id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
7035d5e9-c5d7-45b5-a0c2-84abf617e9f7 | Users can expect throughput in order of thousands of rows per second.
:::note Inserting into single JSON row
If inserting into a single JSON column (see the
syslog_json
schema above), the same insert command can be used. However, users must specify
JSONAsObject
as the format instead of
JSONEachRow
e.g.
shel... | {"source_file": "migrating-data.md"} | [
0.06230125203728676,
0.019852997735142708,
-0.09784752130508423,
0.03097733110189438,
-0.04994623363018036,
-0.05123166739940643,
-0.034299638122320175,
0.018222585320472717,
0.03286577761173248,
0.08105242997407913,
-0.006961462553590536,
-0.03576226532459259,
0.048018764704465866,
0.0284... |
6761bafc-9331-4812-a8d4-430a619569a8 | description: 'Install ClickHouse on MacOS'
keywords: ['ClickHouse', 'install', 'Linux', 'tar']
sidebar_label: 'Other Linux'
slug: /install/linux_other
title: 'Install ClickHouse using tgz archives'
hide_title: true
doc_type: 'guide'
import Tar from './_snippets/_linux_tar_install.md' | {"source_file": "other_linux.md"} | [
-0.020146673545241356,
0.04635027423501015,
-0.006083890330046415,
-0.07637438923120499,
0.09251473844051361,
-0.048644158989191055,
0.012125969864428043,
-0.011722918599843979,
-0.05268516764044762,
-0.04350439831614494,
0.06686703115701675,
-0.03448064252734184,
0.001658551744185388,
-0.... |
bcdb4991-986c-402c-82f5-645a1fc8da96 | description: 'Install ClickHouse on Debian/Ubuntu Linux'
keywords: ['ClickHouse', 'install', 'Debian', 'Ubuntu', 'deb']
sidebar_label: 'Debian/Ubuntu'
slug: /install/debian_ubuntu
title: 'Install ClickHouse on Debian/Ubuntu'
hide_title: true
doc_type: 'guide'
import DebianProd from './_snippets/_deb_install.md' | {"source_file": "debian_ubuntu.md"} | [
-0.01900630258023739,
-0.010271530598402023,
0.04647238925099373,
-0.1129513531923294,
0.02193085290491581,
-0.07785164564847946,
0.030055461451411247,
-0.018362322822213173,
-0.08672719448804855,
-0.02765071578323841,
0.09031949937343597,
-0.04167851805686951,
0.025756772607564926,
-0.054... |
4041ca4e-ee73-42f8-82ca-9405e18ee4dc | description: 'Install ClickHouse on Windows with WSL'
keywords: ['ClickHouse', 'install', 'Redhat', 'rpm']
sidebar_label: 'Windows'
slug: /install/windows
title: 'Install ClickHouse on Windows with WSL'
hide_title: true
doc_type: 'guide'
import Windows from './_snippets/_windows_install.md' | {"source_file": "windows.md"} | [
0.01693863235414028,
0.03713786229491234,
-0.03266679123044014,
0.03743100166320801,
0.052037522196769714,
0.01845608837902546,
0.039306387305259705,
-0.02978450618684292,
-0.048864491283893585,
-0.04713423550128937,
0.029296524822711945,
-0.04342183098196983,
0.025231916457414627,
-0.0017... |
d1ff8072-606f-40e1-81a6-191c0a99fe0d | description: 'Install ClickHouse on MacOS'
keywords: ['ClickHouse', 'install', 'MacOS']
sidebar_label: 'MacOS'
slug: /install/macOS
title: 'Install ClickHouse using Homebrew'
hide_title: true
doc_type: 'guide'
import MacOSProd from './_snippets/_macos.md' | {"source_file": "macos.md"} | [
-0.006040071602910757,
0.01261296309530735,
-0.0034337828401476145,
-0.051344260573387146,
0.0846545621752739,
-0.03153415024280548,
0.02104872651398182,
-0.028976500034332275,
-0.08187134563922882,
-0.04967592656612396,
0.040181223303079605,
-0.04617827758193016,
0.006835620850324631,
-0.... |
03cf37f4-8d49-489b-af01-06979ee4d757 | description: 'Install ClickHouse on any platform using curl'
keywords: ['ClickHouse', 'install', 'quick', 'curl']
sidebar_label: 'Quick install'
slug: /install/quick-install-curl
title: 'Install ClickHouse via script using curl'
hide_title: true
doc_type: 'guide'
import QuickInstall from './_snippets/_quick_install... | {"source_file": "quick-install-curl.md"} | [
-0.005015494767576456,
0.029548386111855507,
-0.033865753561258316,
-0.04095529019832611,
0.011198349297046661,
-0.041125018149614334,
-0.0084176454693079,
-0.03158341720700264,
-0.03924994915723801,
-0.04493807256221771,
0.07955245673656464,
-0.014560617506504059,
0.013945868238806725,
-0... |
9b72b340-3477-4d0a-a412-537a8bbdb1cb | description: 'Instructions for compiling ClickHouse from source or installing a CI-generated binary'
keywords: ['ClickHouse', 'install', 'advanced', 'compile from source', 'CI generated binary']
sidebar_label: 'Advanced install'
slug: /install/advanced
title: 'Advanced installation methods'
hide_title: false
doc_type: ... | {"source_file": "advanced.md"} | [
-0.007526542525738478,
-0.00715529965236783,
-0.041624683886766434,
-0.06276901811361313,
-0.048362452536821365,
-0.06388356536626816,
-0.01303535234183073,
-0.019282346591353416,
-0.10664723068475723,
-0.01337047666311264,
0.043427664786577225,
-0.09307167679071426,
0.013225427828729153,
... |
b3d67d90-a986-4de6-84cf-5346cca7b490 | description: 'Install ClickHouse on Redhat/CentOS Linux'
keywords: ['ClickHouse', 'install', 'Redhat', 'CentOS', 'rpm']
sidebar_label: 'Redhat/CentOS'
slug: /install/redhat
title: 'Install ClickHouse on rpm-based Linux distributions'
hide_title: true
doc_type: 'guide'
import RPM from './_snippets/_rpm_install.md' | {"source_file": "redhat.md"} | [
0.02381940558552742,
-0.03642869368195534,
-0.038943640887737274,
-0.06362326443195343,
0.10166700184345245,
-0.00639997748658061,
0.02195730246603489,
-0.020738905295729637,
-0.08580290526151657,
-0.06501084566116333,
0.09055262058973312,
-0.04088499769568443,
0.02923877164721489,
-0.0384... |
482c1abe-830e-474f-93be-35f3e450568f | description: 'Install ClickHouse on Debian/Ubuntu Linux'
keywords: ['ClickHouse', 'install', 'Docker']
sidebar_label: 'Docker'
slug: /install/docker
title: 'Install ClickHouse using Docker'
hide_title: true
doc_type: 'guide'
import Docker from './_snippets/_docker.md' | {"source_file": "docker.md"} | [
-0.024867724627256393,
0.020768141373991966,
0.02650061435997486,
-0.05048568546772003,
0.0327327623963356,
-0.06226544454693794,
0.017818277701735497,
-0.01937069557607174,
-0.06846145540475845,
-0.018596794456243515,
0.028350768610835075,
-0.0657268688082695,
0.02186739258468151,
-0.0148... |
a38aa550-7606-4367-92fb-a38db474c71f | description: 'Data for billions of taxi and for-hire vehicle (Uber, Lyft, etc.) trips
originating in New York City since 2009'
sidebar_label: 'New York taxi data'
slug: /getting-started/example-datasets/nyc-taxi
title: 'New York Taxi Data'
doc_type: 'guide'
keywords: ['example dataset', 'nyc taxi', 'tutorial', 'sampl... | {"source_file": "nyc-taxi.md"} | [
0.05633191019296646,
-0.07493388652801514,
-0.022866833955049515,
0.03902093693614006,
0.010442706756293774,
-0.0017693097470328212,
0.023979580029845238,
-0.044764064252376556,
-0.07944702357053757,
0.017879730090498924,
0.04765462502837181,
0.009462646208703518,
0.006956998258829117,
-0.... |
b66f7fed-fb63-437b-a79c-69f76209ce54 | The following command streams three files from a GCS bucket into the
trips
table (the
{0..2}
syntax is a wildcard for the values 0, 1, and 2):
sql
INSERT INTO nyc_taxi.trips_small
SELECT
trip_id,
pickup_datetime,
dropoff_datetime,
pickup_longitude,
pickup_latitude,
dropoff_longitude,
d... | {"source_file": "nyc-taxi.md"} | [
-0.01068434864282608,
-0.03921125829219818,
-0.030113032087683678,
0.05727050080895424,
-0.03189292550086975,
-0.08621267974376678,
0.08296684175729752,
-0.020349737256765366,
-0.03600982576608658,
0.033502254635095596,
0.02042418345808983,
-0.0657355859875679,
0.042454853653907776,
-0.101... |
7ae2b5a2-da99-4e09-bca7-25c0dba12622 | See https://github.com/toddwschneider/nyc-taxi-data and http://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html for the description of a dataset and instructions for downloading.
