id stringlengths 36 36 | document stringlengths 3 3k | metadata stringlengths 23 69 | embeddings listlengths 384 384 |
|---|---|---|---|
72c6db24-b8a2-4f83-ad36-c44cdb4f745a | The following query demonstrates this by using the
sum() OVER
clause, which creates a running total. The
bar()
function provides a visual representation of the growth.
sql
SELECT
toDate(time) AS day,
sum(hits) AS h,
sum(h) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND 0 FOLLOWING) AS c,
bar(c, 0, 500... | {"source_file": "03_analysis-functions.md"} | [
0.004205509088933468,
-0.041067808866500854,
0.047647345811128616,
0.07087402790784836,
-0.057564493268728256,
-0.010283419862389565,
-0.016055330634117126,
-0.023965954780578613,
0.015174595639109612,
0.024039437994360924,
0.03806471824645996,
-0.07193561643362045,
0.0014003037940710783,
... |
3de60598-ab96-41c6-8cfd-a5fa07466497 | text
Row 1:
ββββββ
hist: [(10033,23224.55065359477,60.625),(23224.55065359477,37855.38888888889,15.625),(37855.38888888889,52913.5,3.5),(52913.5,69438,1.25),(69438,83102.16666666666,1.25),(83102.16666666666,94267.66666666666,2.5),(94267.66666666666,116778,1.25),(116778,186175.75,1.125),(186175.75,946963.25,1.75),(94696... | {"source_file": "03_analysis-functions.md"} | [
0.003032175125554204,
0.05371318385004997,
-0.04698898643255234,
-0.0376894474029541,
-0.09797801077365875,
-0.06049707159399986,
0.08904550969600677,
-0.003176785307005048,
-0.04643457755446434,
-0.02393268048763275,
-0.049469999969005585,
0.021960526704788208,
0.06583397090435028,
-0.015... |
fae795f0-44e7-4d04-a6bb-9bf0fab3541f | description: 'Index page for the time-series use-case guide.'
slug: /use-cases/time-series
title: 'Time-Series'
pagination_prev: null
pagination_next: null
keywords: ['time-series', 'time-based data', 'metrics', 'sensor data', 'temporal analysis', 'trend analysis']
doc_type: 'guide'
Welcome to our time-series use c... | {"source_file": "index.md"} | [
-0.06481564044952393,
-0.025660360231995583,
-0.0626431405544281,
-0.015755200758576393,
-0.014811217784881592,
-0.05480880290269852,
-0.017096512019634247,
0.005917670670896769,
-0.03695215657353401,
-0.021113891154527664,
0.009500156156718731,
0.03142759948968887,
0.0014585207682102919,
... |
03f92074-c930-461c-a6e8-dce3b3d73f95 | title: 'Basic operations - Time-series'
sidebar_label: 'Basic operations'
description: 'Basic time-series operations in ClickHouse.'
slug: /use-cases/time-series/basic-operations
keywords: ['time-series', 'basic operations', 'data ingestion', 'querying', 'filtering', 'grouping', 'aggregation']
show_related_blogs: true
... | {"source_file": "02_basic-operations.md"} | [
-0.03618517890572548,
-0.045176729559898376,
-0.02873271517455578,
0.021372176706790924,
-0.07627961784601212,
-0.07885605096817017,
0.02695896290242672,
-0.012469214387238026,
-0.0305472519248724,
0.02565131150186062,
0.030409136787056923,
0.008423329330980778,
0.002444969955831766,
0.007... |
19ae6ed4-0a75-4569-8905-07ed203d4e37 | Let's say we want to group by 4-hour intervals.
We can specify the grouping interval using the
INTERVAL
clause:
sql
SELECT
toStartOfInterval(time, INTERVAL 4 HOUR) AS interval,
sum(hits) AS hits
FROM wikistat
WHERE date(time) = '2015-07-01'
GROUP BY ALL
ORDER BY interval ASC
LIMIT 6;
Or we can use the
to... | {"source_file": "02_basic-operations.md"} | [
-0.004835045430809259,
-0.007175194099545479,
0.06039305403828621,
0.09045860916376114,
-0.05260220170021057,
0.05590911954641342,
0.022245515137910843,
0.0007985866977833211,
0.020433923229575157,
-0.0159253291785717,
0.014803636819124222,
-0.05292211472988129,
0.018703611567616463,
-0.00... |
f8f83b75-db48-4a7b-a0a6-150f2266b8c1 | text
βββββββββββββββββhourββ¬βsum(hits)ββ
β 2015-07-01 00:00:00 β 3 β
β 2015-07-01 01:00:00 β 0 β <- new value
β 2015-07-01 02:00:00 β 1 β
β 2015-07-01 03:00:00 β 0 β <- new value
β 2015-07-01 04:00:00 β 1 β
β 2015-07-01 05:00:00 β 2 β
β 2015-07-01 06:00:00 β 1 β
β... | {"source_file": "02_basic-operations.md"} | [
0.0014154870295897126,
0.03224014863371849,
-0.03257527947425842,
0.06483108550310135,
-0.014514831826090813,
0.008251598104834557,
0.02185947075486183,
-0.012836359441280365,
0.0222704466432333,
0.01911786198616028,
0.033559057861566544,
-0.04327245056629181,
-0.008133269846439362,
-0.020... |
45fb4d64-88cf-48b0-ad65-d7373406aa9a | description: 'Landing page for Machine Learning and GenAI use case guides'
pagination_prev: null
pagination_next: null
slug: /use-cases/AI/ask-ai
title: 'Machine learning and GenAI'
keywords: ['machine learning', 'genAI', 'AI']
doc_type: 'landing-page'
Machine Learning and GenAI
ClickHouse is ideally suited as a ... | {"source_file": "index.md"} | [
-0.04397366940975189,
-0.023967450484633446,
-0.0681898221373558,
0.04313334822654724,
0.001041680690832436,
-0.007226176559925079,
-0.020013587549328804,
-0.04370418190956116,
-0.05226054787635803,
-0.05073847621679306,
0.02526632882654667,
-0.001997357001528144,
0.05228368937969208,
-0.0... |
d5211c83-1705-4105-a892-43944d4e60b4 | slug: /use-cases/AI/ai-powered-sql-generation
sidebar_label: 'AI-powered SQL generation'
title: 'AI-powered SQL generation'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to use AI to generate SQL queries in ClickHouse Client or clickhouse-local.'
keywords: ['AI', 'SQL generation']
sh... | {"source_file": "ai-powered-sql-generation.md"} | [
0.027752229943871498,
-0.0212990902364254,
-0.02845640294253826,
0.07586854696273804,
-0.03537847846746445,
-0.025851594284176826,
0.04731506481766701,
-0.02459716610610485,
-0.07577690482139587,
-0.027796532958745956,
0.030101686716079712,
-0.03699558228254318,
0.1095522791147232,
-0.0334... |
00aea6ed-8856-4cd4-8bb7-51e302feb2b6 | sql
?? Can you tell me the most expensive place to buy a house in 2021?;
Once we press enter, we'll see the thought process of the AI as it tries to answer our question.
text
β’ Starting AI SQL generation with schema discovery...
βββββββββββββββββββββββββββββββββββββββββββββββββ
π§ thinking...[INFO] Text generation ... | {"source_file": "ai-powered-sql-generation.md"} | [
0.03891602158546448,
-0.06466800719499588,
-0.057770516723394394,
0.03452775254845619,
0.017255164682865143,
-0.04207487404346466,
0.06403568387031555,
0.007840607315301895,
-0.028664646670222282,
0.08671367168426514,
0.02817820943892002,
-0.0712851881980896,
0.10008470714092255,
-0.038529... |
5bad8487-962e-46ad-876a-22712197d74c | We can see that it did find the
uk_price_paid
table and generated a query for us to run.
If we run that query, we'll see the following output:
text
ββtownββββββββββββ¬βdistrictββββββββββββββββ¬βcountyβββββββββββ¬ββavg_priceββ¬βtotal_salesββ
β ILKLEY β HARROGATE β NORTH YORKSHIRE β 4310200 β ... | {"source_file": "ai-powered-sql-generation.md"} | [
0.0632680207490921,
-0.05633161962032318,
0.029642881825566292,
0.036227982491254807,
0.007203924469649792,
-0.031846653670072556,
-0.009131375700235367,
-0.03231241926550865,
-0.09111267328262329,
0.026953427121043205,
-0.004080752842128277,
-0.10798495262861252,
0.00023610488278791308,
-... |
9ae863e2-01e0-4b21-92f8-9e00acf617f4 | Notice that the AI goes through the same discovery process, even though it just examined this data:
text
β’ Starting AI SQL generation with schema discovery...
βββββββββββββββββββββββββββββββββββββββββββββββββ
π§ thinking[INFO] Text generation successful - model: claude-3-5-sonnet-latest, response_id: msg_012m4ayaSHTY... | {"source_file": "ai-powered-sql-generation.md"} | [
-0.021409617736935616,
-0.06595545262098312,
-0.05123548209667206,
0.024388018995523453,
0.06795473396778107,
-0.0444825179874897,
0.04022767022252083,
-0.027353685349225998,
-0.00580686517059803,
0.06131718307733536,
0.03635542839765549,
-0.06955170631408691,
0.07734581083059311,
-0.03810... |
4dd7891c-3374-4b29-85e4-ff5a9c489703 | This generates a more targeted query that filters specifically for Greater London and breaks down results by year.
