare ClickHouse’s performance to Spark’s.
I’ve spent some time testing ClickHouse for relatively large volumes of data (1.2Tb uncompressed). Here is a list of ClickHouse advantages and disadvantages that I saw.
ClickHouse Advantages
Parallel processing for a single query (utilizing multiple cores).
Distributed processing on multiple servers.
Very fast scans (see benchmarks below) that can be used for real-time queries.
Column storage is great for working with “wide” / “denormalized” tables (many columns).
Good compressionGood set of functions, including support for approximated calculations.
Different storage engines (disk storage format)Great for structural log/event data as well as time series data (engine MergeTree requires date field).
Index support (primary key only, not all storage engines).
Nice command line interface with user-friendly progress bar and formatting.
Here is a full list of ClickHouse features.
ClickHouse Disadvantages
No real delete/update support, and no transactions (same as Spark and most of the big data systems).
No secondary keys (same as Spark and most of the big data systems).
Own protocol (no MySQL protocol support).
Limited SQL support, and the joins implementation is different. If you are migrating from MySQL or Spark, you will probably have to re-write all queries with joins.
No window functions.
And here is a full list of ClickHouse's limitations.
Group by In-Memory vs. On-Disk
Running out of memory is one of the potential problems you may encounter when working with large datasets in ClickHouse:
SELECT
min(toMonth(date)),
max(toMonth(date)),
path,
count(*),
sum(hits),
sum(hits) / count(*) AS hit_ratio
FROM wikistat
WHERE (project = 'en')
GROUP BY path
ORDER BY hit_ratio DESC
LIMIT 10
↖ Progress: 1.83 billion rows, 85.31 GB (68.80 million rows/s., 3.21 GB/s.) ██████████▋ 6%Received exception from server:
Code: 241. DB::Exception: Received from localhost:9000, 127.0.0.1.
DB::Exception: Memory limit (for query) exceeded: would use 9.31 GiB (attempt to allocate chunk of 1048576 bytes), maximum: 9.31 GiB:
(while reading column hits): By default, ClickHouse limits the amount of memory for group by (it uses a hash table for group by). This is easily fixed – if you have free memory, increase this parameter:
SET max_memory_usage = 128000000000; #128GIf you don’t have that much memory available, ClickHouse can “spill” data to disk by setting this:
set max_bytes_before_external_group_by=20000000000; #20G
set max_memory_usage=40000000000; #40GAccording to the documentation, if you need to use max_bytes_before_external_group_by it is recommended to setmax_memory_usage to be ~2x of the size of max_bytes_before_external_group_by.
(The reason for this is that the aggregation is performed in two phases: (1) reading and building an intermediate data, and (2) merging the intermediate data. The spill to disk can only happen during the first phase. If there won’t be spill, ClickHouse might need the same amount of RAM for stage 1 and 2.)
Benchmarks: ClickHouse vs. Spark
Both ClickHouse and Spark can be distributed. However, for the purpose of this test I’ve run a single node for both ClickHouse and Spark. The results are quite impressive.
Benchmark Summary
| Size / compression | Spark v. 2.0.2 | ClickHouse |
| Data storage format | Parquet, compressed: snappy | Internal storage, compressed |
| Size (uncompressed: 1.2TB) | 395G | 212G |
| Test | Spark v. 2.0.2 | ClickHouse | Diff |
| Query 1: count (warm) | 7.37 sec (no disk IO) | 6.61 sec | ~same |
| Query 2: simple group (warm) | 792.55 sec (no disk IO) | 37.45 sec | 21x better |
| Query 3: complex group by | 2522.9 sec | 398.55 sec | 6.3x better |
ClickHouse vs. MySQL
I wanted to see how ClickHouse compared to MySQL. Obviously, we can’t compare some workloads. For example:
- Storing terabytes of data and querying (“crunching” would be a better word here) data without an index. It would take weeks (or even months) to load data and build the indexes. That is a much more suitable workload for ClickHouse or Spark.