Downloading will result in about 227 GB of uncompressed data in CSV files. The download takes about an hour over a 1 Gbit connectio... | {"source_file": "nyc-taxi.md"} | [
0.039862971752882004,
-0.016991566866636276,
-0.04868562892079353,
-0.002722004661336541,
0.05194157361984253,
-0.06414318829774857,
-0.031098369508981705,
-0.03259927034378052,
-0.01040783803910017,
0.054248448461294174,
0.010856231674551964,
0.018060505390167236,
-0.05937509983778,
-0.07... |
f6db9a69-a8ad-41d4-8c48-0ec587a679a0 | sql | {"source_file": "nyc-taxi.md"} | [
0.07582295686006546,
0.0011653322726488113,
-0.03202968090772629,
0.07204441726207733,
-0.10746068507432938,
0.006198782008141279,
0.1837887018918991,
0.028402604162693024,
-0.0444914735853672,
-0.0017817936604842544,
0.0661090537905693,
-0.0014285664074122906,
0.08542580902576447,
-0.0624... |
f8263a1c-abdf-4508-96d1-2a139fd62af4 | CREATE TABLE default.trips_mergetree_third ( trip_id UInt32, vendor_id Enum8('1' = 1, '2' = 2, 'CMT' = 3, 'VTS' = 4, 'DDS' = 5, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14), pickup_date Date, pickup_datetime DateTime, dropoff_date Date, dropoff_datetime DateTime, store_and_fwd_flag U... | {"source_file": "nyc-taxi.md"} | [
0.06548941880464554,
0.01729363016784191,
-0.042913537472486496,
0.012406527996063232,
-0.04907248169183731,
-0.00710943853482604,
0.019786041229963303,
0.040224045515060425,
-0.05135050788521767,
0.06265398859977722,
0.11194583028554916,
-0.10370253771543503,
0.008179716765880585,
-0.0736... |
47056717-0eb3-4077-967b-ae0363f10195 | = 57, 'Eastchester-Edenwald-Baychester' = 58, 'Elmhurst' = 59, 'Elmhurst-Maspeth' = 60, 'Erasmus' = 61, 'Far Rockaway-Bayswater' = 62, 'Flatbush' = 63, 'Flatlands' = 64, 'Flushing' = 65, 'Fordham South' = 66, 'Forest Hills' = 67, 'Fort Greene' = 68, 'Fresh Meadows-Utopia' = 69, 'Ft. Totten-Bay Terrace-Clearview' = 70, ... | {"source_file": "nyc-taxi.md"} | [
0.10098664462566376,
-0.08191058039665222,
0.0557032972574234,
0.01831633411347866,
0.030155044049024582,
0.06574583798646927,
-0.07042296975851059,
-0.08633781224489212,
-0.09561042487621307,
0.006724240258336067,
-0.04015296325087547,
-0.08685784786939621,
-0.0169826690107584,
0.02532734... |
98f40b26-2395-4ffb-b57f-7dcaf390e054 | = 155, 'Springfield Gardens North' = 156, 'Springfield Gardens South-Brookville' = 157, 'Spuyten Duyvil-Kingsbridge' = 158, 'St. Albans' = 159, 'Stapleton-Rosebank' = 160, 'Starrett City' = 161, 'Steinway' = 162, 'Stuyvesant Heights' = 163, 'Stuyvesant Town-Cooper Village' = 164, 'Sunset Park East' = 165, 'Sunset Park ... | {"source_file": "nyc-taxi.md"} | [
0.0700279250741005,
-0.033156026154756546,
0.005018690600991249,
0.0006638895720243454,
-0.025925040245056152,
0.05445704981684685,
-0.07839107513427734,
-0.0527678057551384,
-0.12784430384635925,
-0.012537558563053608,
0.013590402901172638,
-0.06618671119213104,
-0.0015444309683516622,
-0... |
b26c7667-b441-45ae-9b7b-541aeb646ae6 | = 43, 'DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hill' = 44, 'Douglas Manor-Douglaston-Little Neck' = 45, 'Dyker Heights' = 46, 'East Concourse-Concourse Village' = 47, 'East Elmhurst' = 48, 'East Flatbush-Farragut' = 49, 'East Flushing' = 50, 'East Harlem North' = 51, 'East Harlem South' = 52, 'East New York' = 53, ... | {"source_file": "nyc-taxi.md"} | [
0.11493565887212753,
-0.07842840254306793,
0.050981730222702026,
0.013232262805104256,
0.0200498066842556,
0.03812645375728607,
-0.07626472413539886,
-0.12669028341770172,
-0.07874232530593872,
0.00215164409019053,
0.008828346617519855,
-0.044393692165613174,
-0.030251648277044296,
-0.0186... |
9d772dd7-bf94-4038-b56d-33db791c336a | 'Ridgewood' = 143, 'Rikers Island' = 144, 'Rosedale' = 145, 'Rossville-Woodrow' = 146, 'Rugby-Remsen Village' = 147, 'Schuylerville-Throgs Neck-Edgewater Park' = 148, 'Seagate-Coney Island' = 149, 'Sheepshead Bay-Gerritsen Beach-Manhattan Beach' = 150, 'SoHo-TriBeCa-Civic Center-Little Italy' = 151, 'Soundview-Bruckner... | {"source_file": "nyc-taxi.md"} | [
0.0680123046040535,
-0.08119821548461914,
0.0021842061541974545,
-0.005765696056187153,
-0.020191390067338943,
0.04930558800697327,
-0.0388503298163414,
-0.09240609407424927,
-0.14405877888202667,
-0.011921319179236889,
0.033292319625616074,
-0.053255654871463776,
0.01779966987669468,
-0.0... |
4f62aefe-a815-48b0-ae37-043dd72b7fee | On the source server:
sql
CREATE TABLE trips_mergetree_x3 AS trips_mergetree_third ENGINE = Distributed(perftest, default, trips_mergetree_third, rand());
The following query redistributes data:
sql
INSERT INTO trips_mergetree_x3 SELECT * FROM trips_mergetree;
This takes 2454 seconds.
On three servers:
Q1: ... | {"source_file": "nyc-taxi.md"} | [
0.06506422907114029,
-0.055861037224531174,
0.009848363697528839,
0.0803535059094429,
-0.005368295591324568,
-0.14412377774715424,
-0.013303481973707676,
-0.02458902820944786,
0.06922445446252823,
0.01177428662776947,
0.007631328888237476,
-0.04397941008210182,
-0.0027194705326110125,
-0.0... |
68cce6f7-fdc6-4f70-afcc-efeb4a56ba82 | description: 'A terabyte of click logs from Criteo'
sidebar_label: 'Criteo 1TB click logs'
slug: /getting-started/example-datasets/criteo
keywords: ['Criteo click logs', 'advertising data', 'click-through data', 'terabyte dataset', 'getting started']
title: 'Terabyte click logs from Criteo'
doc_type: 'guide'
Downlo... | {"source_file": "criteo.md"} | [
0.029170364141464233,
-0.06263914704322815,
-0.04117220640182495,
0.033519353717565536,
0.012094319798052311,
-0.036302998661994934,
0.09796145558357239,
0.036503538489341736,
-0.10262369364500046,
0.046782899647951126,
0.03856237232685089,
-0.07828966528177261,
0.03432919830083847,
-0.053... |
af589b58-2376-4dd9-aa5f-13453d229ad9 | Transform data from the raw log and put it in the second table:
```sql
INSERT INTO
criteo
SELECT
date,
clicked,
int1,
int2,
int3,
int4,
int5,
int6,
int7,
int8,
int9,
int10,
int11,
int12,
int13,
reinterpretAsUInt32(unhex(cat1)) AS icat1,
reinterpr... | {"source_file": "criteo.md"} | [
0.051757391542196274,
-0.06089457497000694,
0.03293812274932861,
0.008872264996170998,
-0.07140976935625076,
0.016668641939759254,
-0.0045790839940309525,
-0.010588402859866619,
-0.0899113118648529,
0.04415009170770645,
0.02046854980289936,
-0.08996149897575378,
0.0337197482585907,
-0.0626... |
28ad61c2-04d7-4014-8e42-8add700a8bed | description: 'Explore the WikiStat dataset containing 0.5 trillion records.'
sidebar_label: 'WikiStat'
slug: /getting-started/example-datasets/wikistat
title: 'WikiStat'
doc_type: 'guide'
keywords: ['example dataset', 'wikipedia', 'tutorial', 'sample data', 'pageviews']
The dataset contains 0.5 trillion records.
... | {"source_file": "wikistat.md"} | [
-0.033918969333171844,
0.03143541142344475,
-0.028887702152132988,
0.036859605461359024,
0.03582900017499924,
-0.1029479131102562,
-0.0066758315078914165,
-0.006223773583769798,
0.013377880677580833,
0.04450315237045288,
0.10612426698207855,
-0.00023574924853164703,
0.08290843665599823,
-0... |
d9bc2603-6937-4f54-bcd6-be0fa70cba30 | description: 'The TPC-DS benchmark data set and queries.'