The output of the query is shown below:
text
ββdistrictβββββββββββββ¬βyearββ¬βββavg_priceββ¬βtotal_salesββ
β CITY OF LONDON β 2019 β 14504772.73 β 299 β
β CITY OF LONDON β 2017 β 6351366.... | {"source_file": "ai-powered-sql-generation.md"} | [
0.07319741696119308,
-0.03828023746609688,
0.05146903544664383,
0.006363284774124622,
-0.014122667722404003,
-0.008626740425825119,
-0.037778228521347046,
-0.0043841274455189705,
-0.0979166030883789,
0.017295805737376213,
-0.021761104464530945,
-0.10192514955997467,
0.019377537071704865,
0... |
78894350-a39b-4103-bfd9-c9c191e52b40 | slug: /use-cases/observability/oss-monitoring
title: 'Self-managed monitoring'
sidebar_label: 'Self-managed monitoring'
description: 'Self-Managed Monitoring Guide'
doc_type: 'guide'
keywords: ['observability', 'monitoring', 'self-managed', 'metrics', 'system health']
import ObservabilityIntegrations from '@site/do... | {"source_file": "self-managed-monitoring.md"} | [
-0.003415627870708704,
-0.036547303199768066,
-0.05701768398284912,
0.043024323880672455,
0.07835771143436432,
-0.08628663420677185,
0.034879110753536224,
-0.016499631106853485,
-0.06549722701311111,
0.03812790289521217,
0.03740483522415161,
-0.047703664749860764,
0.031064195558428764,
-0.... |
b0b204a5-de47-40ff-a83d-47ce7213e5bf | slug: /use-cases/observability/cloud-monitoring
title: 'ClickHouse Cloud monitoring'
sidebar_label: 'ClickHouse Cloud monitoring'
description: 'ClickHouse Cloud Monitoring Guide'
doc_type: 'guide'
keywords: ['observability', 'monitoring', 'cloud', 'metrics', 'system health']
import AdvancedDashboard from '@site/sta... | {"source_file": "cloud-monitoring.md"} | [
0.03242228925228119,
0.010120752267539501,
-0.03585018590092659,
0.02639525942504406,
0.10957972705364227,
-0.07338806241750717,
0.0389334037899971,
0.0010547131532803178,
-0.05665986239910126,
0.06946500390768051,
0.06023581326007843,
-0.06317642331123352,
0.0645931139588356,
0.0501816272... |
d055475b-8a05-4124-aadd-6f2f81b916dc | Both dashboards offer immediate visibility into service health and performance without external dependencies, distinguishing them from external debugging-focused tools like ClickStack.
For detailed dashboard features and available metrics, see the
advanced dashboard documentation
.
Query insights and resource moni... | {"source_file": "cloud-monitoring.md"} | [
-0.05040443316102028,
-0.03922499716281891,
-0.061595767736434937,
0.018512874841690063,
0.03621410205960274,
-0.11562059074640274,
0.04828425869345665,
-0.008222275413572788,
0.02962278202176094,
0.016691964119672775,
-0.03908349201083183,
-0.0237082839012146,
0.01397131010890007,
0.01584... |
d171be41-2de9-499a-a043-9c9eeaa3277a | :::note
Users can also collect metrics from the ClickHouse Cloud Prometheus endpoint via an OpenTelemetry Collector and forward them to a separate ClickStack deployment for visualization.
:::
System impact considerations {#system-impact}
All of the above approaches use a mixture of either relying on Prometheus ... | {"source_file": "cloud-monitoring.md"} | [
-0.07808102667331696,
-0.018952226266264915,
-0.02135520987212658,
0.006877236068248749,
0.008498906157910824,
-0.15630361437797546,
-0.03720724210143089,
-0.003132663434371352,
-0.014659658074378967,
0.054521847516298294,
-0.04159240797162056,
-0.06786403805017471,
-0.00335917086340487,
-... |
d7ee9b81-4edc-4cc1-af0a-80c50e80311c | slug: /use-cases/observability
title: 'Observability'
pagination_prev: null
pagination_next: null
description: 'Landing page for the Observability use case guide'
keywords: ['observability', 'logs', 'traces', 'metrics', 'OpenTelemetry', 'Grafana', 'OTel']
doc_type: 'guide'
ClickHouse offers unmatched speed, scale, ... | {"source_file": "index.md"} | [
-0.04273652285337448,
-0.054020438343286514,
-0.038004547357559204,
-0.012852302752435207,
-0.04105694219470024,
-0.07396583259105682,
-0.0025977976620197296,
-0.005158842075616121,
-0.07456136494874954,
0.030774511396884918,
0.029403818771243095,
0.01742994599044323,
0.032357197254896164,
... |
a49ebaf6-7322-47a4-b110-837a224a01a9 | | Page | Description |
|-------------------------------------------------------------|-----------------... | {"source_file": "index.md"} | [
-0.028623318299651146,
0.07186510413885117,
-0.0010045311646535993,
0.021435556933283806,
-0.025794483721256256,
0.08885795623064041,
-0.0018362681148573756,
0.016397938132286072,
0.02181084081530571,
-0.05627714842557907,
0.010358192026615143,
0.0484403632581234,
0.007398650515824556,
-0.... |
17a1468a-a184-4132-80e7-6d50896aa653 | slug: /use-cases/data-lake/unity-catalog
sidebar_label: 'Unity catalog'
title: 'Unity catalog'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query
your data in S3 buckets using ClickHouse and the Unity Catalog.'
keywords: ['Unity', 'Data Lake']
show_rela... | {"source_file": "unity_catalog.md"} | [
0.016367726027965546,
-0.04051130264997482,
-0.054602134972810745,
0.008512072265148163,
0.03266718611121178,
0.06773553788661957,
0.026384424418210983,
0.007855048403143883,
-0.024396410211920738,
-0.010635608807206154,
0.0318833626806736,
-0.02419183775782585,
0.11465480923652649,
-0.076... |
002a811e-04d9-4169-9d1a-24f33c4ead95 | Now that the connection is in place, you can start querying via the Unity catalog. For example:
```sql
USE unity;
SHOW TABLES;
ββnameββββββββββββββββββββββββββββββββββββββββββββββββ
β clickbench.delta_hits β
β demo.fake_user β
β information_schema.c... | {"source_file": "unity_catalog.md"} | [
0.04888647049665451,
-0.06893181055784225,
-0.1640840470790863,
0.03324026241898537,
-0.07461598515510559,
-0.03458767384290695,
0.056907523423433304,
0.012214653193950653,
-0.1470005214214325,
0.033819954842329025,
0.012986939400434494,
-0.06538432836532593,
0.050489846616983414,
-0.02089... |
44c16170-d5c8-4728-9f21-33e68b62cf98 | ```
Loading data from your Data Lake into ClickHouse {#loading-data-from-your-data-lake-into-clickhouse}
If you need to load data from Databricks into ClickHouse, start by creating a local ClickHouse table:
sql
CREATE TABLE hits
(
`WatchID` Int64,
`JavaEnable` Int32,
`Title` String,
`GoodEvent` In... | {"source_file": "unity_catalog.md"} | [
0.0371149480342865,
-0.03420150652527809,
-0.055620890110731125,
-0.00977572426199913,
-0.047836754471063614,
-0.008964826352894306,
0.035915765911340714,
0.04042617976665497,
-0.08377286791801453,
0.019499866291880608,
0.011241226457059383,
-0.0444403775036335,
0.0462956428527832,
-0.0846... |
a31a1adf-0f94-45cd-8c76-4487d783fe85 | slug: /use-cases/data-lake/onelake-catalog
sidebar_label: 'Fabric OneLake'
title: 'Fabric OneLake'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query your data in Microsoft OneLake.'
keywords: ['OneLake', 'Data Lake', 'Fabric']
show_related_blogs: true
d... | {"source_file": "onelake_catalog.md"} | [
0.04773430526256561,
0.0010843289783224463,
-0.01784418150782585,
0.0389203205704689,
0.012302227318286896,
0.0781865045428276,
0.026199793443083763,
-0.010822840966284275,
-0.08102589100599289,
-0.006126509513705969,
0.022383669391274452,
-0.028274884447455406,
0.105659618973732,
-0.01820... |
2863095b-85fd-4632-9aba-304c7f8c412b | To query a table:
``sql
SELECT *
FROM onelake_catalog.
year_2017.green_tripdata_2017`
LIMIT 1
Query id: db6b4bda-cc58-4ca1-8891-e0d14f02c890
Row 1:
ββββββ
VendorID: 2
lpep_pickup_datetime: 2017-05-18 16:55:43.000000
lpep_dropoff_datetime: 2017-05-18 18:04:11.000000
store_and_fwd_flag: N
RatecodeI... | {"source_file": "onelake_catalog.md"} | [
0.07932141423225403,
0.003211893606930971,
0.0055632502771914005,
0.03886799514293671,
-0.01798904500901699,
0.0046807099133729935,
0.05159628018736839,
0.0043216547928750515,
-0.0325346440076828,
0.012885259464383125,
0.07595761865377426,
-0.11667748540639877,
-0.023417362943291664,
-0.09... |
d814c420-96d2-4d33-b40d-94ec61ec8a66 | ββstatementββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
1. β CREATE TABLE onelake_catalog.