- Real-time updates / OLTP. ClickHouse does not support real-time updates / deletes.
Usually, big data systems provide us with real-time queries. Systems based on map/reduce (i.e., Hive on top of HDFS) are just too slow for real-time queries, as it takes a long time to initialize the map/reduce job and send the code to all nodes.
Potentially, you can use ClickHouse for real-time queries. It does not support secondary indexes, however. This means it will probably scan lots of rows, but it can do it very quickly.
To do this test, I’m using the data from the Percona Monitoring and Management system. The table I’m using has 150 columns, so it is good for column storage. The size in MySQL is ~250G:
mysql> show table status like 'query_class_metrics'G
*************************** 1. row ***************************
Name: query_class_metrics
Engine: InnoDB
Version: 10
Row_format: Compact
Rows: 364184844
Avg_row_length: 599
Data_length: 218191888384
Max_data_length: 0
Index_length: 18590056448
Data_free: 6291456
Auto_increment: 416994305Scanning the whole table is significantly faster in ClickHouse. Retrieving just ten rows by key is faster in MySQL (especially from memory).
But what if we only need to scan limited amount of rows and do a group by? In this case, ClickHouse may be faster. Here is the example (real query used to create sparklines):
MySQL
SELECT
(1480888800 - UNIX_TIMESTAMP(start_ts)) / 11520 as point,
FROM_UNIXTIME(1480888800 - (SELECT point) * 11520) AS ts,
COALESCE(SUM(query_count), 0) / 11520 AS query_count_per_sec,
COALESCE(SUM(Query_time_sum), 0) / 11520 AS query_time_sum_per_sec,
COALESCE(SUM(Lock_time_sum), 0) / 11520 AS lock_time_sum_per_sec,
COALESCE(SUM(Rows_sent_sum), 0) / 11520 AS rows_sent_sum_per_sec,
COALESCE(SUM(Rows_examined_sum), 0) / 11520 AS rows_examined_sum_per_sec
FROM query_class_metrics
WHERE query_class_id = 7 AND instance_id = 1259 AND (start_ts >= '2014-11-27 00:00:00'
AND start_ts < '2014-12-05 00:00:00')
GROUP BY point;
...
61 rows in set (0.10 sec)
# Query_time: 0.101203 Lock_time: 0.000407 Rows_sent: 61 Rows_examined: 11639 Rows_affected: 0
explain SELECT ...
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: query_class_metrics
partitions: NULL
type: range
possible_keys: agent_class_ts,agent_ts
key: agent_class_ts
key_len: 12
ref: NULL
rows: 21686
filtered: 100.00
Extra: Using index condition; Using temporary; Using filesort
*************************** 2. row ***************************
id: 2
select_type: DEPENDENT SUBQUERY
table: NULL
partitions: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
filtered: NULL
Extra: No tables used
2 rows in set, 2 warnings (0.00 sec)It is relatively fast.
ClickHouse
Note: Some functions are different, so we will have to rewrite the query.
SELECT
intDiv(1480888800 - toRelativeSecondNum(start_ts), 11520) AS point,
toDateTime(1480888800 - (point * 11520)) AS ts,
SUM(query_count) / 11520 AS query_count_per_sec,
SUM(Query_time_sum) / 11520 AS query_time_sum_per_sec,
SUM(Lock_time_sum) / 11520 AS lock_time_sum_per_sec,
SUM(Rows_sent_sum) / 11520 AS rows_sent_sum_per_sec,
SUM(Rows_examined_sum) / 11520 AS rows_examined_sum_per_sec,
SUM(Rows_affected_sum) / 11520 AS rows_affected_sum_per_sec
FROM query_class_metrics
WHERE (query_class_id = 7) AND (instance_id = 1259) AND ((start_ts >= '2014-11-27 00:00:00')
AND (start_ts < '2014-12-05 00:00:00'))
GROUP BY point;
61 rows in set. Elapsed: 0.017 sec. Processed 270.34 thousand rows, 14.06 MB (15.73 million rows/s., 817.98 MB/s.)As we can see, even though ClickHouse scans more rows (270K vs. 11K – over 20x more) it is faster to execute the ClickHouse query (0.10 seconds in MySQL compared to 0.01 second in ClickHouse). The column store format helps a lot here, as MySQL has to read all 150 columns (stored inside InnoDB pages) and ClickHouse only needs to read seven columns.