sidebar_label: 'TPC-DS'
slug: /getting-started/example-datasets/tpcds
title: 'TPC-DS (2012)'
doc_type: 'guide'
keywords: ['example dataset', 'tpcds', 'benchmark', 'sample data', 'performance testing']
Similar to the
Star Schema Benchmark (SSB)
, TPC-DS is b... | {"source_file": "tpcds.md"} | [
-0.029449719935655594,
0.008485103026032448,
-0.026408039033412933,
0.05433010309934616,
-0.03974904492497444,
-0.09707925468683243,
-0.022946661338210106,
0.08575315773487091,
-0.01202641986310482,
-0.034879159182310104,
-0.040606312453746796,
-0.013510649092495441,
0.014713018201291561,
... |
4ce39f32-9677-488a-84e0-0b9a6722b2bd | ```sql
CREATE TABLE call_center(
cc_call_center_sk Int64,
cc_call_center_id LowCardinality(String),
cc_rec_start_date Nullable(Date),
cc_rec_end_date Nullable(Date),
cc_closed_date_sk Nullable(UInt32),
cc_open_date_sk Nullable(UInt3... | {"source_file": "tpcds.md"} | [
0.02515074610710144,
0.0166876632720232,
-0.07735484093427658,
0.06499585509300232,
-0.09705144166946411,
-0.005343285389244556,
0.06110534071922302,
0.0545349158346653,
-0.043871887028217316,
-0.011284533888101578,
0.08839616179466248,
-0.11193125694990158,
-0.015174041502177715,
-0.04584... |
d7c56477-d4a3-440a-92c5-d79dae860a05 | CREATE TABLE catalog_returns(
cr_returned_date_sk Int32,
cr_returned_time_sk Int64,
cr_item_sk Int64,
cr_refunded_customer_sk Nullable(Int64),
cr_refunded_cdemo_sk Nullable(Int64),
cr_refunded_hdemo_sk Nullable(Int64),
cr_refunded_addr_sk Nullable... | {"source_file": "tpcds.md"} | [
0.022197969257831573,
-0.013586128130555153,
-0.07617095857858658,
0.03864339739084244,
-0.03239325433969498,
0.04583209753036499,
-0.031347017735242844,
0.05148952454328537,
-0.07005679607391357,
0.06083877757191658,
0.13140474259853363,
-0.13903513550758362,
0.026573805138468742,
-0.0708... |
1c7cabf8-9342-4339-95ac-7a4e3cb941a5 | CREATE TABLE catalog_sales (
cs_sold_date_sk Nullable(UInt32),
cs_sold_time_sk Nullable(Int64),
cs_ship_date_sk Nullable(UInt32),
cs_bill_customer_sk Nullable(Int64),
cs_bill_cdemo_sk Nullable(Int64),
cs_bill_hdemo_sk Nullable(Int64),
cs_... | {"source_file": "tpcds.md"} | [
0.02553153596818447,
0.005854299757629633,
-0.10531798005104065,
0.03215508162975311,
-0.07714344561100006,
0.060594018548727036,
-0.028765125200152397,
0.04650965705513954,
-0.06647740304470062,
0.05483521893620491,
0.12073281407356262,
-0.13880102336406708,
0.05142618715763092,
-0.086018... |
164febb0-26ec-4d82-9734-849806291c9c | CREATE TABLE customer_demographics (
cd_demo_sk Int64,
cd_gender LowCardinality(String),
cd_marital_status LowCardinality(String),
cd_education_status LowCardinality(String),
cd_purchase_estimate Int32,
cd_credit_rating LowCardinality(St... | {"source_file": "tpcds.md"} | [
0.05969854071736336,
0.05379343032836914,
-0.044157709926366806,
0.04138045012950897,
-0.08707087486982346,
0.07309743016958237,
0.017139002680778503,
0.09338018298149109,
-0.03814571723341942,
0.01686142571270466,
0.17908623814582825,
-0.17007648944854736,
0.04553315415978432,
-0.09349133... |
9c38650c-181c-4b5c-8fae-0af07d11cc17 | CREATE TABLE household_demographics (
hd_demo_sk Int64,
hd_income_band_sk Int64,
hd_buy_potential LowCardinality(String),
hd_dep_count Int32,
hd_vehicle_count Int32,
PRIMARY KEY (hd_demo_sk)
);
CREATE TABLE income_band(
ib_income_band_s... | {"source_file": "tpcds.md"} | [
0.09212084114551544,
0.0379926897585392,
-0.031123755499720573,
0.030692672356963158,
-0.08573533594608307,
0.0553719624876976,
-0.007460231427103281,
0.11556641012430191,
-0.07125622779130936,
0.013664673082530499,
0.13081784546375275,
-0.15724746882915497,
0.05839924141764641,
-0.0789294... |
47b429c5-fe69-4557-9eb7-255ed4aff1bc | CREATE TABLE promotion (
p_promo_sk Int64,
p_promo_id LowCardinality(String),
p_start_date_sk Nullable(UInt32),
p_end_date_sk Nullable(UInt32),
p_item_sk Nullable(Int64),
p_cost Nullable(Decimal(15,2)),
p_... | {"source_file": "tpcds.md"} | [
0.0662822425365448,
0.00863656960427761,
-0.07770358771085739,
0.016022631898522377,
-0.025090133771300316,
0.053283631801605225,
0.05856476351618767,
0.058779701590538025,
-0.0032294720876961946,
0.01563890092074871,
0.09264440089464188,
-0.14779426157474518,
0.03467420116066933,
-0.02407... |
f737da89-4f74-454c-85af-8f44d2ee3201 | CREATE TABLE store_sales (
ss_sold_date_sk Nullable(UInt32),
ss_sold_time_sk Nullable(Int64),
ss_item_sk Int64,
ss_customer_sk Nullable(Int64),
ss_cdemo_sk Nullable(Int64),
ss_hdemo_sk Nullable(Int64),
ss_addr_sk ... | {"source_file": "tpcds.md"} | [
0.015124657191336155,
0.01933007873594761,
-0.12230294942855835,
0.023427071049809456,
-0.06261537969112396,
0.08643060177564621,
-0.0018111236859112978,
0.09477408975362778,
-0.043197885155677795,
0.053842511028051376,
0.12727764248847961,
-0.13677214086055756,
0.05799892172217369,
-0.011... |
11cb3f3c-af6c-44d6-8f9c-ed44612e335e | CREATE TABLE time_dim (
t_time_sk UInt32,
t_time_id LowCardinality(String),
t_time UInt32,
t_hour UInt8,
t_minute UInt8,
t_second UInt8,
t_am_pm LowCardinality(String),
... | {"source_file": "tpcds.md"} | [
0.08803053200244904,
0.03991769626736641,
-0.02444089576601982,
0.039559733122587204,
-0.07792540639638901,
0.01712733320891857,
0.019299758598208427,
0.026462944224476814,
0.0020201613660901785,
-0.04121548682451248,
0.13206234574317932,
-0.12232591211795807,
0.01081222016364336,
-0.05308... |
e660ae79-ebd3-4706-be14-6546c1de289d | CREATE TABLE web_returns (
wr_returned_date_sk Nullable(UInt32),
wr_returned_time_sk Nullable(Int64),
wr_item_sk Int64,
wr_refunded_customer_sk Nullable(Int64),
wr_refunded_cdemo_sk Nullable(Int64),
wr_refunded_hdemo_sk Nullable(Int64),
wr_refunded_addr... | {"source_file": "tpcds.md"} | [
-0.00736127607524395,
0.0025878059677779675,
-0.08994951844215393,
0.03801369294524193,
-0.016496505588293076,
0.054244861006736755,
-0.011030683293938637,
0.027840636670589447,
-0.0743492841720581,
0.0684170052409172,
0.11089499294757843,
-0.11319440603256226,
0.027510451152920723,
-0.073... |
9b182921-e898-4cf2-8d54-ddacf1dd3701 | CREATE TABLE web_site (
web_site_sk Int64,
web_site_id LowCardinality(String),
web_rec_start_date LowCardinality(String),
web_rec_end_date LowCardinality(Nullable(String)),
web_name LowCardinality(String),
web_open_date_sk UInt32,
web_close_date_... | {"source_file": "tpcds.md"} | [
0.06928835064172745,
0.012366079725325108,
-0.06643004715442657,
0.00916255172342062,
-0.056588057428598404,
-0.001325874007306993,
0.06898007541894913,
0.0037090061232447624,
-0.10718221217393875,
-0.025110630318522453,
0.1152997761964798,
-0.1374000906944275,
0.011756771244108677,
-0.110... |
70820963-e970-4077-b457-ca5d04f994c4 | The data can be imported as follows:
bash
clickhouse-client --format_csv_delimiter '|' --query "INSERT INTO call_center FORMAT CSV" < call_center.tbl
clickhouse-client --format_csv_delimiter '|' --query "INSERT INTO catalog_page FORMAT CSV" < catalog_page.tbl
clickhouse-client --format_csv_delimiter '|' --query "INSE... | {"source_file": "tpcds.md"} | [
0.02621936984360218,
-0.04525356739759445,
-0.06037649139761925,
0.03980831429362297,
-0.09100665897130966,
0.05544522404670715,
-0.023116979748010635,
-0.0018830442568287253,
-0.05643893778324127,
-0.011779194697737694,
0.014903522096574306,
-0.06535282731056213,
0.07981684058904648,
-0.1... |
ecb0e9ad-cd79-4448-8a92-f7fc0f0cc7d3 | description: 'COVID-19 Open-Data is a large, open-source database of COVID-19 epidemiological
data and related factors like demographics, economics, and government responses'
sidebar_label: 'COVID-19 open-data'
slug: /getting-started/example-datasets/covid19
title: 'COVID-19 Open-Data'
keywords: ['COVID-19 data', 'ep... | {"source_file": "covid19.md"} | [
0.01971649006009102,
-0.03643934428691864,
-0.07128757238388062,
0.019795790314674377,
0.06318281590938568,
-0.0005378350615501404,
-0.03259284794330597,
0.05327229201793671,
-0.08541884273290634,
0.017887191846966743,
0.08486024290323257,
-0.026468336582183838,
-0.008671306073665619,
-0.0... |
37c0f5a1-a685-4aa9-af32-d186ad09bd65 | sql
SELECT *
FROM url('https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv')
LIMIT 100;
Notice the
url
function easily reads data from a CSV file:
response
ββc1ββββββββββ¬βc2ββββββββββββ¬βc3βββββββββββββ¬βc4ββββββββββββ¬βc5βββββββββββββ¬βc6ββββββββββ¬βc7ββββββββββββββββββββ¬βc8βββββββββββββββββββ¬βc9ββββ... | {"source_file": "covid19.md"} | [
-0.010755576193332672,
0.03843924030661583,
-0.06047261133790016,
0.018726631999015808,
0.022787882015109062,
-0.07493586093187332,
-0.012678918428719044,
-0.0013983258977532387,
-0.004315354395657778,
0.07863672077655792,
0.0788327306509018,
-0.08720587193965912,
0.05993896350264549,
-0.0... |