year_2017.green_tripdata_2017
... | {"source_file": "onelake_catalog.md"} | [
0.06406445056200027,
0.022889966145157814,
-0.008358280174434185,
-0.006640339735895395,
-0.0432722233235836,
0.028686977922916412,
-0.0006230557337403297,
0.021131029352545738,
-0.07686907052993774,
0.019908063113689423,
0.14427156746387482,
-0.14784160256385803,
0.014467664994299412,
-0.... |
b4169b0e-d732-4841-abeb-35da96195380 | tip_amount
Nullable(Float64), β΄β
ββ³
tolls_amount
Nullable(Float64), ... | {"source_file": "onelake_catalog.md"} | [
0.005792622920125723,
0.07173751294612885,
-0.03717707097530365,
0.02531939372420311,
0.05435303598642349,
-0.011607948690652847,
0.045328572392463684,
0.03867928683757782,
-0.03605364263057709,
0.06837217509746552,
0.09711770713329315,
-0.14289432764053345,
0.003912803716957569,
-0.014935... |
3fdba888-7e00-4a38-9f17-34ba67e7864b | Loading data from your Data Lake into ClickHouse {#loading-data-from-onelake-into-clickhouse}
If you need to load data from OneLake into ClickHouse:
``sql
CREATE TABLE trips
ENGINE = MergeTree
ORDER BY coalesce(VendorID, 0)
AS SELECT *
FROM onelake_catalog.
year_2017.green_tripdata_2017`
Query id: d15983a6-ef6a-4... | {"source_file": "onelake_catalog.md"} | [
0.08094809949398041,
-0.02270466461777687,
0.021357130259275436,
-0.0025401690509170294,
-0.07601873576641083,
-0.08785959333181381,
0.013087410479784012,
0.017539184540510178,
-0.08797777444124222,
0.032008737325668335,
0.023864490911364555,
-0.002045780885964632,
0.0030447060707956553,
-... |
d7b05ce1-7c93-4119-a6ae-3a35dcbfed22 | slug: /use-cases/data-lake/nessie-catalog
sidebar_label: 'Nessie catalog'
title: 'Nessie catalog'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query
your data using ClickHouse and the Nessie Catalog.'
keywords: ['Nessie', 'REST', 'Transactional', 'Data ... | {"source_file": "nessie_catalog.md"} | [
-0.015241927467286587,
-0.030525831505656242,
-0.014074880629777908,
0.010927198454737663,
-0.010413631796836853,
0.02260785549879074,
-0.03419661894440651,
0.015598504804074764,
-0.01254849974066019,
0.023903314024209976,
0.04217566177248955,
-0.017135199159383774,
0.08962564170360565,
-0... |
c60f267b-db8a-49b2-acce-2174ae8d29ac | Step 1:
Create a new folder in which to run the example, then create a file
docker-compose.yml
with the following configuration:
```yaml
version: '3.8'
services:
nessie:
image: ghcr.io/projectnessie/nessie:latest
ports:
- "19120:19120"
environment:
- nessie.version.store.type=IN_MEMORY
... | {"source_file": "nessie_catalog.md"} | [
0.0025352940429002047,
-0.06737565994262695,
-0.049000516533851624,
-0.06072157993912697,
0.01872977614402771,
-0.0070024398155510426,
-0.10895359516143799,
-0.031284112483263016,
0.006815451662987471,
0.029623206704854965,
0.007849163375794888,
-0.038966573774814606,
0.0032281705643981695,
... |
8dc80819-71b8-4452-a25e-e822440c3f0a | Connecting to Local Nessie Catalog {#connecting-to-local-nessie-catalog}
Connect to your ClickHouse container:
bash
docker exec -it nessie-clickhouse clickhouse-client
Then create the database connection to the Nessie catalog:
```sql
SET allow_experimental_database_iceberg = 1;
CREATE DATABASE demo
ENGINE = D... | {"source_file": "nessie_catalog.md"} | [
0.07582482695579529,
-0.02350533939898014,
-0.021158887073397636,
0.0034405565820634365,
-0.09477970749139786,
-0.0276379082351923,
0.0036072770599275827,
0.011816328391432762,
-0.07293743640184402,
-0.014499550685286522,
0.00919591635465622,
-0.09586462378501892,
0.049789074808359146,
-0.... |
07b7195d-03a2-447f-a776-30d1ae0371b3 | :::note Backticks required
Backticks are required because ClickHouse doesn't support more than one namespace.
:::
To inspect the table DDL:
sql
SHOW CREATE TABLE `default.taxis`;
sql title="Response"
ββstatementββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β CREATE TABLE d... | {"source_file": "nessie_catalog.md"} | [
0.012929537333548069,
-0.07699647545814514,
-0.032761652022600174,
0.07289308309555054,
-0.07348871231079102,
0.04610983654856682,
0.04548081010580063,
-0.0176620464771986,
-0.04246284440159798,
0.04442403092980385,
0.09020424634218216,
-0.07915861904621124,
0.01745803840458393,
-0.0312821... |
7c276928-0464-4857-8fd1-da9d337aa22b | If you need to load data from the Nessie catalog into ClickHouse, start by creating a local ClickHouse table:
sql
CREATE TABLE taxis
(
`VendorID` Int64,
`tpep_pickup_datetime` DateTime64(6),
`tpep_dropoff_datetime` DateTime64(6),
`passenger_count` Float64,
`trip_distance` Float64,
`RatecodeID`... | {"source_file": "nessie_catalog.md"} | [
0.061767641454935074,
-0.0017674182308837771,
0.02526819333434105,
0.002354447962716222,
-0.09840244799852371,
-0.0048482357524335384,
0.005432145204395056,
0.07076147943735123,
-0.09462101012468338,
0.041626062244176865,
0.06324492394924164,
-0.09011328965425491,
-0.007652281783521175,
-0... |
3220bc23-b7d8-4288-b00b-598d17fad2c5 | slug: /use-cases/data-lake/rest-catalog
sidebar_label: 'REST catalog'
title: 'REST catalog'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query
your data using ClickHouse and the REST Catalog.'
keywords: ['REST', 'Tabular', 'Data Lake', 'Iceberg']
show_r... | {"source_file": "rest_catalog.md"} | [
-0.011449034325778484,
-0.020928362384438515,
-0.03930157423019409,
-0.0008811361039988697,
0.013360520824790001,
0.038502391427755356,
-0.060196537524461746,
0.005009065382182598,
0.005715842358767986,
0.012907684780657291,
0.020900018513202667,
0.004266528878360987,
0.09460021555423737,
... |
ad07b7d1-bea1-4425-b68f-d5b91ec0df63 | Step 3:
Run the following command to start the services:
bash
docker compose up
Step 4:
Wait for all services to be ready. You can check the logs:
bash
docker-compose logs -f
:::note
The REST catalog setup requires that sample data be loaded into the Iceberg tables first. Make sure the Spark environment has c... | {"source_file": "rest_catalog.md"} | [
0.04992754012346268,
-0.07097876816987991,
-0.025992536917328835,
-0.007795579265803099,
-0.043663471937179565,
-0.004065288230776787,
-0.044176891446113586,
0.015010327100753784,
-0.057276736944913864,
-0.014434797689318657,
-0.034281227737665176,
-0.045833323150873184,
0.02931583859026432,... |
25278767-59ec-4edb-b6b6-24934df35dce | :::note Backticks required
Backticks are required because ClickHouse doesn't support more than one namespace.
:::
To inspect the table DDL:
sql
SHOW CREATE TABLE `default.taxis`;
sql title="Response"
ββstatementββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β CREATE TABLE d... | {"source_file": "rest_catalog.md"} | [
0.012929537333548069,
-0.07699647545814514,
-0.032761652022600174,
0.07289308309555054,
-0.07348871231079102,
0.04610983654856682,
0.04548081010580063,
-0.0176620464771986,
-0.04246284440159798,
0.04442403092980385,
0.09020424634218216,
-0.07915861904621124,
0.01745803840458393,
-0.0312821... |
1182b7f7-4c98-4288-a7f4-be05960a0f8e | If you need to load data from the REST catalog into ClickHouse, start by creating a local ClickHouse table:
sql
CREATE TABLE taxis
(
`VendorID` Int64,
`tpep_pickup_datetime` DateTime64(6),
`tpep_dropoff_datetime` DateTime64(6),
`passenger_count` Float64,
`trip_distance` Float64,
`RatecodeID` F... | {"source_file": "rest_catalog.md"} | [
0.0690087080001831,
0.00040270143654197454,
0.015826571732759476,
-0.007079016417264938,
-0.10496096312999725,
0.01004656683653593,
-0.016525594517588615,
0.053023748099803925,
-0.07619071751832962,
0.031100433319807053,
0.06941045075654984,
-0.08197567611932755,
0.0002229477686341852,
-0.... |
30de7290-1b6a-46f7-8f32-cab6ff6de5b7 | description: 'Landing page for the Data Lake use case guide'
pagination_prev: null
pagination_next: null
slug: /use-cases/data-lake
title: 'Data Lake'
keywords: ['data lake', 'glue', 'unity', 'rest', 'OneLake']
doc_type: 'landing-page'
ClickHouse supports integration with multiple catalogs (Unity, Glue, REST, Polar... | {"source_file": "index.md"} | [
0.014253715984523296,
-0.06826778501272202,
-0.03696928545832634,
-0.056083157658576965,
-0.007413518149405718,
0.017217928543686867,
-0.006803077179938555,
-0.02189575880765915,
-0.03473411127924919,
-0.0036306041292846203,
0.02504224330186844,
-0.02052401937544346,
0.1189589574933052,
-0... |
e14c6b36-a708-4119-956f-0b1f2a7fe166 | slug: /use-cases/data-lake/lakekeeper-catalog
sidebar_label: 'Lakekeeper catalog'
title: 'Lakekeeper catalog'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query
your data using ClickHouse and the Lakekeeper Catalog.'
keywords: ['Lakekeeper', 'REST', 'Ta... | {"source_file": "lakekeeper_catalog.md"} | [
-0.0390450693666935,
0.0011892261682078242,
-0.024245621636509895,
-0.012406324967741966,
0.052773162722587585,
0.013057120144367218,
-0.05340324342250824,
0.0008192059467546642,
-0.013887890614569187,
0.00896515604108572,
0.03420038893818855,
-0.017686234787106514,
0.11246544122695923,
-0... |
821cb384-6bcd-4ae2-9888-51f5b2990623 | migrate:
image: quay.io/lakekeeper/catalog:latest-main
environment:
- LAKEKEEPER__PG_ENCRYPTION_KEY=This-is-NOT-Secure!
