Prijevod članka pripremljen je posebno za studente predmeta
Prije dvije godine sam proveo
ClickHouse se sastoji od 170 linija C++ koda, isključujući biblioteke trećih strana, i jedna je od najmanjih baza koda za distribuirane baze podataka. Za usporedbu, SQLite ne podržava distribuciju i sastoji se od 235 linija C koda. U vrijeme pisanja ovog teksta, 207 inženjera je doprinijelo ClickHouseu, a stopa urezivanja se u posljednje vrijeme povećava.
U martu 2017. ClickHouse je počeo da diriguje
U ovom članku ću pogledati performanse ClickHouse klastera na AWS EC2 koristeći 36-jezgrene procesore i NVMe skladište.
AŽURIRANJE: Sedmicu nakon prvobitne objave ovog posta, ponovo sam pokrenuo test sa poboljšanom konfiguracijom i postigao mnogo bolje rezultate. Ovaj post je ažuriran kako bi odražavao ove promjene.
Pokretanje AWS EC2 klastera
Koristit ću tri c5d.9xlarge EC2 instance za ovaj post. Svaki od njih sadrži 36 vCPU-a, 72 GB RAM-a, 900 GB NVMe SSD memorije i podržava 10 Gigabitno umrežavanje. Oni koštaju 1,962 USD/sat svaki u eu-west-1 kada se lansiraju na zahtjev. Koristiću Ubuntu Server 16.04 LTS kao svoj operativni sistem.
Firewall je postavljen tako da svaka mašina može međusobno komunicirati bez ograničenja, a samo je moja IPv4 adresa na bijeloj listi SSH u klasteru.
NVMe pogon u operativnoj spremnosti
Da bi ClickHouse radio, kreiraću EXT4 sistem datoteka na NVMe drajvu na svakom od servera.
$ sudo mkfs -t ext4 /dev/nvme1n1
$ sudo mkdir /ch
$ sudo mount /dev/nvme1n1 /ch
Kada se sve podesi, možete vidjeti tačku montiranja i 783 GB slobodnog prostora na svakom sistemu.
$ 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
Skup podataka koji ću koristiti u ovom testu je deponija podataka koju sam generirao iz 1.1 milijarde vožnja taksijem u New Yorku tokom šest godina. Blog
$ sudo apt update
$ sudo apt install awscli
$ aws configure
Postavit ću klijentovo ograničenje istovremenih zahtjeva na 100 tako da se datoteke učitavaju brže od zadanih postavki.
$ aws configure set
default.s3.max_concurrent_requests
100
Skinut ću skup podataka o taksi putovanju sa AWS S3 i pohraniti ga na NVMe disk na prvom serveru. Ovaj skup podataka je ~104 GB u GZIP kompresovanom CSV formatu.
$ sudo mkdir -p /ch/csv
$ sudo chown -R ubuntu /ch/csv
$ aws s3 sync s3://<bucket>/csv /ch/csv
Instalacija ClickHouse
Instaliraću distribuciju OpenJDK za Javu 8, jer je potrebna za pokretanje Apache ZooKeepera, koji je neophodan za distribuiranu instalaciju ClickHouse na sve tri mašine.
$ sudo apt update
$ sudo apt install
openjdk-8-jre
openjdk-8-jdk-headless
Zatim sam postavio varijablu okruženja JAVA_HOME
.
$ sudo vi /etc/profile
export JAVA_HOME=/usr
$ source /etc/profile
Zatim ću koristiti Ubuntuov sistem za upravljanje paketima da instaliram ClickHouse 18.16.1, poglede i ZooKeeper na sve tri mašine.
$ 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
glances
zookeeperd
Napravit ću direktorij za ClickHouse i također napraviti neke promjene konfiguracije na sva tri servera.
$ 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
Ovo su konfiguracije koje ću koristiti.