74b3e92a-f5f5-4452-b246-344b273cd9ee | It goes pretty quick - let's see how many rows were inserted:
sql
SELECT formatReadableQuantity(count())
FROM covid19;
response
ββformatReadableQuantity(count())ββ
β 12.53 million β
βββββββββββββββββββββββββββββββββββ
Let's see how many total cases of Covid-19 were recorded:
sql
SELECT f... | {"source_file": "covid19.md"} | [
0.02025560475885868,
-0.03333744406700134,
0.0018175144214183092,
0.03653842955827713,
-0.017023760825395584,
-0.026878945529460907,
0.020757226273417473,
0.04529889300465584,
-0.02698994241654873,
0.06871858984231949,
0.040301863104104996,
-0.056244995445013046,
0.02479449100792408,
0.011... |
8eaf4d3a-316f-4ba9-9617-27fc541f320d | The response look like:
response
ββconfirmed_cases_deltaββ¬βnew_confirmedββ¬βlocation_keyββ¬βββββββdateββ
β 0 β 0 β US_DC β 2020-03-08 β
β 2 β 2 β US_DC β 2020-03-09 β
β -2 β 0 β US_DC β 2020-03-10 β
β ... | {"source_file": "covid19.md"} | [
-0.055711328983306885,
0.04229430481791496,
-0.015283778309822083,
0.010282293893396854,
0.01076002512127161,
-0.06890509277582169,
-0.006909415125846863,
-0.029786821454763412,
-0.03120068646967411,
0.08876930177211761,
0.0771314799785614,
-0.0032951487228274345,
0.07219962775707245,
-0.0... |
f0c4c871-ce00-46d4-b283-65bcad168341 | The results look like
response
ββββββββdateββ¬βnew_confirmedββ¬βpercent_changeββ¬βtrendββββββ
β 2020-03-08 β 0 β nan β decrease β
β 2020-03-09 β 2 β inf β increase β
β 2020-03-10 β 0 β -100 β decrease β
β 2020-03-11 β 6 β inf β... | {"source_file": "covid19.md"} | [
-0.05809435620903969,
0.003600717056542635,
0.0019577748607844114,
0.006637689657509327,
-0.011940416879951954,
-0.10229170322418213,
-0.02704908326268196,
-0.008524884469807148,
0.0009120781323872507,
0.08143606781959534,
0.05068393424153328,
-0.0026772269047796726,
0.0654115229845047,
-0... |
bae42e7e-66ce-4b71-8a37-0325efc8d65e | description: '2.5 billion rows of climate data for the last 120 yrs'
sidebar_label: 'NOAA Global Historical Climatology Network '
slug: /getting-started/example-datasets/noaa
title: 'NOAA Global Historical Climatology Network'
doc_type: 'guide'
keywords: ['example dataset', 'noaa', 'weather data', 'sample data', 'clima... | {"source_file": "noaa.md"} | [
-0.08328356593847275,
0.0012433993397280574,
0.06681420654058456,
0.03938550502061844,
-0.02130606770515442,
-0.0704401507973671,
-0.014418038539588451,
-0.004242143593728542,
0.0011793560115620494,
-0.03289181739091873,
-0.00037112028803676367,
-0.07059912383556366,
-0.0013315231772139668,
... |
60e2636f-3a78-4dd0-bdb1-7b9e2db6b9b6 | To download:
bash
wget https://datasets-documentation.s3.eu-west-3.amazonaws.com/noaa/noaa_enriched.parquet
Original data {#original-data}
The following details the steps to download and transform the original data in preparation for loading into ClickHouse.
Download {#download}
To download the original data:... | {"source_file": "noaa.md"} | [
-0.04403778538107872,
0.015503717586398125,
-0.10713561624288559,
-0.015615974552929401,
0.03215055167675018,
-0.06595446914434433,
-0.01640271581709385,
-0.055779486894607544,
0.03409207612276077,
0.06791753321886063,
0.019022347405552864,
-0.06397020071744919,
-0.00832627434283495,
-0.13... |
675a9cfb-2149-435c-b0f1-9d862e468c19 | S-FLAG is the source flag for the observation. Not useful for our analysis and ignored.
OBS-TIME = 4-character time of observation in hour-minute format (i.e. 0700 =7:00 am). Typically not present in older data. We ignore this for our purposes.
A measurement per line would result in a sparse table structure in Cl... | {"source_file": "noaa.md"} | [
0.0047805169597268105,
0.04668380320072174,
-0.032578811049461365,
0.07319772243499756,
-0.04571365937590599,
-0.016810309141874313,
0.1113005131483078,
-0.03244653344154358,
0.006283771712332964,
0.009181439876556396,
-0.06753812730312347,
-0.08333419263362885,
0.01831532083451748,
-0.014... |
a3fdc101-a941-4a99-bfa6-38a3c2989628 | Using ClickHouse local and a simple
GROUP BY
, we can repivot our data to this structure. To limit memory overhead, we do this one file at a time.
bash
for i in {1900..2022}
do
clickhouse-local --query "SELECT station_id,
toDate32(date) as date,
anyIf(value, measurement = 'TAVG') as tempAvg,
any... | {"source_file": "noaa.md"} | [
0.03217316046357155,
0.01590733602643013,
0.008515628054738045,
0.059613704681396484,
0.011381878517568111,
-0.009726917371153831,
0.05566520616412163,
0.021402200683951378,
-0.04318050295114517,
0.01355182845145464,
0.004328252747654915,
-0.07091076672077179,
0.040088094770908356,
-0.0807... |
66b47603-61b4-426e-a43d-ed56fba40a68 | This query takes a few minutes to run and produces a 6.4 GB file,
noaa_enriched.parquet
.
Create table {#create-table}
Create a MergeTree table in ClickHouse (from the ClickHouse client).
``sql
CREATE TABLE noaa
(
station_id
LowCardinality(String),
date
Date32,
tempAvg
Int32 COMMENT 'Average temperature (tenths ... | {"source_file": "noaa.md"} | [
0.017729422077536583,
0.01619395613670349,
0.001594536006450653,
0.06646668910980225,
-0.03209659084677696,
-0.058619726449251175,
0.040637608617544174,
0.020813632756471634,
-0.0293057169765234,
0.08857988566160202,
0.030057992786169052,
-0.1455446183681488,
0.036049775779247284,
-0.07848... |
387f1253-4236-4844-9b80-22727b7cbb8a | 5 rows in set. Elapsed: 0.514 sec. Processed 1.06 billion rows, 4.27 GB (2.06 billion rows/s., 8.29 GB/s.)
```
Reassuringly consistent with the
documented record
at
Furnace Creek
as of 2023.
Best ski resorts {#best-ski-resorts}
Using a
list of ski resorts
in the united states and their respective locations,... | {"source_file": "noaa.md"} | [
0.05303359776735306,
-0.04940455034375191,
0.02796250395476818,
0.09554096311330795,
-0.0005462213885039091,
-0.04200604185461998,
0.044298991560935974,
-0.004124426282942295,
-0.07309896498918533,
0.06281287223100662,
-0.055550068616867065,
0.008131429553031921,
0.06880722939968109,
0.041... |
f3594236-ced4-43ac-9cf6-8d832784221d | Credits {#credits}
We would like to acknowledge the efforts of the Global Historical Climatology Network for preparing, cleansing, and distributing this data. We appreciate your efforts.
Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Housto... | {"source_file": "noaa.md"} | [
0.005077620502561331,
0.05795121565461159,
0.10008002817630768,
0.008622300811111927,
0.059473246335983276,
-0.05737235024571419,
-0.060591116547584534,
-0.022816916927695274,
-0.030951833352446556,
0.038136132061481476,
0.012263927608728409,
-0.08518066257238388,
-0.00825481116771698,
0.0... |
ba76107f-0eeb-46c5-8b84-61d2dee5ef7a | description: 'Dataset containing all events on GitHub from 2011 to Dec 6 2020, with
a size of 3.1 billion records.'
sidebar_label: 'GitHub events'
slug: /getting-started/example-datasets/github-events
title: 'GitHub Events Dataset'
doc_type: 'guide'
keywords: ['GitHub events', 'version control data', 'developer activ... | {"source_file": "github-events.md"} | [
0.011171426624059677,
-0.0488242469727993,
-0.03809737786650658,
0.03190697357058525,
0.039719391614198685,
-0.0266745425760746,
-0.05689448118209839,
0.01953049562871456,
0.006029770243912935,
0.07490333914756775,
0.04597404971718788,
0.037816308438777924,
-0.0018737957580015063,
-0.07991... |
9ef23afc-1aab-49ec-917c-0b4bee95b8b5 | description: 'Over 150M customer reviews of Amazon products'
sidebar_label: 'Amazon customer reviews'
slug: /getting-started/example-datasets/amazon-reviews
title: 'Amazon Customer Review'
doc_type: 'guide'
keywords: ['Amazon reviews', 'customer reviews dataset', 'e-commerce data', 'example dataset', 'getting started']... | {"source_file": "amazon-reviews.md"} | [
-0.048394810408353806,
-0.04218409210443497,
-0.08767180889844894,
0.025050293654203415,
0.023091593757271767,
-0.03881875425577164,
0.010919407941401005,
-0.04850250482559204,
0.012864463962614536,
0.023128917440772057,
0.0948445200920105,
0.00016383141337428242,
0.06896598637104034,
-0.1... |
2ab11754-28db-45cc-94bd-cf07b044b528 | Row 3:
ββββββ
review_date: 16462
marketplace: US
customer_id: 24803564 -- 24.80 million
review_id: R7K9U5OEIRJWR
product_id: B00LB8C4U4
product_parent: 524588109 -- 524.59 million
product_title: iPhone 5s Case, BUDDIBOX [Shield] Slim Dual Layer Protective Case with Kickstand for ... | {"source_file": "amazon-reviews.md"} | [
-0.020783837884664536,
0.04075075313448906,
0.012588267214596272,
-0.00023332446289714426,
0.030665719881653786,
0.004950067959725857,
0.03282249718904495,
0.036176614463329315,
-0.030007079243659973,
0.055441081523895264,
0.08497092872858047,
-0.021838052198290825,
0.08158912509679794,
-0... |
d530f6b8-f148-4f96-ac0a-2f0d8059f06f | The original data was about 70G, but compressed in ClickHouse it takes up about 30G.