- LAKEKEEPER__PG_DATABASE_URL_READ=postgresql://postgres:postgres@db:5432/postgres
- LAKEKEEPER__PG_DATABASE_URL_WRITE=postgresql://postgres:postgres@db:5432/postgres
-... | {"source_file": "lakekeeper_catalog.md"} | [
-0.029448730871081352,
-0.03421558439731598,
-0.03281082957983017,
-0.022104954347014427,
0.10982145369052887,
-0.05567997694015503,
-0.020572278648614883,
-0.03384242206811905,
-0.022641820833086967,
0.01836789771914482,
0.034804169088602066,
-0.023505201563239098,
0.006104311440140009,
-... |
ab8c414f-f2a6-449d-9b42-6c53058d9a0f | clickhouse:
image: clickhouse/clickhouse-server:head
container_name: lakekeeper-clickhouse
user: '0:0' # Ensures root permissions
ports:
- "8123:8123"
- "9000:9000"
volumes:
- clickhouse_data:/var/lib/clickhouse
- ./clickhouse/data_import:/var/lib/clickhouse/data_import # M... | {"source_file": "lakekeeper_catalog.md"} | [
0.015085541643202305,
-0.04277558997273445,
-0.01819947361946106,
-0.0010710207279771566,
0.01985316351056099,
-0.08539249747991562,
0.014241897501051426,
0.004835129715502262,
-0.031415365636348724,
0.009100295603275299,
0.0018587803933769464,
-0.024334661662578583,
0.002257907297462225,
... |
4223cf31-b69b-4b04-9f49-216a86e16c42 | :::note Backticks required
Backticks are required because ClickHouse doesn't support more than one namespace.
:::
To inspect the table DDL:
sql
SHOW CREATE TABLE `default.taxis`;
sql title="Response"
ββstatementββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β CREATE TABLE d... | {"source_file": "lakekeeper_catalog.md"} | [
0.012929537333548069,
-0.07699647545814514,
-0.032761652022600174,
0.07289308309555054,
-0.07348871231079102,
0.04610983654856682,
0.04548081010580063,
-0.0176620464771986,
-0.04246284440159798,
0.04442403092980385,
0.09020424634218216,
-0.07915861904621124,
0.01745803840458393,
-0.0312821... |
d140235b-1580-45bd-b8bd-0110d48b7d73 | If you need to load data from the Lakekeeper catalog into ClickHouse, start by creating a local ClickHouse table:
sql
CREATE TABLE taxis
(
`VendorID` Int64,
`tpep_pickup_datetime` DateTime64(6),
`tpep_dropoff_datetime` DateTime64(6),
`passenger_count` Float64,
`trip_distance` Float64,
`Ratecod... | {"source_file": "lakekeeper_catalog.md"} | [
0.04842790216207504,
0.0041345288045704365,
0.026828626170754433,
-0.013698738999664783,
-0.05745283514261246,
-0.017778821289539337,
0.022710692137479782,
0.062225256115198135,
-0.09469111263751984,
0.039124369621276855,
0.07247357070446014,
-0.1014794185757637,
0.005161311477422714,
-0.0... |
90379ae9-c690-4231-a110-50049ced8a81 | slug: /use-cases/data-lake/glue-catalog
sidebar_label: 'AWS Glue catalog'
title: 'AWS Glue catalog'
pagination_prev: null
pagination_next: null
description: 'In this guide, we will walk you through the steps to query
your data in S3 buckets using ClickHouse and the AWS Glue Data Catalog.'
keywords: ['Glue', 'Data Lake... | {"source_file": "glue_catalog.md"} | [
-0.03573083505034447,
-0.05492328479886055,
-0.03172113001346588,
0.006087954621762037,
0.015664858743548393,
0.06349944323301315,
-0.004462813958525658,
-0.027052124962210655,
-0.027912190183997154,
-0.017779607325792313,
0.0456136018037796,
0.012829676270484924,
0.10015957802534103,
-0.0... |
41ea01f7-311d-4f76-8444-f8ed80033f52 | sql title="Response"
ββstatementββββββββββββββββββββββββββββββββββββββββββββββββ
1.β CREATE TABLE glue.`iceberg-benchmark.hitsiceberg` β
β ( β
β `watchid` Nullable(Int64), β
β `javaenable` Nullable(Int32), ... | {"source_file": "glue_catalog.md"} | [
-0.01664247363805771,
-0.051761433482170105,
-0.03289896994829178,
0.07023666054010391,
-0.06704369187355042,
-0.008616253733634949,
0.022169284522533417,
0.01714601367712021,
-0.05941448733210564,
0.04462091252207756,
-0.004408309701830149,
-0.05549655109643936,
0.05462374910712242,
-0.02... |
e782873d-368e-4a62-83ef-c82c559e470f | β `windowclientwidth` Nullable(Int32), β
β `windowclientheight` Nullable(Int32), β
β `clienttimezone` Nullable(Int32), β
β `clienteventtime` Nullable(DateTime64(6)), β
β `silverlightversion1` Nullable(Int32), β
β `sil... | {"source_file": "glue_catalog.md"} | [
-0.059018414467573166,
-0.04073530435562134,
-0.0498325414955616,
0.025750618427991867,
0.01707443967461586,
-0.02410135045647621,
0.0003643874078989029,
0.01981423981487751,
-0.08993460983037949,
0.003441904904320836,
0.027307765558362007,
-0.037646111100912094,
0.024719350039958954,
-0.0... |
c13ab583-cee8-44fa-9433-b5e4e968124d | β `paramprice` Nullable(Int32), β
β `paramorderid` Nullable(String), β
β `paramcurrency` Nullable(String), β
β `paramcurrencyid` Nullable(Int32), β
β `openstatservicename` Nullable(String), β
β `ope... | {"source_file": "glue_catalog.md"} | [
-0.05144518241286278,
-0.015177526511251926,
-0.11052681505680084,
-0.017665846273303032,
-0.049945589154958725,
-0.02885112538933754,
-0.013042602688074112,
-0.012629046104848385,
-0.08087251335382462,
0.035332538187503815,
0.06026100739836693,
-0.029514653608202934,
0.09920217841863632,
... |
952de2d0-9058-4c8f-8a42-1dd121269f2b | Loading data from your Data Lake into ClickHouse {#loading-data-into-clickhouse}
If you need to load data from Databricks into ClickHouse, start by creating a
local ClickHouse table: | {"source_file": "glue_catalog.md"} | [
0.027276575565338135,
-0.042578112334012985,
-0.006739123724400997,
-0.0184137262403965,
-0.02076299488544464,
-0.07125741243362427,
-0.0023701984900981188,
0.003581037512049079,
-0.08697876334190369,
-0.02670340985059738,
0.013542305678129196,
-0.02823706716299057,
0.009371538646519184,
-... |
f5afb3c4-62dd-400e-bdd8-2c68fb3a6eff | sql title="Query"
CREATE TABLE hits
(
`WatchID` BIGINT NOT NULL,
`JavaEnable` SMALLINT NOT NULL,
`Title` TEXT NOT NULL,
`GoodEvent` SMALLINT NOT NULL,
`EventTime` TIMESTAMP NOT NULL,
`EventDate` Date NOT NULL,
`CounterID` INTEGER NOT NULL,
`ClientIP` INTEGER NOT NULL,
`RegionID` INTE... | {"source_file": "glue_catalog.md"} | [
0.04598699137568474,
0.006550278980284929,
-0.07167363166809082,
0.05816339701414108,
-0.06030077859759331,
0.01941422000527382,
0.05230845510959625,
-0.005195398349314928,
-0.01855943351984024,
0.05538256838917732,
0.049358904361724854,
-0.05827464535832405,
0.08997131139039993,
-0.086265... |
e96765ab-57ba-4931-b079-18ae50ee6cef | `SocialAction` TEXT NOT NULL,
`HTTPError` SMALLINT NOT NULL,
`SendTiming` INTEGER NOT NULL,
`DNSTiming` INTEGER NOT NULL,
`ConnectTiming` INTEGER NOT NULL,
`ResponseStartTiming` INTEGER NOT NULL,
`ResponseEndTiming` INTEGER NOT NULL,
`FetchTiming` INTEGER NOT NULL,
`SocialSourceNetworkID... | {"source_file": "glue_catalog.md"} | [
-0.015517369844019413,
0.04694758355617523,
-0.05799047276377678,
0.012799271382391453,
-0.057354819029569626,
-0.01951620727777481,
-0.0018745927372947335,
-0.017536098137497902,
-0.016426512971520424,
0.03294381871819496,
0.05623090639710426,
0.00900551863014698,
0.12544089555740356,
-0.... |
90063bf9-a84c-438f-bf65-54213e04637a | Then load the data from your Iceberg table:
sql title="Query"
INSERT INTO default.hits
SELECT * FROM glue.`iceberg-benchmark.hitsiceberg`; | {"source_file": "glue_catalog.md"} | [
0.005638807080686092,
-0.06669886410236359,
-0.05216499790549278,
0.11080341786146164,
-0.10662741214036942,
0.013593642972409725,
-0.029667222872376442,
0.0961337685585022,
-0.10057427734136581,
-0.007337307557463646,
0.006227412261068821,
-0.019848451018333435,
0.07564685493707657,
-0.10... |
db343135-c557-4496-ade9-d52bf15ee070 | slug: /use-cases/AI/jupyter-notebook
sidebar_label: 'Exploring data with Jupyter notebooks and chDB'
title: 'Exploring data in Jupyter notebooks with chDB'
description: 'This guide explains how to setup and use chDB to explore data from ClickHouse Cloud or local files in Jupyer notebooks'
keywords: ['ML', 'Jupyer', 'ch... | {"source_file": "jupyter-notebook.md"} | [
0.05201060697436333,
0.028564365580677986,
-0.0017921481048688293,
-0.022050127387046814,
0.047857169061899185,
0.005217901896685362,
-0.006251544691622257,
0.03974322974681854,
-0.027582891285419464,
0.0037520972546190023,
0.09030506014823914,
-0.04767189547419548,
0.12757566571235657,
-0... |
1d947569-910c-4d94-bf15-e114308e41f1 | :::note
The environment variables above persist only as long as your terminal session.