Wikipedia Trending Article of the Month
Inspired by the article about finding trending topics using Google Books n-grams data, I decided to implement the same algorithm on top of the Wikipedia page visit statistics data. My goal here is to find the “article trending this month,” which has significantly more visits this month compared to the previous month. As I was implementing the algorithm, I came across another ClickHouse limitation: join syntax is limited. In ClickHouse, you can only do join with the “using” keyword. This means that the fields you’re joining need to have the same name. If the field name is different, we have to use a subquery.
Below is an example.
First, create a temporary table to aggregate the visits per month per page:
CREATE TABLE wikistat_by_month ENGINE = Memory AS
SELECT
path,
mon,
sum(hits) / total_hits AS ratio
FROM
(
SELECT
path,
hits,
toMonth(date) AS mon
FROM wikistat
WHERE (project = 'en') AND (lower(path) NOT LIKE '%special%') AND (lower(path) NOT LIKE '%page%') AND (lower(path) NOT LIKE '%test%') AND (lower(path) NOT LIKE '%wiki%') AND (lower(path) NOT LIKE '%index.html%')
) AS a
ANY INNER JOIN
(
SELECT
toMonth(date) AS mon,
sum(hits) AS total_hits
FROM wikistat
WHERE (project = 'en') AND (lower(path) NOT LIKE '%special%') AND (lower(path) NOT LIKE '%page%') AND (lower(path) NOT LIKE '%test%') AND (lower(path) NOT LIKE '%wiki%') AND (lower(path) NOT LIKE '%index.html%')
GROUP BY toMonth(date)
) AS b USING (mon)
GROUP BY
path,
mon,
total_hits
ORDER BY ratio DESC
Ok.
0 rows in set. Elapsed: 543.607 sec. Processed 53.77 billion rows, 2.57 TB (98.91 million rows/s., 4.73 GB/s.)Second, calculate the actual list:
SELECT
path,
mon + 1,
a_ratio AS ratio,
a_ratio / b_ratio AS increase
FROM
(
SELECT
path,
mon,
ratio AS a_ratio
FROM wikistat_by_month
WHERE ratio > 0.0001
) AS a
ALL INNER JOIN
(
SELECT
path,
CAST((mon - 1) AS UInt8) AS mon,
ratio AS b_ratio
FROM wikistat_by_month
WHERE ratio > 0.0001
) AS b USING (path, mon)
WHERE (mon > 0) AND (increase > 2)
ORDER BY
mon ASC,
increase DESC
LIMIT 100
┌─path───────────────────────────────────────────────┬─plus(mon, 1)─┬──────────────────ratio─┬───────────increase─┐
│ Heath_Ledger │ 2 │ 0.0008467223172121601 │ 6.853825241458039 │
│ Cloverfield │ 2 │ 0.0009372609760313347 │ 3.758937474560766 │
│ The_Dark_Knight_(film) │ 2 │ 0.0003508532447770276 │ 2.8858100355450484 │
│ Scientology │ 2 │ 0.0003300109101992719 │ 2.52497180013816 │