Example queries {#example-queries}
Let's run some queries. Here are the top 10 most-helpful reviews in the dataset:
sql runnable
SELECT
product_title,
review_headline
FROM amazon.amazon_reviews
ORDER BY helpful_votes ... | {"source_file": "amazon-reviews.md"} | [
-0.004448557738214731,
-0.07029175758361816,
-0.10430313646793365,
0.08524695038795471,
0.0013569227885454893,
0.025045214220881462,
-0.0008529279148206115,
-0.010438543744385242,
0.0065058451145887375,
0.028333870694041252,
-0.003789351088926196,
-0.005874055437743664,
0.06325902044773102,
... |
56f97400-7952-4506-b4d5-487f2b810b36 | description: 'A new analytical benchmark for machine-generated log data'
sidebar_label: 'Brown university benchmark'
slug: /getting-started/example-datasets/brown-benchmark
title: 'Brown University Benchmark'
keywords: ['Brown University Benchmark', 'MgBench', 'log data benchmark', 'machine-generated data', 'getting st... | {"source_file": "brown-benchmark.md"} | [
-0.008299112319946289,
-0.0037010773085057735,
-0.059957653284072876,
0.054131604731082916,
0.007605767343193293,
-0.1420244723558426,
0.056194450706243515,
0.02390161342918873,
-0.08321013301610947,
0.04285008832812309,
0.008028979413211346,
-0.06640048325061798,
0.08463964611291885,
-0.0... |
1f3fbeba-6c76-44a6-830b-914c0a513318 | Run benchmark queries {#run-benchmark-queries}
sql
USE mgbench;
```sql
-- Q1.1: What is the CPU/network utilization for each web server since midnight?
SELECT machine_name,
MIN(cpu) AS cpu_min,
MAX(cpu) AS cpu_max,
AVG(cpu) AS cpu_avg,
MIN(net_in) AS net_in_min,
MAX(net_in) AS n... | {"source_file": "brown-benchmark.md"} | [
0.0772862508893013,
-0.0014233340043574572,
0.0018329720478504896,
0.07383868098258972,
-0.0597238652408123,
-0.13014139235019684,
0.07444193959236145,
-0.009500009939074516,
-0.10913528501987457,
0.04885849729180336,
-0.051339492201805115,
-0.0898352637887001,
0.026665057986974716,
-0.059... |
46c8ff7b-38f3-4cde-a0c8-e66609ba05fd | ```sql
-- Q1.6: What is the total hourly network traffic across all file servers?
SELECT dt,
hr,
SUM(net_in) AS net_in_sum,
SUM(net_out) AS net_out_sum,
SUM(net_in) + SUM(net_out) AS both_sum
FROM (
SELECT CAST(log_time AS DATE) AS dt,
EXTRACT(HOUR FROM log_time) AS hr,
... | {"source_file": "brown-benchmark.md"} | [
0.026913082227110863,
0.019597068428993225,
0.02417590469121933,
0.07038236409425735,
-0.04633261635899544,
-0.09084104746580124,
0.12198829650878906,
0.012167800217866898,
0.016103694215416908,
0.08427157998085022,
-0.04745098203420639,
-0.00949123315513134,
0.03495379537343979,
-0.054518... |
99cda51d-d2c9-4ead-b20a-16cca53dc07d | ```sql
-- Q3.1: Did the indoor temperature reach freezing over the weekend?
SELECT *
FROM logs3
WHERE event_type = 'temperature'
AND event_value <= 32.0
AND log_time >= '2019-11-29 17:00:00.000';
```
```sql
-- Q3.4: Over the past 6 months, how frequently were each door opened?
SELECT device_name,
devic... | {"source_file": "brown-benchmark.md"} | [
0.07116882503032684,
-0.024007104337215424,
0.13192525506019592,
0.10032562166452408,
0.00393138499930501,
-0.08539526909589767,
0.033813346177339554,
-0.032283715903759,
0.0060315984301269054,
0.05225304141640663,
0.0324062779545784,
-0.04798215627670288,
0.057688698172569275,
-0.01752236... |
d20851af-3ca1-4ed2-a580-ab77a259f017 | ```sql
-- Q3.6: For each device category, what are the monthly power consumption metrics?
SELECT yr,
mo,
SUM(coffee_hourly_avg) AS coffee_monthly_sum,
AVG(coffee_hourly_avg) AS coffee_monthly_avg,
SUM(printer_hourly_avg) AS printer_monthly_sum,
AVG(printer_hourly_avg) AS printer_mon... | {"source_file": "brown-benchmark.md"} | [
0.026855500414967537,
-0.010945789515972137,
-0.011644750833511353,
0.09190869331359863,
-0.03574558347463608,
-0.08289077132940292,
0.030927708372473717,
-0.036076270043849945,
0.029814334586262703,
0.00028681830735877156,
-0.000592433731071651,
-0.08864787966012955,
0.040385130792856216,
... |
9ecdc315-83b4-489a-b8b2-e0c6cbd3a051 | description: 'Dataset containing all of the commits and changes for the ClickHouse
repository'
sidebar_label: 'Github repo'
slug: /getting-started/example-datasets/github
title: 'Writing Queries in ClickHouse using GitHub Data'
keywords: ['Github']
show_related_blogs: true
doc_type: 'guide'
import Image from '@th... | {"source_file": "github.md"} | [
-0.022569265216588974,
-0.0408354327082634,
-0.040035784244537354,
0.0556245781481266,
0.05675210431218147,
-0.025337422266602516,
-0.0006817228859290481,
0.008949055336415768,
-0.062178824096918106,
0.05131852626800537,
0.06901570409536362,
0.015291954390704632,
0.054780375212430954,
-0.0... |
27af14e0-4211-4d56-a4ba-44869f5ef7e7 | Linux -
~/clickhouse git-import
- 160 mins
Downloading and inserting the data {#downloading-and-inserting-the-data}
The following data can be used to reproduce a working environment. Alternatively, this dataset is available in play.clickhouse.com - see
Queries
for further details.
Generated files for the fo... | {"source_file": "github.md"} | [
-0.04281031712889671,
-0.06477479636669159,
-0.08469875901937485,
-0.002394146053120494,
0.05635525658726692,
-0.06748238205909729,
-0.11177371442317963,
-0.020645247772336006,
-0.032361697405576706,
0.08439113199710846,
0.058510445058345795,
-0.02383624203503132,
-0.0290532186627388,
-0.0... |
e91c352a-ef42-403f-a4a5-0f3f6d361b5c | prev_commit_hash String,
prev_author LowCardinality(String),
prev_time DateTime,
file_change_type Enum('Add' = 1, 'Delete' = 2, 'Modify' = 3, 'Rename' = 4, 'Copy' = 5, 'Type' = 6),
path LowCardinality(String),
old_path LowCardinality(String),
file_extension LowCardinality(String),
file_lines_added UInt32,
file_lines_d... | {"source_file": "github.md"} | [
0.02706041932106018,
0.04629974067211151,
-0.04073842987418175,
-0.018168671056628227,
0.019010910764336586,
-0.0070829628966748714,
0.0027498991694301367,
0.07780392467975616,
0.02224385365843773,
0.07973071187734604,
0.09896668046712875,
-0.021878136321902275,
0.04719628021121025,
-0.079... |
0b6e8206-f0be-4fda-b64c-b15d145d3665 | 0 rows in set. Elapsed: 2.688 sec. Processed 266.05 thousand rows, 48.30 MB (98.97 thousand rows/s., 17.97 MB/s.)
```
line_changes
```sql
INSERT INTO git.line_changes SELECT *
FROM s3('https://datasets-documentation.s3.amazonaws.com/github/commits/clickhouse/line_changes.tsv.xz', 'TSV', ' sign Int8, line_number_... | {"source_file": "github.md"} | [
-0.03405030444264412,
-0.05661282688379288,
-0.05910445749759674,
0.008108105510473251,
-0.010147787630558014,
-0.04295364394783974,
0.024047577753663063,
-0.0006252343882806599,
0.006401877384632826,
0.054098065942525864,
0.07129473239183426,
-0.033691875636577606,
0.0456884428858757,
-0.... |
6c3c5dee-c954-4d78-a510-cab6f3a9fe64 | βββββββββββββββββtimeββ¬βcommitβββββββ¬βchange_typeββ¬βauthorββββββββββββββ¬βpathβββββββββββββββββββββββββββββββββββββββββ¬βold_pathββ¬βlines_addedββ¬βlines_deletedββ¬βcommit_messageββββββββββββββββββββββββββββββββββββ
β 2022-10-30 16:30:51 β c68ab231f91 β Modify β Alexander Tokmakov β src/Storages/StorageReplicatedMergeT... | {"source_file": "github.md"} | [
-0.02357364073395729,
0.009524907916784286,
0.01029194612056017,
-0.005953008309006691,
0.015866104513406754,
-0.08322105556726456,
0.05654730275273323,
-0.02563278004527092,
0.008831488899886608,
0.08558627218008041,
0.06668209284543991,
-0.012361399829387665,
0.07361352443695068,
-0.0066... |
dd0ff15c-8c3a-401d-9a0f-3c37ee084546 | We can also review the line changes, excluding renames i.e. we won't show changes before a rename event when the file existed under a different name:
play
```sql
SELECT
time,
substring(commit_hash, 1, 11) AS commit,
sign,
line_number_old,
line_number_new,
author,
line
FROM git.line_chang... | {"source_file": "github.md"} | [
-0.031056104227900505,
-0.03579781576991081,
0.023127511143684387,
-0.022563161328434944,
-0.012448033317923546,
-0.0032008166890591383,
0.04740598797798157,
0.013570506125688553,
0.10939860343933105,
0.07890691608190536,
0.02428325079381466,
0.01642855629324913,
0.00405453285202384,
-0.06... |
a383c8c2-09ef-4425-953d-b81e5c712152 | Note there appears to have been a broken commit history in relation to files under the
dbms
,
libs
,
tests/testflows/
directories during their renames. We also thus exclude these.