To set them permanently, add them to your shell configuration file.
:::
Now activate your virtual environment.
From within your virtual environment, install Jupyter Notebook with the following command:
python
pip install notebook... | {"source_file": "jupyter-notebook.md"} | [
0.05305223539471626,
-0.007883304730057716,
0.03137373551726341,
-0.03456754982471466,
-0.020400134846568108,
-0.0034050748217850924,
-0.04226463660597801,
-0.003968190401792526,
0.010576046071946621,
0.013892436400055885,
0.003839259734377265,
-0.06218571215867996,
0.03533802926540375,
-0... |
0ae828f3-4a40-48f8-bb37-be99a9aa7bb2 | The
remoteSecure
function connects to the remote ClickHouse Cloud service, runs the query and returns the result.
Depending on the size of your data, this could take a few seconds.
In this case we return an average price point per year, and filter by
town='LONDON'
.
The result is then stored as a DataFrame in a vari... | {"source_file": "jupyter-notebook.md"} | [
0.08231665194034576,
-0.012441790662705898,
-0.017880816012620926,
0.02673378959298134,
0.0030743677634745836,
-0.03812050819396973,
-0.007868747226893902,
-0.022907942533493042,
0.026805812492966652,
0.025422843173146248,
0.03632766380906105,
-0.07297144830226898,
-0.016703613102436066,
-... |
d7120226-727a-4406-bec3-a8497c514ff8 | Although we are missing data from 2020 onwards, we can plot the two datasets against each other for the years 1995 to 2019.
In a new cell run the following command:
```python
Create a figure with two y-axes
fig, ax1 = plt.subplots(figsize=(14, 8))
Plot houses sold on the left y-axis
color = 'tab:blue'
ax1.set... | {"source_file": "jupyter-notebook.md"} | [
0.01752748154103756,
-0.06106003746390343,
0.0554494671523571,
0.004027205053716898,
0.0461350753903389,
-0.03921537473797798,
-0.07233557850122452,
-0.0014813876478001475,
-0.04967353492975235,
-0.039857327938079834,
0.0874665305018425,
-0.040843624621629715,
-0.03359474614262581,
-0.0334... |
8c7af61f-8f6e-4946-89f3-28b5d02fbf94 | slug: /use-cases/AI/marimo-notebook
sidebar_label: 'Exploring data with Marimo notebooks and chDB'
title: 'Exploring data with Marimo notebooks and chDB'
description: 'This guide explains how to setup and use chDB to explore data from ClickHouse Cloud or local files in Marimo notebooks'
keywords: ['ML', 'Marimo', 'chDB... | {"source_file": "marimo-notebook.md"} | [
0.06774505972862244,
0.021965255960822105,
-0.0488240048289299,
-0.02032439224421978,
0.05106945335865021,
0.006730773486196995,
-0.0011048841988667846,
0.04676569998264313,
-0.042237117886543274,
-0.019528530538082123,
0.10693686455488205,
-0.04512249678373337,
0.10903656482696533,
-0.033... |
9edd847d-b643-4bc7-b54a-511905e52f25 | Setting up credentials {#setting-up-credentials}
bash
export CLICKHOUSE_CLOUD_HOSTNAME=<HOSTNAME>
export CLICKHOUSE_CLOUD_USER=default
export CLICKHOUSE_CLOUD_PASSWORD=your_actual_password
:::note
The environment variables above persist only as long as your terminal session.
To set them permanently, add them to you... | {"source_file": "marimo-notebook.md"} | [
0.03192795813083649,
-0.060618940740823746,
-0.07456004619598389,
-0.04268364980816841,
-0.03859163075685501,
-0.015958962962031364,
-0.018295805901288986,
0.027921240776777267,
-0.01736619882285595,
0.049211177974939346,
0.011078122071921825,
-0.07250001281499863,
0.024041827768087387,
0.... |
9c167c07-02a5-4933-993e-dd7eb27da3c3 | In the query we are using the
remoteSecure
function to connect to ClickHouse Cloud.
The
remoteSecure
functions takes as parameters:
- a connection string
- the name of the database and table to use
- your username
- your password
As a security best practice, you should prefer using environment variables for the... | {"source_file": "marimo-notebook.md"} | [
0.07868468016386032,
-0.007513813674449921,
-0.027591433376073837,
0.023581156507134438,
-0.05076226592063904,
-0.05276193469762802,
0.046363674104213715,
-0.02147870697081089,
0.006437080446630716,
-0.0004654598596971482,
0.023484643548727036,
-0.04723748564720154,
0.04512364789843559,
-0... |
10ef7bc1-22a0-4ecc-abd4-6eda7c58590c | fig_reactive.update_traces(mode='lines+markers')
fig_reactive.update_layout(hovermode='x unified')
fig_reactive
```
Now when you select a town from the drop-down the chart will update dynamically:
Exploring price distributions with interactive box plots {#exploring-price-distributions}
Let's dive deeper into th... | {"source_file": "marimo-notebook.md"} | [
-0.01948726363480091,
-0.023057904094457626,
0.08684424310922623,
0.0967002734541893,
-0.014949923381209373,
-0.046317875385284424,
-0.06548009067773819,
0.008068420924246311,
-0.054965537041425705,
-0.013712313026189804,
0.008928355760872364,
-0.09697196632623672,
0.0074699209071695805,
0... |
b8fc2171-d2a2-43d8-a335-da138f7d8fdb | slug: /use-cases/AI/MCP/ollama
sidebar_label: 'Integrate Ollama'
title: 'Set Up ClickHouse MCP Server with Ollama'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up Ollama with a ClickHouse MCP server.'
keywords: ['AI', 'Ollama', 'MCP']
show_related_blogs: true
doc_type: 'guide... | {"source_file": "06_ollama.md"} | [
-0.03960104286670685,
-0.00628732331097126,
-0.006413768045604229,
-0.005700826644897461,
-0.014498988166451454,
-0.031930822879076004,
-0.040740471333265305,
0.05224049091339111,
0.0015636496245861053,
-0.05541088804602623,
0.04542186111211777,
0.07039924710988998,
0.03265175223350525,
-0... |
bede1b9c-54e3-473c-b00a-4272a6a12231 | We can configure MCP Servers with MCPHost in YAML or JSON files.
MCPHost will look for config files in your home directory the following order:
.mcphost.yml
or
.mcphost.json
(preferred)
.mcp.yml
or
.mcp.json
(backwards compatibility)
It uses a syntax that's similar to that used in the standard MCP con... | {"source_file": "06_ollama.md"} | [
0.000016411762771895155,
-0.08581221103668213,
0.05762892961502075,
-0.01994863525032997,
0.01985994726419449,
-0.029557030647993088,
-0.05486317351460457,
0.03973113372921944,
-0.03727150708436966,
-0.0008906807051971555,
0.026309126988053322,
0.03695604205131531,
0.026214906945824623,
0.... |
e3f07537-96b1-490d-9883-8ea458bf4e0b | Enter your prompt (Type /help for commands, Ctrl+C to quit, ESC to cancel generation)
```
We can use the
/servers
command to list the MCP Servers:
text
β β
β ## Configured MCP Servers ... | {"source_file": "06_ollama.md"} | [
0.0403413288295269,
-0.13385482132434845,
0.010572466999292374,
0.04475059360265732,
-0.016407135874032974,
-0.020345380529761314,
0.028788814321160316,
-0.023166293278336525,
-0.05100846290588379,
0.04773910716176033,
0.0315573513507843,
-0.06895090639591217,
0.08065929263830185,
-0.04990... |
31e154aa-0f1e-49b4-95b8-82ef2d53df03 | slug: /use-cases/AI/MCP/anythingllm
sidebar_label: 'Integrate AnythingLLM'
title: 'Set Up ClickHouse MCP Server with AnythingLLM and ClickHouse Cloud'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up AnythingLLM with a ClickHouse MCP server using Docker.'
keywords: ['AI', 'Any... | {"source_file": "04_anythingllm.md"} | [
-0.0265756044536829,
-0.004423619247972965,
0.007640710566192865,
-0.03353913128376007,
0.01531718298792839,
-0.026172500103712082,
-0.012533466331660748,
0.0048008933663368225,
-0.02334190160036087,
0.012681787833571434,
0.05186345428228378,
-0.017951494082808495,
0.06863623857498169,
-0.... |
52711efc-f916-4971-850e-6ad32777a1e7 | Once that's started, navigate to
http://localhost:3001
in your browser.
Select the model that you want to use and provide your API key.