<?xml version="1.0"?>
<yandex>
<listen_host>0.0.0.0</listen_host>
<path>/ch/clickhouse/</path>
<remote_servers>
<perftest_3shards>
<shard>
<replica>
<host>172.30.2.192</host>
<port>9000</port>
</replica>
</shard>
<shard>
<replica>
<host>172.30.2.162</host>
<port>9000</port>
</replica>
</shard>
<shard>
<replica>
<host>172.30.2.36</host>
<port>9000</port>
</replica>
</shard>
</perftest_3shards>
</remote_servers>
<zookeeper-servers>
<node>
<host>172.30.2.192</host>
<port>2181</port>
</node>
<node>
<host>172.30.2.162</host>
<port>2181</port>
</node>
<node>
<host>172.30.2.36</host>
<port>2181</port>
</node>
</zookeeper-servers>
<macros>
<shard>03</shard>
<replica>01</replica>
</macros>
</yandex>
Zatim ću pokrenuti ZooKeeper i ClickHouse server na sve tri mašine.
$ sudo /etc/init.d/zookeeper start
$ sudo service clickhouse-server start
Prijenos podataka na ClickHouse
Na prvom serveru ću kreirati tabelu putovanja (trips
) koji će pohraniti skup podataka o vožnji taksijem koristeći 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;
Zatim raspakujem i učitam svaki od CSV fajlova u tabelu putovanja (trips
). Sljedeće se završava za 55 minuta i 10 sekundi. Nakon ove operacije, veličina direktorija podataka bila je 134 GB.
$ time (for FILENAME in /ch/csv/trips_x*.csv.gz; do
echo $FILENAME
gunzip -c $FILENAME |
clickhouse-client
--host=0.0.0.0
--query="INSERT INTO trips FORMAT CSV"
done)
Brzina uvoza bila je 155 MB nekomprimovanog CSV sadržaja u sekundi. Pretpostavljam da je to bilo zbog uskog grla u GZIP dekompresiji. Možda je bilo brže paralelno dekomprimirati sve gzip datoteke koristeći xargs, a zatim preuzeti dekomprimirane podatke. Ispod je opis onoga što je prijavljeno tokom procesa uvoza CSV-a.
$ 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
Oslobodiću prostor na NVMe disku brisanjem originalnih CSV fajlova pre nego što nastavim.
$ sudo rm -fr /ch/csv
Pretvorite u oblik stupca
Mehanizam Log ClickHouse će pohraniti podatke u formatu orijentiranom na nizove. Da bih brže tražio podatke, pretvaram ih u format kolone koristeći MergeTree engine.
$ clickhouse-client --host=0.0.0.0
Sljedeće se završava za 34 minute i 50 sekundi. Nakon ove operacije, veličina direktorija podataka bila je 237 GB.
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;
Ovako je izgledao izlaz pogleda tokom operacije:
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)
U posljednjem testu nekoliko kolona je konvertirano i ponovo izračunato. Otkrio sam da neke od ovih funkcija više ne rade ispravno na ovom skupu podataka. Da bih riješio ovaj problem, uklonio sam neprikladne funkcije i učitao podatke bez konverzije u finije tipove.
Distribucija podataka klastera
Ja ću distribuirati podatke na sva tri čvora klastera. Za početak, u nastavku ću napraviti tabelu za sve tri mašine.
$ 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);
Tada ću se pobrinuti da prvi server može vidjeti sva tri čvora u klasteru.
SELECT *
FROM system.clusters
WHERE cluster = 'perftest_3shards'
FORMAT Vertical;
Row 1:
──────
cluster: perftest_3shards
shard_num: 1
shard_weight: 1
replica_num: 1
host_name: 172.30.2.192
host_address: 172.30.2.192
port: 9000
is_local: 1
user: default
default_database:
Row 2:
──────
cluster: perftest_3shards
shard_num: 2
shard_weight: 1
replica_num: 1
host_name: 172.30.2.162
host_address: 172.30.2.162
port: 9000
is_local: 0
user: default
default_database:
Row 3:
──────
cluster: perftest_3shards
shard_num: 3
shard_weight: 1
replica_num: 1
host_name: 172.30.2.36
host_address: 172.30.2.36
port: 9000
is_local: 0
user: default
default_database:
Zatim ću definisati novu tabelu na prvom serveru koja se zasniva na šemi trips_mergetree_third
i koristi Distributed engine.