play
```sql
SELECT path
FROM
(
SELECT
old_path AS path,
max(time) AS last_time,
2 AS change_type
FRO... | {"source_file": "github.md"} | [
-0.016298901289701462,
-0.06360838562250137,
-0.0035072762984782457,
-0.02705131284892559,
0.026177331805229187,
-0.03244304284453392,
0.05574077367782593,
-0.006574583239853382,
0.0804273709654808,
0.05385192856192589,
0.018540851771831512,
0.0256949495524168,
0.02894873172044754,
0.00472... |
4b814372-0e1d-4378-b8d7-1b9aafd2a153 | --skip-paths 'generated\.cpp|^(contrib|docs?|website|libs/(libcityhash|liblz4|libdivide|libvectorclass|libdouble-conversion|libcpuid|libzstd|libfarmhash|libmetrohash|libpoco|libwidechar_width))/'
Applying this pattern to
git list-files
, reports 18155.
bash
git ls-files | grep -v -E 'generated\.cpp|^(contrib|docs?... | {"source_file": "github.md"} | [
-0.07448600232601166,
0.018908413127064705,
0.028830599039793015,
-0.03845997899770737,
0.07512759417295456,
-0.04355928301811218,
0.04045676067471504,
0.03492429479956627,
0.049668654799461365,
0.0304417684674263,
0.06960399448871613,
-0.006798570975661278,
0.024058490991592407,
-0.040262... |
a90c9a0a-8ad0-4f82-9d98-58e19d20cab7 | ββchange_typeββ¬βpathββββββββββββββββββββββββββββββββ¬βold_pathββββββββββββββββββββββββββββ¬ββββββββββββββββtimeββ¬βcommit_hashβββββββββββββββββββββββββββββββ
β Add β src/Functions/geometryFromColumn.h β β 2021-03-11 12:08:16 β 9376b676e9a9bb8911b872e1887da85a45f7479d β
β Modi... | {"source_file": "github.md"} | [
-0.041834622621536255,
-0.003750588744878769,
0.021761644631624222,
-0.07314731180667877,
0.04682988300919533,
-0.05858258157968521,
-0.06027799844741821,
-0.025432372465729713,
0.014640538021922112,
0.08131226152181625,
0.04303513467311859,
-0.034048549830913544,
0.026488158851861954,
-0.... |
3e10ed24-b910-42eb-af05-9baf35fc01a3 | List files with most modifications {#list-files-with-most-modifications}
Limiting to current files, we consider the number of modifications to be the sum of deletes and additions.
play
```sql
WITH current_files AS
(
SELECT path
FROM
(
SELECT
old_path AS path... | {"source_file": "github.md"} | [
0.015914810821413994,
-0.03451930731534958,
0.018228968605399132,
-0.011643643490970135,
0.03601008653640747,
0.0018248986452817917,
0.1349666267633438,
0.05041762441396713,
0.021364543586969376,
0.10255171358585358,
0.010056288912892342,
0.06785456836223602,
0.03346735239028931,
-0.014999... |
911d3104-2249-4545-8722-8fc69caa81a1 | History of subdirectory/file - number of lines, commits and contributors over time {#history-of-subdirectoryfile---number-of-lines-commits-and-contributors-over-time}
This would produce a large query result that is unrealistic to show or visualize if unfiltered. We, therefore, allow a file or subdirectory to be filte... | {"source_file": "github.md"} | [
-0.016005603596568108,
-0.03871601074934006,
-0.021735703572630882,
0.01930692419409752,
0.04658044874668121,
0.06530321389436722,
0.05152740702033043,
-0.03759538754820824,
0.021220270544290543,
0.04473090171813965,
-0.0000943408376770094,
-0.006938114296644926,
0.034482356160879135,
-0.0... |
8d71087e-de2e-4f35-9248-ecef729ec3cd | ββpathβββββββββββββββββββββββββββββββββββββββββ¬βnum_authorsββ
β src/Core/Settings.h β 127 β
β CMakeLists.txt β 96 β
β .gitmodules β 85 β
β src/Storages/MergeTree/MergeTreeData.cpp β 72 β
β src/CMak... | {"source_file": "github.md"} | [
0.017635036259889603,
-0.03241413086652756,
-0.027368873357772827,
-0.04148072376847267,
-0.005648620426654816,
-0.05309499055147171,
0.03206612169742584,
0.07887290418148041,
-0.026128262281417847,
0.07399284094572067,
0.04800533130764961,
0.032932113856077194,
0.043464452028274536,
-0.07... |
56f1c334-0af0-4386-80d7-ebe8235eb29f | ββfile_pathββββββββββββββββββββββββββββββββββββ¬βlineβββββββββββββββββββββββββββββββββββββββββββββββββββββββββ¬βββββββlatest_changeββ¬βany(file_change_type)ββ
β utils/compressor/test.sh β ./compressor -d < compressor.snp > compressor2 β 2011-06-17 22:19:39 β Modify β
β utils/... | {"source_file": "github.md"} | [
-0.11936286091804504,
0.07175913453102112,
-0.015211476013064384,
-0.025830406695604324,
0.06668839603662491,
-0.02278784103691578,
-0.040248893201351166,
0.03744000196456909,
0.00740476930513978,
0.058139368891716,
0.0874210074543953,
0.01175699383020401,
0.010874191299080849,
-0.07015599... |
066467f2-3613-47f1-922f-89c6f9b88406 | ββββcββ¬βpathβββββββββββββββββββββββββββββββββββββββββ¬βββββββlatest_changeββ
β 790 β src/Storages/StorageReplicatedMergeTree.cpp β 2022-10-30 16:30:51 β
β 788 β src/Storages/MergeTree/MergeTreeData.cpp β 2022-11-04 09:26:44 β
β 752 β src/Core/Settings.h β 2022-10-25 11:35:25 β
β 749 β CMakeLis... | {"source_file": "github.md"} | [
-0.023332763463258743,
-0.003127165837213397,
0.002752848668023944,
-0.01042608730494976,
0.04347454756498337,
-0.09491419047117233,
0.03313225135207176,
0.06254434585571289,
0.01987856812775135,
0.025601567700505257,
0.06248002126812935,
-0.036155227571725845,
0.04811643064022064,
-0.0546... |
0d6d0fb0-29dd-43ad-8318-01e9e60b1d69 | ββdayββ¬βbarββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β 1 β ββββββββββββββββββββββββββββββββββββ β
β 2 β ββββββββββββββββββββββββ β
β 3 β βββββββββββββββββββββββββββββββββ β
β 4 β βββββββββββββ ... | {"source_file": "github.md"} | [
-0.05390174686908722,
-0.022587832063436508,
-0.014351698569953442,
0.07572145760059357,
0.027960708364844322,
-0.08990379422903061,
0.00953185185790062,
0.0010148724541068077,
-0.01583847962319851,
0.1022038459777832,
0.05588790029287338,
-0.014732646755874157,
0.05079555884003639,
-0.077... |
9276579e-36f5-4147-b1eb-a4fb0d1c502e | Authors with the most diverse impact {#authors-with-the-most-diverse-impact}
We consider diversity here to be the number of unique files an author has contributed to.
play
```sql
SELECT
author,
uniq(path) AS num_files
FROM git.file_changes
WHERE (change_type IN ('Add', 'Modify')) AND (file_extension IN ('... | {"source_file": "github.md"} | [
0.027167312800884247,
-0.037394482642412186,
-0.015056374482810497,
-0.007514967583119869,
0.03418009355664253,
0.009741706773638725,
0.09029633551836014,
0.040081918239593506,
0.02735838107764721,
0.082981176674366,
0.008979463018476963,
0.03210823982954025,
0.028812577947974205,
-0.08013... |
bbc752da-eff3-4e11-9261-807ca5ad63d8 | Favorite files for an author {#favorite-files-for-an-author}
Here we select our founder
Alexey Milovidov
and limit our analysis to current files.
play
```sql
WITH current_files AS
(
SELECT path
FROM
(
SELECT
old_path AS path,
max(time) AS l... | {"source_file": "github.md"} | [
0.024754099547863007,
-0.035298481583595276,
0.008791450411081314,
0.0005888650193810463,
0.050624169409275055,
0.03642435744404793,
0.1569964736700058,
0.053666915744543076,
0.024290556088089943,
0.045340895652770996,
-0.02598552592098713,
0.04834091290831566,
0.07051122933626175,
-0.0402... |
72ee426a-331f-4ad3-b8ef-b8383b7f0218 | This is maybe more reflective of his areas of interest.
Largest files with lowest number of authors {#largest-files-with-lowest-number-of-authors}
For this, we first need to identify the largest files. Estimating this via a full file reconstruction, for every file, from the history of commits will be very expensive... | {"source_file": "github.md"} | [
0.02357378602027893,
-0.041916217654943466,
0.006798830348998308,
0.0020052511245012283,
0.007476718630641699,
-0.00590736186131835,
0.05321379750967026,
0.03945767506957054,
0.023509955033659935,
0.07721830904483795,
-0.022754086181521416,
0.039458248764276505,
0.028253398835659027,
0.003... |
ca4e1194-dd58-4cf3-bc13-ed3c13335c29 | 10 rows in set. Elapsed: 0.138 sec. Processed 798.15 thousand rows, 16.57 MB (5.79 million rows/s., 120.11 MB/s.)