Wait for MCP Servers to start up {#wait-for-mcp-servers-to-start-up}
Click on the tool icon in the bottom left-hand side of the UI:
Click on
Agent Skills
and look under the... | {"source_file": "04_anythingllm.md"} | [
-0.045685261487960815,
-0.13738781213760376,
0.03381915017962456,
-0.010417081415653229,
-0.037238847464323044,
-0.002768926089629531,
-0.033862508833408356,
-0.017397532239556313,
0.0010110174771398306,
0.034353017807006836,
0.003251461312174797,
-0.016953522339463234,
0.026234686374664307,... |
b140ce0f-f928-47a2-9971-6492d670267c | slug: /use-cases/AI/MCP/remote_mcp
sidebar_label: 'ClickHouse Cloud remote MCP'
title: 'Enabling the ClickHouse Cloud Remote MCP Server'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to enable and use the ClickHouse Cloud Remote MCP'
keywords: ['AI', 'ClickHouse Cloud', 'MCP']
show_r... | {"source_file": "01_remote_mcp.md"} | [
-0.023341549560427666,
0.003833952359855175,
0.04964876547455788,
-0.010431730188429356,
0.06623613834381104,
0.022550703957676888,
-0.025244751945137978,
0.018957972526550293,
-0.002453987952321768,
0.012705100700259209,
0.11237819492816925,
0.051101382821798325,
0.10106749832630157,
-0.0... |
169a6af7-58cc-4958-9dbf-70dd08c6557f | Verify in Claude Code that the remote MCP server is connected
Congratulations! You can now use the ClickHouse Cloud Remote MCP Server from Claude Code
Although this example used Claude code, you can use any LLM client that supports MCP by following similar steps. | {"source_file": "01_remote_mcp.md"} | [
-0.008704437874257565,
-0.10199307650327682,
0.0504806749522686,
-0.05364556238055229,
0.00402850890532136,
0.03928661346435547,
-0.07407523691654205,
-0.025810129940509796,
-0.017108624801039696,
0.05538249388337135,
0.03873799741268158,
0.029844505712389946,
0.030848585069179535,
-0.0265... |
856443f0-32ee-4b6a-8d9c-40b7e2a13d0e | slug: /use-cases/AI/MCP
sidebar_label: 'MCP'
title: 'MCP guides'
pagination_prev: null
pagination_next: null
description: 'This page provides an intro to Model Context Protocol (MCP) and has a table of contents for MCP guides.'
keywords: ['ClickHouse', 'MCP']
show_related_blogs: true
doc_type: 'guide'
import Image ... | {"source_file": "index.md"} | [
-0.09161420166492462,
-0.039149221032857895,
0.016190828755497932,
-0.02462671510875225,
0.057721421122550964,
-0.0015456106048077345,
0.0069342912174761295,
0.07101943343877792,
-0.013470305129885674,
-0.013886181637644768,
0.022149888798594475,
0.016663171350955963,
0.102259561419487,
0.... |
a32d34f7-1eb7-4ab0-a958-ef9943592b74 | | Page | Description |
|-----|-----|
|
Enabling the ClickHouse Cloud Remote MCP Server
| This guide explains how to enable and use the ClickHouse Cloud Remote MCP |
|
How to build a ClickHouse-backed AI Agent with Streamlit
| Learn how to build a web-based AI Agent with Streamlit and the ClickHouse MCP Server |
|
... | {"source_file": "index.md"} | [
-0.017608702182769775,
-0.12704214453697205,
-0.0022109001874923706,
-0.01738041825592518,
-0.03489714115858078,
-0.0233635101467371,
-0.012438198551535606,
-0.00051316455937922,
-0.08955812454223633,
0.05206948146224022,
-0.013937420211732388,
-0.06545254588127136,
0.06670859456062317,
0.... |
033752ba-4514-4f63-b404-565f01d92cfa | |
Set Up ClickHouse MCP Server with Jan.ai
| This guide explains how to set up Jan.ai with a ClickHouse MCP server. |
|
Set Up ClickHouse MCP Server with LibreChat and ClickHouse Cloud
| This guide explains how to set up LibreChat with a ClickHouse MCP server using Docker. |
|
Set Up ClickHouse MCP Server with Oll... | {"source_file": "index.md"} | [
-0.012720932252705097,
-0.04734156280755997,
0.032769426703453064,
0.014066202566027641,
-0.007740991655737162,
-0.07718969136476517,
-0.02736639231443405,
0.02451351471245289,
-0.041867710649967194,
-0.034647680819034576,
0.017961226403713226,
0.0011315681040287018,
-0.04014372080564499,
... |
9e741b1e-8bd9-4cb7-a721-63f0d48c4767 | slug: /use-cases/AI/MCP/librechat
sidebar_label: 'Integrate LibreChat'
title: 'Set Up ClickHouse MCP Server with LibreChat and ClickHouse Cloud'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up LibreChat with a ClickHouse MCP server using Docker.'
keywords: ['AI', 'Librechat',... | {"source_file": "03_librechat.md"} | [
0.023585060611367226,
0.0018434516387060285,
0.04319852218031883,
0.014027413912117481,
0.0182789359241724,
-0.024864250794053078,
-0.024152960628271103,
0.06976757198572159,
-0.01162037719041109,
-0.0407058447599411,
0.05279412120580673,
0.023078853264451027,
0.04717868193984032,
-0.04492... |
df6c21f9-aab4-42ec-ab6b-557f8734cc71 | ClickHouse SQL playground
.
Create a file called
docker-compose.override.yml
and add the following configuration to it:
yml title="docker-compose.override.yml"
services:
api:
volumes:
- ./librechat.yaml:/app/librechat.yaml
mcp-clickhouse:
image: mcp/clickhouse
container_name: mcp-clickhouse
... | {"source_file": "03_librechat.md"} | [
0.06023214012384415,
-0.04804929718375206,
0.03900248184800148,
0.023644283413887024,
-0.009400556795299053,
-0.08964459598064423,
0.011917218565940857,
-0.013161987997591496,
0.010051146149635315,
-0.05089984089136124,
-0.018082691356539726,
-0.02282484620809555,
-0.0008941771229729056,
-... |
6b4d84f5-96aa-455a-8e40-e325134bcc24 | From the chat interface, select
clickhouse-playground
as your MCP server:
You can now prompt the LLM to explore the ClickHouse example datasets. Give it a go:
text title="Prompt"
What datasets do you have access to? | {"source_file": "03_librechat.md"} | [
0.06644495576620102,
-0.09134312719106674,
0.03644536808133125,
0.014641143381595612,
0.02520061284303665,
0.014838479459285736,
0.03483179211616516,
-0.04319201037287712,
-0.020941734313964844,
-0.016167767345905304,
0.004047262016683817,
-0.04640436917543411,
0.0049925679340958595,
-0.01... |
42847838-8a82-42d2-82ea-4992f9811a1c | slug: /use-cases/AI/MCP/claude-desktop
sidebar_label: 'Integrate Claude Desktop'
title: 'Set Up ClickHouse MCP Server with Claude Desktop'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up Claude Desktop with a ClickHouse MCP server.'
keywords: ['AI', 'Librechat', 'MCP']
show_r... | {"source_file": "02_claude-desktop.md"} | [
-0.012464979663491249,
0.011760689318180084,
0.010553166270256042,
-0.02581544779241085,
0.023471059277653694,
0.006946061737835407,
-0.013705668039619923,
0.0651705339550972,
-0.04593779146671295,
-0.01367809809744358,
0.07429587095975876,
0.014494205825030804,
0.10831161588430405,
-0.009... |
80fd6fd2-cd53-4d86-8bb9-2f4d20149933 | Once you've updated the config, you'll need to restart Claude Desktop for the changes to take effect.
:::warning
Depending on how you installed
uv
, you might receive the following error when restarting Claude Desktop:
text
MCP mcp-clickhouse: spawn uv ENOENT
If that happens, you'll need to update the
command
... | {"source_file": "02_claude-desktop.md"} | [
0.006140200886875391,
-0.019296294078230858,
-0.002646797802299261,
-0.010209484957158566,
-0.021441569551825523,
0.01763935387134552,
0.03201679885387421,
0.029324974864721298,
-0.1127718836069107,
0.013332749716937542,
0.06307283788919449,
-0.04457852244377136,
0.04719700291752815,
0.030... |
87504926-8b12-42b1-9c61-e91ed4d2a3c9 | slug: /use-cases/AI/MCP/open-webui
sidebar_label: 'Integrate Open WebUI'
title: 'Set Up ClickHouse MCP Server with Open WebUI and ClickHouse Cloud'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up Open WebUI with a ClickHouse MCP server using Docker.'
keywords: ['AI', 'Open We... | {"source_file": "05_open-webui.md"} | [
-0.015205557458102703,
-0.003387295175343752,
0.019742485135793686,
0.01554943062365055,
0.023661181330680847,
-0.0431867316365242,
-0.038871053606271744,
0.04889298602938652,
-0.0527118556201458,
-0.015580207109451294,
0.0783877968788147,
-0.017306553199887276,
0.04758475720882416,
0.0215... |
ab6cf393-57ac-4d38-93a7-b7219c8a7709 | Once we've done this, we should see a
1
next to the tool icon on the chat bar:
If we click on the tool icon, we can then list the available tools:
Configure OpenAI {#configure-openai}
By default, Open WebUI works with Ollama models, but we can add OpenAI compatible endpoints as well.