CREATE TABLE trips_mergetree_x3
AS trips_mergetree_third
ENGINE = Distributed(perftest_3shards,
default,
trips_mergetree_third,
rand());
Zatim ću kopirati podatke iz tabele zasnovane na MergeTree na sva tri servera. Sljedeće je završeno za 34 minute i 44 sekunde.
INSERT INTO trips_mergetree_x3
SELECT * FROM trips_mergetree;
Nakon gore navedene operacije, dao sam ClickHouse-u 15 minuta da pređe oznaku maksimalnog skladištenja. Direktorijumi podataka su na kraju imali 264 GB, 34 GB i 33 GB, respektivno, na svakom od tri servera.
ClickHouse Cluster Performance Evaluation
Sljedeće sam vidio najbrži put kada sam pokrenuo svaki upit više puta na tabeli trips_mergetree_x3
.
$ clickhouse-client --host=0.0.0.0
Sljedeće je završeno za 2.449 sekundi.
SELECT cab_type, count(*)
FROM trips_mergetree_x3
GROUP BY cab_type;
Sljedeće je završeno za 0.691 sekundi.
SELECT passenger_count,
avg(total_amount)
FROM trips_mergetree_x3
GROUP BY passenger_count;
Sljedeće se radi za 0 sekunde.
SELECT passenger_count,
toYear(pickup_date) AS year,
count(*)
FROM trips_mergetree_x3
GROUP BY passenger_count,
year;
Sljedeće je završeno za 0.983 sekundi.
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;
Poređenja radi, pokrenuo sam iste upite na tabeli zasnovanoj na MergeTree koja se nalazi isključivo na prvom serveru.
Procjena performansi jednog čvora ClickHouse
Sljedeće sam vidio najbrži put kada sam pokrenuo svaki upit više puta na tabeli trips_mergetree_x3
.
Sljedeće je završeno za 0.241 sekundi.
SELECT cab_type, count(*)
FROM trips_mergetree
GROUP BY cab_type;
Sljedeće je završeno za 0.826 sekundi.
SELECT passenger_count,
avg(total_amount)
FROM trips_mergetree
GROUP BY passenger_count;
Sljedeće je završeno za 1.209 sekundi.
SELECT passenger_count,
toYear(pickup_date) AS year,
count(*)
FROM trips_mergetree
GROUP BY passenger_count,
year;
Sljedeće je završeno za 1.781 sekundi.
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;
Razmišljanja o rezultatima
Ovo je prvi put da je besplatna baza podataka zasnovana na CPU-u uspjela nadmašiti bazu podataka zasnovanu na GPU-u u mojim testovima. Ta baza podataka bazirana na GPU-u je od tada prošla dvije revizije, ali ipak, performanse koje je ClickHouse pokazao na jednom čvoru su vrlo impresivne.
U isto vrijeme, kada se upit 1 izvršava na distribuiranom stroju, režijski troškovi su za red veličine veći. Nadam se da sam nešto propustio u svom istraživanju za ovaj post, jer bi bilo lijepo vidjeti da se vrijeme upita smanjuje kako dodajem više čvorova u klaster. Međutim, zapanjujuće je da se pri izvršavanju drugih upita performanse povećale za oko 2 puta.
Bilo bi lijepo kada bi ClickHouse evoluirao u smjeru mogućnosti odvajanja prostora za skladištenje i računanja kako bi se mogli samostalno skalirati. Podrška za HDFS, koja je dodata prošle godine, mogla bi biti korak ka tome. Što se tiče računarstva, ako se jedan upit može ubrzati dodavanjem više čvorova u klaster, tada će budućnost ovog softvera biti vrlo svijetla.
Hvala što ste odvojili vrijeme da pročitate ovaj post. Nudim usluge savjetovanja, arhitekture i praktičnog razvoja za klijente u Sjevernoj Americi i Europi. Ako želite da razgovarate o tome kako moji prijedlozi mogu pomoći vašem poslovanju, kontaktirajte me putem
izvor: www.habr.com