```
Text dictionaries aren't maybe realistic, so lets restrict to code only via a file extension filter!
play
```sql
WITH current_files AS
(
SELECT path
FROM
(
... | {"source_file": "github.md"} | [
0.04204659163951874,
-0.04680045321583748,
-0.013020047917962074,
-0.020011359825730324,
0.0006442085723392665,
-0.05194120854139328,
0.07320474833250046,
0.0005753614823333919,
-0.010560920462012291,
0.07498665899038315,
0.025677809491753578,
0.057686660438776016,
0.01801707223057747,
-0.... |
deb741c4-ce20-4f9c-8d22-aa73e8f2960a | There is some recency bias in this - newer files have fewer opportunities for commits. What about if we restrict to files at least 1 yr old?
play
```sql
WITH current_files AS
(
SELECT path
FROM
(
SELECT
old_path AS path,
max(time) AS last_time,... | {"source_file": "github.md"} | [
0.008833101019263268,
-0.08369927108287811,
0.0019385311752557755,
-0.008334089070558548,
0.030405975878238678,
0.011459418572485447,
0.03607713431119919,
0.017399931326508522,
0.04860330745577812,
0.07620944827795029,
0.014405898749828339,
0.07511488348245621,
0.016372613608837128,
-0.010... |
d44cb20e-ed0d-43a6-be7d-e343511c6d7c | 10 rows in set. Elapsed: 0.143 sec. Processed 798.15 thousand rows, 18.00 MB (5.58 million rows/s., 125.87 MB/s.)
```
Commits and lines of code distribution by time; by weekday, by author; for specific subdirectories {#commits-and-lines-of-code-distribution-by-time-by-weekday-by-author-for-specific-subdirectories}
... | {"source_file": "github.md"} | [
-0.023654798045754433,
-0.021371254697442055,
-0.020779509097337723,
0.024495569989085197,
-0.025781279429793358,
-0.036867402493953705,
0.06626082211732864,
0.006888297852128744,
0.03451602905988693,
0.06127490475773811,
0.022189294919371605,
0.022508684545755386,
0.022931130602955818,
-0... |
c79d5f0f-b7a1-4326-8946-7f4e2fade002 | 24 rows in set. Elapsed: 0.039 sec. Processed 266.05 thousand rows, 14.66 MB (6.77 million rows/s., 372.89 MB/s.)
```
This distribution makes sense given most of our development team is in Amsterdam. The
bar
functions helps us visualize these distributions:
play
```sql
SELECT
hourOfDay,
bar(commits, 0, ... | {"source_file": "github.md"} | [
0.041589610278606415,
-0.04110854119062424,
0.009146220050752163,
-0.01781029999256134,
-0.06968434900045395,
-0.05565328523516655,
-0.011570721864700317,
0.010869710706174374,
0.06934966146945953,
0.09534668177366257,
0.04887959733605385,
-0.02918867953121662,
0.041960276663303375,
-0.055... |
0345e968-509d-4cd5-8616-d9a7632d9424 | ββhourOfDayββ¬βcommitsββββββββββββββββββββββββ¬βlines_addedβββββββββββββββββββββββββββββββββββββββββ¬βlines_deletedβββββββββββββββββββββββββββββββββββββββ
β 0 β βββββββββ β βββββββ β ββββββββββββ β
β 1 β ββ... | {"source_file": "github.md"} | [
-0.0036505332682281733,
-0.007561025209724903,
-0.04185090586543083,
-0.007487685885280371,
-0.040836237370967865,
-0.03468731418251991,
0.027736281976103783,
-0.07329637557268143,
-0.021637357771396637,
0.09360013902187347,
0.054234884679317474,
-0.02622048743069172,
0.038357850164175034,
... |
cf861966-149f-495e-8bd5-5b4d0c790809 | β 17 β ββββββββββββββββββ β βββββββββ β βββββββββββββ β
β 18 β ββββββββββββββββ β βββββββ β ββββββββββ β
β 19 β ββ... | {"source_file": "github.md"} | [
0.027299916371703148,
0.023766981437802315,
-0.029305437579751015,
0.003133162157610059,
0.001374101615510881,
0.0011350432178005576,
0.025802619755268097,
-0.058539729565382004,
-0.028902672231197357,
0.09316836297512054,
0.09676660597324371,
0.007784352172166109,
0.028681190684437752,
-0... |
976fbfc7-7ba9-4217-90c3-0b43fc754af0 | 24 rows in set. Elapsed: 0.038 sec. Processed 266.05 thousand rows, 14.66 MB (7.09 million rows/s., 390.69 MB/s.)
```
Matrix of authors that shows what authors tends to rewrite another authors code {#matrix-of-authors-that-shows-what-authors-tends-to-rewrite-another-authors-code}
The
sign = -1
indicates a code de... | {"source_file": "github.md"} | [
0.04478317126631737,
-0.012696857564151287,
-0.018256209790706635,
-0.004285986535251141,
-0.0538487434387207,
-0.004000967834144831,
0.05008146911859512,
-0.025623047724366188,
0.027934344485402107,
0.07629960775375366,
0.04404295235872269,
0.06450197845697403,
0.027318255975842476,
-0.13... |
2f346f4a-6e57-49d6-a0f6-db57230e6a6a | ```sql
SELECT
day_of_week,
author,
count() AS c
FROM git.commits
GROUP BY
dayOfWeek(time) AS day_of_week,
author
ORDER BY
day_of_week ASC,
c DESC
LIMIT 1 BY day_of_week
ββday_of_weekββ¬βauthorββββββββββββ¬ββββcββ
β 1 β Alexey Milovidov β 2204 β
β 2 β Alexey Milovidov β 15... | {"source_file": "github.md"} | [
-0.00078334950376302,
-0.02588997595012188,
-0.03230695798993111,
0.056491345167160034,
-0.04705201834440231,
0.0037325953599065542,
0.06332848221063614,
0.008797211572527885,
-0.029651837423443794,
0.08368809521198273,
-0.043377649039030075,
0.010550354607403278,
0.006640605162829161,
-0.... |
a3854186-faa3-434d-8a2b-bfc14ee46b14 | ββday_of_weekββ¬βauthorβββββββββββββββ¬ββtop_author_percentββ
β 1 β Alexey Milovidov β 0.3168282877768332 β
β 2 β Mikhail f. Shiryaev β 0.3523434231193969 β
β 3 β vdimir β 0.11859742484577324 β
β 4 β Nikolay Degterinsky β 0.34577318920318467 β
β 5 β Alex... | {"source_file": "github.md"} | [
-0.06541705876588821,
-0.004851421806961298,
-0.04827461019158363,
0.0016361394664272666,
0.05925016105175018,
-0.0404147170484066,
-0.01575326733291149,
0.022111257538199425,
-0.03153539448976517,
0.10488996654748917,
-0.009160969406366348,
0.000749186787288636,
0.05330744758248329,
-0.07... |
7b48cffd-9bf6-4514-a982-b0fe713ba680 | ββfolderββββββββββββββββββββββββββββ¬βavg_age_of_filesββ¬βmin_age_filesββ¬βmax_age_filesββ¬ββββcββ
β base/base β 387 β 201 β 397 β 84 β
β base/glibc-compatibility β 887 β 59 β 993 β 19 β
β base/consistent-hashing ... | {"source_file": "github.md"} | [
0.00922255776822567,
0.002901060739532113,
-0.034291334450244904,
-0.03762111812829971,
0.020692089572548866,
-0.10861364752054214,
0.01072385162115097,
0.029397541657090187,
-0.07616245001554489,
0.06391296535730362,
0.05909878760576248,
-0.018425295129418373,
0.09561052918434143,
-0.0418... |
31ac079f-909b-417b-a41c-738728b81ea3 | For this question, we need the number of lines written by an author divided by the total number of lines they have had removed by another contributor.
play
```sql
SELECT
k,
written_code.c,
removed_code.c,
removed_code.c / written_code.c AS remove_ratio
FROM
(
SELECT
author AS k,
... | {"source_file": "github.md"} | [
0.014962565153837204,
0.009156816639006138,
0.01350306160748005,
-0.013958660885691643,
-0.0674344152212143,
0.07090415060520172,
0.07534793019294739,
0.024267492815852165,
0.005751691292971373,
0.03719047084450722,
0.06142314150929451,
0.03696029633283615,
0.03150152042508125,
-0.07761973... |
2a76c714-d6cf-4dbb-adda-91f035450a8d | ββpathββββββββββββββββββββββββββββββββββββββββββββββββββββ¬βββββcββ
β src/Storages/StorageReplicatedMergeTree.cpp β 21871 β
β src/Storages/MergeTree/MergeTreeData.cpp β 17709 β
β programs/client/Client.cpp β 15882 β
β src/Storages/MergeTree/MergeTreeDataSelectExecutor... | {"source_file": "github.md"} | [
-0.04141505807638168,
-0.03342369571328163,
0.004626397974789143,
0.004965824540704489,
-0.03371899947524071,
-0.07797825336456299,
0.057369664311409,
0.038899749517440796,
0.042695190757513046,
0.03749912977218628,
0.0036287307739257812,
-0.0639253556728363,
0.021707767620682716,
-0.05086... |
73d704ac-8952-47cb-8d85-42adc1fa9f4b | play
```sql
WITH
current_files AS
(
SELECT path
FROM
(
SELECT
old_path AS path,
max(time) AS last_time,
2 AS change_type
FROM git.file_changes
GROUP BY old_path
UNION ALL
SELECT
... | {"source_file": "github.md"} | [
0.04680149629712105,
-0.06365841627120972,
-0.006871780380606651,
-0.00348671805113554,
0.002460623160004616,
0.01183319091796875,
0.07599503546953201,
0.011857002042233944,
0.009305745363235474,
0.08408278226852417,
0.01803191937506199,
0.0602678582072258,
0.03411600738763809,
-0.00389403... |
6389b537-4c8d-4404-880c-c15d55c3b6ef | 10 rows in set. Elapsed: 0.299 sec. Processed 798.15 thousand rows, 31.52 MB (2.67 million rows/s., 105.29 MB/s.)