These are configured vi... | {"source_file": "05_open-webui.md"} | [
-0.04294782131910324,
-0.08923473209142685,
0.004843581467866898,
0.0586484931409359,
-0.005538763478398323,
-0.08320771902799606,
-0.018749916926026344,
-0.011471482925117016,
0.004045314621180296,
-0.04416137933731079,
0.0382261723279953,
-0.04775434732437134,
-0.07177083194255829,
0.047... |
949e98a1-23ec-4f86-ba3e-ec660fe489b5 | slug: /use-cases/AI/MCP/janai
sidebar_label: 'Integrate Jan.ai'
title: 'Set Up ClickHouse MCP Server with Jan.ai'
pagination_prev: null
pagination_next: null
description: 'This guide explains how to set up Jan.ai with a ClickHouse MCP server.'
keywords: ['AI', 'Jan.ai', 'MCP']
show_related_blogs: true
doc_type: 'guide'... | {"source_file": "07_janai.md"} | [
-0.020870491862297058,
0.007679789327085018,
0.014431839808821678,
-0.011460625566542149,
0.035144366323947906,
-0.008328333497047424,
-0.036847010254859924,
0.028859954327344894,
-0.017940187826752663,
-0.02041703648865223,
0.08081882447004318,
0.020038636401295662,
0.08550816774368286,
-... |
132c7c3f-778c-4d00-8741-6f7c7f8f052e | Chat to ClickHouse MCP Server with Jan.ai {#chat-to-clickhouse-mcp-server}
It's time to have a conversation about some data stored in ClickHouse!
Let's ask a question:
Jan.ai will ask confirmation before calling a tool:
It will then show us the list of tool calls that were made:
If we click on the tool ca... | {"source_file": "07_janai.md"} | [
-0.06566592305898666,
-0.03566286712884903,
-0.004654652439057827,
0.02633533626794815,
0.021930916234850883,
-0.06545958667993546,
0.08305849879980087,
-0.018793776631355286,
0.0459173284471035,
-0.027115000411868095,
-0.025193020701408386,
-0.0012372798519209027,
-0.047789961099624634,
-... |
ec86c736-52f8-49fb-88bb-f20ab78be745 | slug: /use-cases/AI_ML/AIChat
sidebar_label: 'AI chat'
title: 'Using AI Chat in ClickHouse Cloud'
pagination_prev: null
pagination_next: null
description: 'Guide to enabling and using the AI Chat feature in ClickHouse Cloud Console'
keywords: ['AI', 'ClickHouse Cloud', 'Chat', 'SQL Console', 'Agent', 'Docs AI']
show_re... | {"source_file": "index.md"} | [
0.017823386937379837,
0.012136688455939293,
0.02311519905924797,
-0.011562761850655079,
0.06744901090860367,
-0.012856590561568737,
0.03484884276986122,
0.0654691606760025,
0.013436411507427692,
0.03893185034394264,
0.10919374227523804,
0.022097496315836906,
0.07798738777637482,
0.03403907... |
10422595-539d-4114-94dc-cb1d5acfe785 | Viewing chat history {#history}
The lower section lists your recent chats.
Select a previous chat to load its messages.
Delete a conversation using the trash icon.
Working with generated SQL {#sql-actions}
When the assistant returns SQL:
Review for correctness.
Click βOpen in editorβ to load the q... | {"source_file": "index.md"} | [
-0.019150234758853912,
-0.03029555454850197,
0.033696942031383514,
0.051780782639980316,
-0.03204504773020744,
0.01660190336406231,
0.06643209606409073,
-0.02209649421274662,
0.0563407801091671,
0.03483646735548973,
-0.02610064297914505,
-0.010664785280823708,
-0.06677903234958649,
-0.0020... |
384edab6-7333-4b15-8bd5-f4df6931266d | slug: /use-cases/AI/MCP/ai-agent-libraries/DSPy
sidebar_label: 'Integrate DSPy'
title: 'How to build an AI Agent with DSPy and the ClickHouse MCP Server'
pagination_prev: null
pagination_next: null
description: 'Learn how to build an AI agent with DSPy and the ClickHouse MCP Server'
keywords: ['ClickHouse', 'MCP', 'DSP... | {"source_file": "dspy.md"} | [
-0.0385126918554306,
-0.0912727564573288,
-0.03117743879556656,
0.0008116689859889448,
-0.049648601561784744,
-0.019725214689970016,
0.011465511284768581,
0.026509879156947136,
-0.10006769746541977,
-0.006757085211575031,
0.017917310819029808,
0.023301566019654274,
0.09793180227279663,
-0.... |
dec23190-0d2e-448f-b4c6-b249579e25e8 | user_request: str = dspy.InputField()
process_result: str = dspy.OutputField(
desc=(
"Answer to the query"
)
)
from utils import print_dspy_result
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
... | {"source_file": "dspy.md"} | [
0.02690088376402855,
-0.03364497050642967,
-0.09529408067464828,
0.04925234988331795,
0.021730661392211914,
0.003650224767625332,
0.023644795641303062,
0.06882352381944656,
0.012098587118089199,
-0.0304777380079031,
-0.002974348608404398,
-0.026987727731466293,
-0.02334073930978775,
-0.083... |
8e9700dc-9a99-46c5-8355-7e27242c1798 | π STEP 4
π§ THINKING: Perfect! I have found the answer to the user's question. Based on the Amazon reviews data, I can clearly see that "Books" is the most popular Amazon product category with 19,530,930 reviews, followed by "Digital_Ebook_Purchase" with 17,622,107 reviews. The data shows a clear ranking of product ... | {"source_file": "dspy.md"} | [
0.032851122319698334,
-0.06010231748223305,
-0.07954613119363785,
0.02519938535988331,
-0.027656443417072296,
0.07771214842796326,
0.010477225296199322,
0.03488505259156227,
0.07625182718038559,
0.058863960206508636,
0.03442646190524101,
0.019633324816823006,
0.09442716836929321,
-0.059225... |
30800d4b-9eea-480d-9466-8ef225607c4a | slug: /use-cases/AI/MCP/ai-agent-libraries/claude-agent-sdk
sidebar_label: 'Integrate Claude Agent SDK'
title: 'How to build an AI Agent with Claude Agent SDK and the ClickHouse MCP Server'
pagination_prev: null
pagination_next: null
description: 'Learn how build an AI Agent with Claude Agent SDK and the ClickHouse MCP... | {"source_file": "claude-agent-sdk.md"} | [
-0.028709841892123222,
-0.06424254924058914,
-0.07267594337463379,
-0.023375332355499268,
-0.04182358831167221,
0.009774389676749706,
-0.00047871278366073966,
0.015279725193977356,
-0.07457119226455688,
0.025423437356948853,
0.01892412081360817,
-0.024944592267274857,
0.09681835770606995,
... |
ddc0886b-8357-4411-b540-6ab049270fb1 | async for message in query(prompt="Tell me something interesting about UK property sales", options=options):
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(f"π€ {block.text}")
if isinstance(block, ToolUse... | {"source_file": "claude-agent-sdk.md"} | [
0.00086041702888906,
-0.015639329329133034,
-0.020177505910396576,
0.05669771879911423,
-0.016713222488760948,
-0.076597198843956,
0.1283557415008545,
-0.046294208616018295,
-0.04306114837527275,
-0.05281739681959152,
0.05672657489776611,
-0.09428296983242035,
0.049464140087366104,
-0.0668... |
13473187-d6f7-4528-bf57-265293212457 | slug: /use-cases/AI/MCP/ai-agent-libraries/mcp-agent
sidebar_label: 'Integrate mcp-agent'
title: 'How to build an AI Agent with mcp-agent and the ClickHouse MCP Server'
pagination_prev: null
pagination_next: null
description: 'Learn how build an AI Agent with mcp-agent and the ClickHouse MCP Server'
keywords: ['ClickHo... | {"source_file": "mcp-agent.md"} | [
-0.01490349043160677,
-0.09618127346038818,
-0.05158887803554535,
0.0025609959848225117,
-0.025791138410568237,
0.011759653687477112,
-0.0021475357934832573,
-0.0077585927210748196,
-0.07137900590896606,
0.017471781000494957,
0.03857909142971039,
-0.025906851515173912,
0.09410910308361053,
... |
115a2951-e316-4110-ba87-010f8537f39f | async with data_agent:
llm = await data_agent.attach_llm(OpenAIAugmentedLLM)
result = await llm.generate_str(
message="Tell me about UK property prices in 2025. Use ClickHouse to work it out."
)
logger.info(result)
``` | {"source_file": "mcp-agent.md"} | [
0.05501699075102806,
0.016152631491422653,
-0.08606882393360138,
0.1138482317328453,
-0.010932136327028275,
-0.07586335390806198,
-0.012343162670731544,
0.00034013224649243057,
-0.024461057037115097,
-0.013203158043324947,
0.028270775452256203,
-0.12049354612827301,
0.0013225857401266694,
... |
9a70777c-e19c-443e-906a-5b66d0f03a1e | ```response title="Response"
[10/10/25 11:26:20] INFO Starting MCP server 'mcp-clickhouse' with transport 'stdio' server.py:1502
2025-10-10 11:26:20,183 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
2025-10-10 11:26:20,184 - mcp.server.lowleve... | {"source_file": "mcp-agent.md"} | [
-0.01530083641409874,
-0.05469586327672005,
-0.022710133343935013,
0.01599741168320179,
0.07622403651475906,
-0.12268955260515213,
0.06517673283815384,
0.06879521161317825,
-0.04949362576007843,
0.049648791551589966,
-0.011635066010057926,
-0.041462309658527374,
0.0076317209750413895,
0.01... |
8cc4297d-8f36-4132-bd09-75fab33c6fd9 | "agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:2... | {"source_file": "mcp-agent.md"} | [
0.010896580293774605,
-0.0316503532230854,
-0.06405030936002731,
0.03293168544769287,
-0.016925973817706108,
-0.0655282586812973,
0.05739300325512886,
-0.005515580996870995,
-0.032914068549871445,
0.047285664826631546,
0.029024777933955193,
-0.07489664852619171,
0.017741555348038673,
-0.03... |
19f16bae-6579-4953-8f50-5837887ed00d | 2025-10-10 11:26:50,594 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:50,741 - mcp-clickhouse - INFO - Query returned 10 rows
2025-10-10 11:26:50,744 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:50,746 - mc... | {"source_file": "mcp-agent.md"} | [
0.02098892256617546,
-0.05605744197964668,
0.04468369483947754,
0.05720396339893341,
-0.14170600473880768,
-0.03264566510915756,
0.014588876627385616,
-0.03173636645078659,
-0.013669929467141628,
-0.004869587253779173,
0.025825249031186104,
-0.06611093133687973,
0.03788750246167183,
-0.026... |
d22adf49-9d98-40e1-a898-1f6925108716 | What I computed (how)
I ran aggregations on the uk.price-paid tables in ClickHouse:
- overall 2025 summary (count, mean, median, min, max) from uk.uk_price_paid_simple_partitioned
- monthly breakdown for 2025 (transactions, mean, median)
- top towns in 2025 by average price (towns with >= 50 transactions)
- year compar... | {"source_file": "mcp-agent.md"} | [
0.05535942688584328,
-0.05599791556596756,
0.033650316298007965,
0.030033757910132408,
-0.06221238151192665,
-0.04388762637972832,
-0.03973851352930069,
0.015244279988110065,
-0.009228356182575226,
0.013324780389666557,
0.026651546359062195,
-0.09192506223917007,
0.029725991189479828,
-0.0... |
07fe4cbd-1c01-4f8f-ac53-6f1f9c88dde6 | Suggested next steps (I can run these)
- Clean out obvious outliers (e.g., prices < Β£10k or > Β£10M) and recompute averages/medians.