│ Barack_Obama │ 3 │ 0.0005786473399980557 │ 2.323409928527576 │
│ Canine_reproduction │ 3 │ 0.0004836300843539438 │ 2.0058985801174662 │
│ Iron_Man │ 6 │ 0.00036261003907049 │ 3.5301196568303888 │
│ Iron_Man_(film) │ 6 │ 0.00035634745198422497 │ 3.3815325090507193 │
│ Grand_Theft_Auto_IV │ 6 │ 0.0004036713142943461 │ 3.2112732008504885 │
│ Indiana_Jones_and_the_Kingdom_of_the_Crystal_Skull │ 6 │ 0.0002856570195547951 │ 2.683443198030021 │
│ Tha_Carter_III │ 7 │ 0.00033954377342889735 │ 2.820114216429247 │
│ EBay │ 7 │ 0.0006575000133427979 │ 2.5483158977946787 │
│ Bebo │ 7 │ 0.0003958340022793501 │ 2.3260912792668162 │
│ Facebook │ 7 │ 0.001683658379576915 │ 2.16460972864883 │
│ Yahoo!_Mail │ 7 │ 0.0002190640575012259 │ 2.1075879062784737 │
│ MySpace │ 7 │ 0.001395608643577507 │ 2.103263660621813 │
│ Gmail │ 7 │ 0.0005449834079575953 │ 2.0675919337716757 │
│ Hotmail │ 7 │ 0.0009126863121737026 │ 2.052471735190232 │
│ Google │ 7 │ 0.000601645849087389 │ 2.0155448612416644 │
│ Barack_Obama │ 7 │ 0.00027336526076130943 │ 2.0031305241832302 │
│ Facebook │ 8 │ 0.0007778115183044431 │ 2.543477658022576 │
│ MySpace │ 8 │ 0.000663544314346641 │ 2.534512981232934 │
│ Two-Face │ 8 │ 0.00026975137404447024 │ 2.4171743959768803 │
│ YouTube │ 8 │ 0.001482456447101451 │ 2.3884527929836152 │
│ Hotmail │ 8 │ 0.00044467667764940547 │ 2.2265750216262954 │
│ The_Dark_Knight_(film) │ 8 │ 0.0010482536106662156 │ 2.190078096294301 │
│ Google │ 8 │ 0.0002985028319919154 │ 2.0028812075734637 │
│ Joe_Biden │ 9 │ 0.00045067411455437264 │ 2.692262662620829 │
│ The_Dark_Knight_(film) │ 9 │ 0.00047863754833213585 │ 2.420864550676665 │
│ Sarah_Palin │ 10 │ 0.0012459220318907518 │ 2.607063205782761 │
│ Barack_Obama │ 12 │ 0.0034487235202817087 │ 15.615409029600414 │
│ George_W._Bush │ 12 │ 0.00042708730873936023 │ 3.6303098900144937 │
│ Fallout_3 │ 12 │ 0.0003568429236849597 │ 2.6193094036745155 │
└────────────────────────────────────────────────────┴──────────────┴────────────────────────┴────────────────────┘
34 rows in set. Elapsed: 1.062 sec. Processed 1.22 billion rows, 49.03 GB (1.14 billion rows/s., 46.16 GB/s.)Their response time is really good, considering the amount of data it needed to scan (the first query scanned 2.57 TB of data).
Conclusion
The ClickHouse column-oriented database looks promising for data analytics, as well as for storing and processing structural event data and time series data. ClickHouse can be ~10x faster than Spark for some workloads.