```
What weekday does the code have the highest chance to stay in the repository? {#what-weekday-does-the-code-have-the-highest-chance-to-stay-in-the-repository}
For this, we need to identify a line of ... | {"source_file": "github.md"} | [
-0.05650338530540466,
-0.03078063577413559,
-0.01990208588540554,
0.015166079625487328,
-0.05489924177527428,
-0.00553941773250699,
0.06339779496192932,
-0.013377602212131023,
-0.017000941559672356,
0.02350863814353943,
-0.02583147957921028,
0.016191978007555008,
0.026784079149365425,
-0.0... |
594aff32-37cf-4f84-8926-fb2c45ea4088 | play
```sql
WITH
current_files AS
(
SELECT path
FROM
(
SELECT
old_path AS path,
max(time) AS last_time,
2 AS change_type
FROM git.file_changes
GROUP BY old_path
UNION ALL
SELECT
... | {"source_file": "github.md"} | [
0.04563676938414574,
-0.04087383672595024,
0.0077777449041605,
0.010036617517471313,
-0.010184836573898792,
0.013261663727462292,
0.07003055512905121,
0.00541806872934103,
-0.020863015204668045,
0.06394725292921066,
0.03321799635887146,
0.04853398725390434,
0.04009504243731499,
-0.02138831... |
63d671db-6d1a-45f9-87f2-e30c89006903 | 10 rows in set. Elapsed: 3.134 sec. Processed 16.13 million rows, 1.83 GB (5.15 million rows/s., 582.99 MB/s.)
```
Who tends to write more tests / CPP code / comments? {#who-tends-to-write-more-tests--cpp-code--comments}
There are a few ways we can address this question. Focusing on the code to test ratio, this que... | {"source_file": "github.md"} | [
0.06002547964453697,
-0.0610051155090332,
-0.053301382809877396,
0.015054651536047459,
-0.03873846307396889,
0.013108888640999794,
0.04044165089726448,
0.04685596749186516,
-0.0058349003084003925,
0.08632250130176544,
0.020344190299510956,
-0.04017161950469017,
0.045823704451322556,
-0.060... |
0104df75-cfd4-4e79-977f-83e411b86e10 | 20 rows in set. Elapsed: 0.034 sec. Processed 266.05 thousand rows, 4.65 MB (7.93 million rows/s., 138.76 MB/s.)
```
We can plot this distribution as a histogram.
play
```sql
WITH (
SELECT histogram(10)(ratio_code) AS hist
FROM
(
SELECT
author,
c... | {"source_file": "github.md"} | [
0.07620774209499359,
-0.02144732140004635,
-0.04705881327390671,
-0.027956347912549973,
-0.03708361089229584,
-0.025648802518844604,
0.06802956014871597,
0.02129710465669632,
-0.03419165313243866,
0.0796247348189354,
0.07736027985811234,
-0.020450931042432785,
0.07385184615850449,
-0.04975... |
a91fe08b-9b34-4830-b768-e3efaba848c3 | Most contributors write more code than tests, as you'd expect.
What about who adds the most comments when contributing code?
play
sql
SELECT
author,
avg(ratio_comments) AS avg_ratio_comments,
sum(code) AS code
FROM
(
SELECT
author,
commit_hash,
countIf(line_type = 'Comment'... | {"source_file": "github.md"} | [
0.00558937294408679,
-0.09327466040849686,
-0.032460812479257584,
0.007017334457486868,
-0.0347311869263649,
0.0023481324315071106,
0.054470889270305634,
0.02424345538020134,
0.01929190568625927,
0.07849948108196259,
0.009201101958751678,
-0.011436671018600464,
0.01611788384616375,
-0.0793... |
64cf1890-8d23-4f76-ad12-7c19d998126b | ββauthorβββββββββββββββββββββββ¬βcode_linesββ¬βcommentsββ¬βββββββββratio_codeββ¬βββββββweekββ
β 1lann β 8 β 0 β 1 β 2022-03-06 β
β 20018712 β 2 β 0 β 1 β 2020-09-13 β
β 243f6a8885a308d313198a2e037 β 0 β ... | {"source_file": "github.md"} | [
-0.04431293159723282,
-0.03112984634935856,
-0.07208969444036484,
-0.036060407757759094,
-0.008594905957579613,
0.041094474494457245,
0.05324958264827728,
-0.00326547771692276,
-0.014602511189877987,
0.07586994767189026,
-0.007020085584372282,
0.026900526136159897,
0.0517934113740921,
0.05... |
34b95775-351d-45c0-bcfd-c5ed3c744e1f | After calculating the average by-week offset across all authors, we sample these results by selecting every 10th week.
play
```sql
WITH author_ratios_by_offset AS
(
SELECT
author,
dateDiff('week', start_dates.start_date, contributions.week) AS week_offset,
ratio_code
... | {"source_file": "github.md"} | [
0.02478283829987049,
-0.024488316848874092,
0.021985920146107674,
0.02613505721092224,
-0.026574740186333656,
0.08634164184331894,
0.027158869430422783,
0.0614648163318634,
-0.05147644877433777,
0.03505362942814827,
0.023215269669890404,
-0.0235283225774765,
0.012684285640716553,
-0.033283... |
9c5166f3-3730-47e8-b449-b9211e9e350c | play
```sql
WITH
changes AS
(
SELECT
path,
commit_hash,
max_time,
type,
num_added,
num_deleted,
sum(num_added - num_deleted) OVER (PARTITION BY path ORDER BY max_time ASC) AS current_size,
if(current_size >... | {"source_file": "github.md"} | [
0.038095757365226746,
-0.020826278254389763,
-0.007553055416792631,
-0.023011095821857452,
-0.019238179549574852,
-0.01149779837578535,
0.049769241362810135,
0.01729212887585163,
0.03805017098784447,
0.11220090836286545,
0.04960373416543007,
0.017454400658607483,
0.016001863405108452,
-0.0... |
c45be7a3-65cd-4cfd-b568-110110173443 | play
```sql
WITH
changes AS
(
SELECT
path,
commit_hash,
max_time,
type,
num_added,
num_deleted,
sum(num_added - num_deleted) OVER (PARTITION BY path ORDER BY max_time ASC) AS current_size,
if(current_size >... | {"source_file": "github.md"} | [
0.046090852469205856,
-0.0244214478880167,
-0.01651860773563385,
-0.026985932141542435,
-0.027895187959074974,
0.009337466210126877,
0.05509492754936218,
0.025018680840730667,
0.037732720375061035,
0.11318658292293549,
0.05671011283993721,
0.021260598674416542,
0.024085665121674538,
-0.006... |
dc481c4b-66fe-482f-a06b-d362a59f8946 | play
```sql
WITH
changes AS
(
SELECT
path,
author,
commit_hash,
max_time,
type,
num_added,
num_deleted,
sum(num_added - num_deleted) OVER (PARTITION BY path ORDER BY max_time ASC) AS current_size,
... | {"source_file": "github.md"} | [
0.04203003644943237,
-0.024402311071753502,
-0.026416543871164322,
-0.022832220420241356,
-0.03620108589529991,
0.017915884032845497,
0.06930958479642868,
0.0210261270403862,
0.025654174387454987,
0.11362184584140778,
0.04776247218251228,
0.023407328873872757,
0.03258124738931656,
-0.01541... |
c8f925b0-ab60-45a6-bb0b-b3fbc0dbb797 | Most consecutive days of commits by an author {#most-consecutive-days-of-commits-by-an-author}
This query first requires us to calculate the days when an author has committed. Using a window function, partitioning by author, we can compute the days between their commits. For each commit, if the time since the last co... | {"source_file": "github.md"} | [
0.03616895154118538,
0.026513725519180298,
-0.030432164669036865,
-0.041308287531137466,
-0.06796924769878387,
0.017911052331328392,
0.010187163949012756,
-0.05167338252067566,
-0.00991013552993536,
0.01876491867005825,
-0.046800121665000916,
0.03031625971198082,
-0.017631592229008675,
0.0... |
6134cc7c-d222-4baf-a12b-276c17a9cc2e | ```sql
SELECT
time,
path,
old_path,
commit_hash,
commit_message
FROM git.file_changes
WHERE (path = 'src/Storages/StorageReplicatedMergeTree.cpp') AND (change_type = 'Rename')
βββββββββββββββββtimeββ¬βpathβββββββββββββββββββββββββββββββββββββββββ¬βold_pathββββββββββββββββββββββββββββββββββββββ¬βcommi... | {"source_file": "github.md"} | [
-0.010641125962138176,
-0.0215928852558136,
-0.04862450063228607,
0.029138537123799324,
-0.014343801885843277,
-0.0671824961900711,
0.03245177119970322,
0.034846704453229904,
0.04735076427459717,
0.09526704996824265,
0.02465677633881569,
0.011119669303297997,
0.023615214973688126,
-0.03454... |
b06a3cbe-c9cf-42fc-92c2-a6ce0d3c610f | For example,
```sql
SELECT file_path_history('src/Storages/StorageReplicatedMergeTree.cpp') AS paths
ββpathsββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β ['src/Storages/StorageReplicatedMergeTree.cpp','dbms/Storages/S... | {"source_file": "github.md"} | [
0.010330050252377987,
0.008966848254203796,
-0.059523604810237885,
0.055445726960897446,
-0.0368652306497097,
-0.05089089274406433,
0.008249987848103046,
0.037317752838134766,
0.07081883400678635,
0.10604031383991241,
0.024256153032183647,
0.022556954994797707,
0.04517001286149025,
-0.0427... |
7fdf0bbe-7a49-44e2-8ccc-c0ac415c6dac | ββline_number_newββ¬βargMax(author, time)ββ¬βargMax(line, time)βββββββββββββββββββββββββββββββββββββββββββββ
β 1 β Alexey Milovidov β #include
β
β 2 β s-kat β #include
β
β 3 β Anton Popov β #inc... | {"source_file": "github.md"} | [
-0.028018254786729813,
-0.013560133054852486,
-0.06202063709497452,
0.0304199680685997,
0.0182782169431448,
-0.011359075084328651,
0.04245283082127571,
-0.006049191579222679,
-0.0008285603253170848,
0.08720199763774872,
-0.016718463972210884,
-0.023494046181440353,
0.045389000326395035,
-0... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.