- Produce regional / county / postcode-area summaries and maps.
- Compute month-on-month or rolling 3-month median to show trend through 2025.
- Produce year-on-year (YoY) growth rates by ... | {"source_file": "mcp-agent.md"} | [
0.000485801458125934,
-0.0416121743619442,
0.06029636040329933,
0.08472461253404617,
-0.028472905978560448,
-0.05486725643277168,
-0.11984898149967194,
-0.017046498134732246,
-0.09292662143707275,
0.0074259014800190926,
0.0334760956466198,
-0.05042845383286476,
0.03547576442360878,
-0.0414... |
45cf3ab8-fd5d-4da3-8247-ee322cb8de0d | slug: /use-cases/AI/MCP/ai-agent-libraries/upsonic
sidebar_label: 'Integrate Upsonic'
title: 'How to build an AI Agent with Upsonic and the ClickHouse MCP Server'
pagination_prev: null
pagination_next: null
description: 'Learn how build an AI Agent with Upsonic and the ClickHouse MCP Server'
keywords: ['ClickHouse', 'M... | {"source_file": "upsonic.md"} | [
-0.08317920565605164,
-0.10290072858333588,
-0.07767906785011292,
-0.01901070587337017,
-0.07323648780584335,
0.012652195058763027,
0.01928040012717247,
0.007235283497720957,
-0.08761242032051086,
0.05260670930147171,
0.05229644849896431,
0.002348981099203229,
0.10189332067966461,
-0.00183... |
bfa4b3cf-d4a2-4a17-a528-72dbfa9308f3 | Execute the workflow
workflow_result = database_agent.do(task)
print("\nMulti-MCP Workflow Result:")
print(workflow_result)
```
```response title="Response"
2025-10-10 11:26:12,758 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
Found 3 tools from DatabaseMCP
- list_databases: Li... | {"source_file": "upsonic.md"} | [
0.013472522608935833,
-0.03476836159825325,
-0.02491234801709652,
0.07257863134145737,
-0.07157420367002487,
-0.07147735357284546,
0.04717075079679489,
0.0030976939015090466,
-0.05441105365753174,
-0.00003695399936987087,
-0.018678812310099602,
-0.04561878740787506,
0.06359264254570007,
-0... |
4e9df108-5258-4bf1-bac6-9a4f4c1bd6de | [10/10/25 11:26:20] INFO Starting MCP server 'mcp-clickhouse' with transport 'stdio' server.py:1502
2025-10-10 11:26:20,183 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
2025-10-10 11:26:20,184 - mcp.server.lowlevel.server - INFO - Processing ... | {"source_file": "upsonic.md"} | [
-0.030485017225146294,
-0.0510660819709301,
-0.029176397249102592,
0.018770208582282066,
0.07206548005342484,
-0.13284802436828613,
0.06196705251932144,
0.06113779544830322,
-0.04684675484895706,
0.05322412773966789,
-0.011389410123229027,
-0.043042924255132675,
0.0008328110561706126,
0.01... |
e11b31f2-d9d1-4605-86f7-bcece94a2bc4 | }
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.... | {"source_file": "upsonic.md"} | [
-0.00009548664093017578,
-0.025581365451216698,
-0.05854864791035652,
0.02387116104364395,
-0.019074199721217155,
-0.06394761800765991,
0.05949155613780022,
0.01164997462183237,
-0.024044862017035484,
0.05638710781931877,
0.029670117422938347,
-0.07766716927289963,
0.010166267864406109,
-0... |
e0aa0d5f-b5d7-49f5-b764-8015177e4de6 | 2025-10-10 11:26:50,594 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:50,741 - mcp-clickhouse - INFO - Query returned 10 rows
2025-10-10 11:26:50,744 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:50,746 - mc... | {"source_file": "upsonic.md"} | [
0.02098892256617546,
-0.05605744197964668,
0.04468369483947754,
0.05720396339893341,
-0.14170600473880768,
-0.03264566510915756,
0.014588876627385616,
-0.03173636645078659,
-0.013669929467141628,
-0.004869587253779173,
0.025825249031186104,
-0.06611093133687973,
0.03788750246167183,
-0.026... |
2cd28c52-e374-4180-9131-5c02aa69a611 | What I computed (how)
I ran aggregations on the uk.price-paid tables in ClickHouse:
- overall 2025 summary (count, mean, median, min, max) from uk.uk_price_paid_simple_partitioned
- monthly breakdown for 2025 (transactions, mean, median)
- top towns in 2025 by average price (towns with >= 50 transactions)
- year compar... | {"source_file": "upsonic.md"} | [
0.05535942688584328,
-0.05599791556596756,
0.033650316298007965,
0.030033757910132408,
-0.06221238151192665,
-0.04388762637972832,
-0.03973851352930069,
0.015244279988110065,
-0.009228356182575226,
0.013324780389666557,
0.026651546359062195,
-0.09192506223917007,
0.029725991189479828,
-0.0... |
6c947357-4c3e-40ca-a7a5-ce35b0cf5908 | Suggested next steps (I can run these)
- Clean out obvious outliers (e.g., prices < Β£10k or > Β£10M) and recompute averages/medians.
- Produce regional / county / postcode-area summaries and maps.
- Compute month-on-month or rolling 3-month median to show trend through 2025.
- Produce year-on-year (YoY) growth rates by ... | {"source_file": "upsonic.md"} | [
0.000485801458125934,
-0.0416121743619442,
0.06029636040329933,
0.08472461253404617,
-0.028472905978560448,
-0.05486725643277168,
-0.11984898149967194,
-0.017046498134732246,
-0.09292662143707275,
0.0074259014800190926,
0.0334760956466198,
-0.05042845383286476,
0.03547576442360878,
-0.0414... |
512ec1c1-a33e-459e-92ef-1e553d21a279 | slug: /use-cases/AI/MCP/ai-agent-libraries/pydantic-ai
sidebar_label: 'Integrate PydanticAI'
title: 'How to build a PydanticAI agent using ClickHouse MCP Server.'
pagination_prev: null
pagination_next: null
description: 'Learn how to build a PydanticAI agent that can interact with ClickHouse MCP Server.'
keywords: ['Cl... | {"source_file": "pydantic-ai.md"} | [
-0.020693114027380943,
-0.055462874472141266,
-0.05559935420751572,
-0.029749765992164612,
-0.06420361250638962,
-0.016569379717111588,
-0.017194978892803192,
0.008531194180250168,
-0.10183016210794449,
0.020890550687909126,
0.05861181765794754,
-0.026191875338554382,
0.09824976325035095,
... |
f9f0ba54-27cd-4929-8cbd-5c4f7c7d7c2b | python
async with agent.run_mcp_servers():
result = await agent.run("Who's done the most PRs for ClickHouse?")
print(result.output)
You'll get back a similar response as below:
```response title="Response"
Based on the data from the ClickHouse GitHub repository, here are the top contributors by number of pu... | {"source_file": "pydantic-ai.md"} | [
-0.022730672731995583,
-0.09515995532274246,
-0.12077535688877106,
0.03527600318193436,
-0.019399316981434822,
-0.0467924140393734,
0.022520286962389946,
0.02361607737839222,
0.02735542319715023,
0.01832551695406437,
-0.02871420606970787,
-0.010775011964142323,
-0.029917292296886444,
-0.06... |
1b28fb9b-64f8-4bf9-9942-05a7c2e2a937 | slug: /use-cases/AI/MCP/ai-agent-libraries/slackbot
sidebar_label: 'Integrate SlackBot'
title: 'How to build a SlackBot agent using ClickHouse MCP Server.'
pagination_prev: null
pagination_next: null
description: 'Learn how to build a SlackBot agent that can interact with ClickHouse MCP Server.'
keywords: ['ClickHouse'... | {"source_file": "slackbot.md"} | [
-0.032680343836545944,
-0.08894985914230347,
-0.03214053809642792,
-0.006714335642755032,
-0.0523320697247982,
-0.004784977994859219,
-0.025364121422171593,
0.04707162827253342,
-0.105304054915905,
0.03464940935373306,
0.05354880914092064,
-0.03675662726163864,
0.08367356657981873,
0.02324... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.