Appendix: Benchmark Details
Hardware
- CPU: 24xIntel(R) Xeon(R) CPU L5639 @ 2.13GHz (physical = 2, cores = 12, virtual = 24, hyperthreading = yes)
- Disk: 2 consumer grade SSD in software RAID 0 (mdraid)
Query 1
select count(*) from wikistatClickHouse
:) select count(*) from wikistat;
SELECT count(*)
FROM wikistat
┌─────count()─┐
│ 26935251789 │
└─────────────┘
1 rows in set. Elapsed: 6.610 sec. Processed 26.88 billion rows, 53.77 GB (4.07 billion rows/s., 8.13 GB/s.) Spark
spark-sql> select count(*) from wikistat;
26935251789
Time taken: 7.369 seconds, Fetched 1 row(s)Query 2
select count(*), month(dt) as mon
from wikistat where year(dt)=2008
and month(dt) between 1 and 10
group by month(dt)
order by month(dt);ClickHouse
:) select count(*), toMonth(date) as mon from wikistat
where toYear(date)=2008 and toMonth(date) between 1 and 10 group by mon;
SELECT
count(*),
toMonth(date) AS mon
FROM wikistat
WHERE (toYear(date) = 2008) AND ((toMonth(date) >= 1) AND (toMonth(date) <= 10))
GROUP BY mon
┌────count()─┬─mon─┐
│ 2100162604 │ 1 │
│ 1969757069 │ 2 │
│ 2081371530 │ 3 │
│ 2156878512 │ 4 │
│ 2476890621 │ 5 │
│ 2526662896 │ 6 │
│ 2489723244 │ 7 │
│ 2480356358 │ 8 │
│ 2522746544 │ 9 │
│ 2614372352 │ 10 │
└────────────┴─────┘
10 rows in set. Elapsed: 37.450 sec. Processed 23.37 billion rows, 46.74 GB (623.97 million rows/s., 1.25 GB/s.) Spark
spark-sql> select count(*), month(dt) as mon from wikistat where year(dt)=2008 and month(dt) between 1 and 10 group by month(dt) order by month(dt);
2100162604 1
1969757069 2
2081371530 3
2156878512 4
2476890621 5
2526662896 6
2489723244 7
2480356358 8
2522746544 9
2614372352 10
Time taken: 792.552 seconds, Fetched 10 row(s)Query 3
SELECT
path,
count(*),
sum(hits) AS sum_hits,
round(sum(hits) / count(*), 2) AS hit_ratio
FROM wikistat
WHERE project = 'en'
GROUP BY path
ORDER BY sum_hits DESC
LIMIT 100;ClickHouse
:) SELECT
:-] path,
:-] count(*),
:-] sum(hits) AS sum_hits,
:-] round(sum(hits) / count(*), 2) AS hit_ratio
:-] FROM wikistat
:-] WHERE (project = 'en')
:-] GROUP BY path
:-] ORDER BY sum_hits DESC
:-] LIMIT 100;
SELECT
path,
count(*),
sum(hits) AS sum_hits,
round(sum(hits) / count(*), 2) AS hit_ratio
FROM wikistat
WHERE project = 'en'
GROUP BY path
ORDER BY sum_hits DESC
LIMIT 100
┌─path────────────────────────────────────────────────┬─count()─┬───sum_hits─┬─hit_ratio─┐
│ Special:Search │ 44795 │ 4544605711 │ 101453.41 │
│ Main_Page │ 31930 │ 2115896977 │ 66266.74 │
│ Special:Random │ 30159 │ 533830534 │ 17700.54 │
│ Wiki │ 10237 │ 40488416 │ 3955.11 │
│ Special:Watchlist │ 38206 │ 37200069 │ 973.67 │
│ YouTube │ 9960 │ 34349804 │ 3448.78 │
│ Special:Randompage │ 8085 │ 28959624 │ 3581.9 │
│ Special:AutoLogin │ 34413 │ 24436845 │ 710.11 │
│ Facebook │ 7153 │ 18263353 │ 2553.24 │
│ Wikipedia │ 23732 │ 17848385 │ 752.08 │
│ Barack_Obama │ 13832 │ 16965775 │ 1226.56 │
│ index.html │ 6658 │ 16921583 │ 2541.54 │
…
100 rows in set. Elapsed: 398.550 sec. Processed 26.88 billion rows, 1.24 TB (67.45 million rows/s., 3.10 GB/s.)Spark
spark-sql> SELECT
> path,
> count(*),
> sum(hits) AS sum_hits,
> round(sum(hits) / count(*), 2) AS hit_ratio
> FROM wikistat
> WHERE (project = 'en')
> GROUP BY path
> ORDER BY sum_hits DESC
> LIMIT 100;
...
Time taken: 2522.903 seconds, Fetched 100 row(s)Published at DZone with permission of Alexander Rubin, DZone MVB. See the original article here.
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