ektravel commented on code in PR #13465:
URL: https://github.com/apache/druid/pull/13465#discussion_r1046465411
##########
examples/quickstart/jupyter-notebooks/sql-tutorial.ipynb:
##########
@@ -0,0 +1,715 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ad4e60b6",
+ "metadata": {
+ "deletable": true,
+ "editable": true,
+ "tags": []
+ },
+ "source": [
+ "# Tutorial: Learn the basics of Druid SQL\n",
+ "\n",
+ "<!--\n",
+ " ~ Licensed to the Apache Software Foundation (ASF) under one\n",
+ " ~ or more contributor license agreements. See the NOTICE file\n",
+ " ~ distributed with this work for additional information\n",
+ " ~ regarding copyright ownership. The ASF licenses this file\n",
+ " ~ to you under the Apache License, Version 2.0 (the\n",
+ " ~ \"License\"); you may not use this file except in compliance\n",
+ " ~ with the License. You may obtain a copy of the License at\n",
+ " ~\n",
+ " ~ http://www.apache.org/licenses/LICENSE-2.0\n";,
+ " ~\n",
+ " ~ Unless required by applicable law or agreed to in writing,\n",
+ " ~ software distributed under the License is distributed on an\n",
+ " ~ \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ " ~ KIND, either express or implied. See the License for the\n",
+ " ~ specific language governing permissions and limitations\n",
+ " ~ under the License.\n",
+ " -->\n",
+ " \n",
+ "Druid SQL is a Structured Query Language (SQL) dialect that enables you
to query datasources in Apache Druid using SQL statements.\n",
+ "SQL and Druid SQL use similar syntax, with some notable differences.\n",
+ "Not all SQL functions are supported in Druid SQL. Instead, Druid includes
Druid-specific SQL functions for optimized query performance.\n",
+ "\n",
+ "This interactive tutorial introduces you to the unique aspects of Druid
SQL."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d6bbbcb",
+ "metadata": {
+ "deletable": true,
+ "tags": []
+ },
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "Make sure that you meet the requirements outlined in the README.md file
of the [apache/druid
repo](https://github.com/apache/druid/tree/master/examples/quickstart/jupyter-notebooks/).\n",
+ "Specifically, you need the following:\n",
+ "- Knowledge of SQL\n",
+ "- Python3\n",
+ "- The `requests` package for Python\n",
+ "- JupyterLab (recommended) or Jupyter Notebook running on a non-default
port. Druid and Jupyter both default to port `8888`, so you need to start
Jupyter on a different port. \n",
+ "- An available Druid instance. This tutorial uses the `micro-quickstart`
configuration described in the [Druid
quickstart](https://druid.apache.org/docs/latest/tutorials/index.html), so no
authentication or authorization is required unless explicitly mentioned. If you
haven’t already, download Druid and start Druid services as described in the
quickstart."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8f8e64f0-c29a-473c-8783-a2ff8648acd7",
+ "metadata": {},
+ "source": [
+ "## Prepare your environment\n",
+ "\n",
+ "This section contains the steps required to prepare your environment to
follow along with this tutorial.\n",
+ "\n",
+ "Start by running the following cell. It imports the required Python
packages and defines a variable for the Druid host."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b7f08a52",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "import json\n",
+ "\n",
+ "# druid_host is the hostname and port for your Druid deployment. \n",
+ "druid_host = \"http://localhost:8888\"\n";,
+ "dataSourceName = \"wikipedia-sql-tutorial\"\n",
+ "print(druid_host)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e893ef7d-7136-442f-8bd9-31b5a5276518",
+ "metadata": {},
+ "source": [
+ "In the rest of the tutorial, the `endpoint`, `http_method`, and `payload`
variables are updated to accomplish different tasks.\n",
+ "\n",
+ "Run the following cell to ingest data from an external source into a
table named `wikipedia-sql-tutorial` using the [multi-stage query (MSQ) task
engine](https://druid.apache.org/docs/latest/multi-stage-query/index.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "045f782c-74d8-4447-9487-529071812b51",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql/task\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\": \"INSERT INTO \\\"wikipedia-sql-tutorial\\\" SELECT
TIME_PARSE(\\\"timestamp\\\") AS __time, * FROM TABLE( EXTERN( '{\\\"type\\\":
\\\"http\\\", \\\"uris\\\":
[\\\"https://druid.apache.org/data/wikipedia.json.gz\\\"]}','{\\\"type\\\":
\\\"json\\\"}', '[{\\\"name\\\": \\\"added\\\", \\\"type\\\": \\\"long\\\"},
{\\\"name\\\": \\\"channel\\\", \\\"type\\\": \\\"string\\\"}, \\
{\\\"name\\\": \\\"cityName\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"comment\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"commentLength\\\", \\\"type\\\": \\\"long\\\"}, {\\\"name\\\":
\\\"countryIsoCode\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"countryName\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"deleted\\\", \\\"type\\\": \\\"long\\\"},{\\\"name\\\": \\\"delta\\\",
\\\"type\\\": \\\"long\\\"}, {\\\"name\\\": \\\"deltaBucket\\\", \\\"type\\\":
\\\"string\\\"}, {\\\"name\\\": \\\"diffUrl\\\", \\\"type\\\": \\\"string\\\"},
{\\\"name\\\": \\\
"flags\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\": \\\"isAnonymous\\\",
\\\"type\\\": \\\"string\\\"}, {\\\"name\\\": \\\"isMinor\\\", \\\"type\\\":
\\\"string\\\"}, {\\\"name\\\": \\\"isNew\\\", \\\"type\\\": \\\"string\\\"},
{\\\"name\\\": \\\"isRobot\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"isUnpatrolled\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"metroCode\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\":
\\\"namespace\\\", \\\"type\\\": \\\"string\\\"}, {\\\"name\\\": \\\"page\\\",
\\\"type\\\": \\\"string\\\"}, {\\\"name\\\": \\\"regionIsoCode\\\",
\\\"type\\\": \\\"string\\\"}, {\\\"name\\\": \\\"regionName\\\", \\\"type\\\":
\\\"string\\\"}, {\\\"name\\\": \\\"timestamp\\\", \\\"type\\\":
\\\"string\\\"}, {\\\"name\\\": \\\"user\\\", \\\"type\\\": \\\"string\\\"}]'))
PARTITIONED BY DAY\",\n",
+ " \"context\": {\n",
+ " \"maxNumTasks\": 3\n",
+ " }\n",
+ "})\n",
+ "\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "ingestiion_taskId_response = response\n",
+ "ingestion_taskId = json.loads(response.text)['taskId']\n",
+ "print(ingestion_taskId + f\"\\nInserting data into the table named
{dataSourceName}.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ceb86ce0-85f6-4c63-8fd6-883033ee96e9",
+ "metadata": {},
+ "source": [
+ "To check the status of your ingestion task, run the following cell. \n",
+ "When the value of the `status` property changes to `SUCCESS`, you can
query your data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df12d12c-a067-4759-bae0-0410c24b6205",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = f\"/druid/indexer/v1/task/{ingestion_taskId}/status\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"GET\"\n",
+ "\n",
+ "payload = {}\n",
+ "headers = {}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "240b0ad5-48f2-4737-b12b-5fd5f98da300",
+ "metadata": {},
+ "source": [
+ "## Datasources\n",
+ "\n",
+ "Druid supports a variety of datasources, with the table datasource being
the most common. In Druid documentation, the word \"datasource\" often
implicitly refers to the table datasource.\n",
+ "The
[Datasources](https://druid.apache.org/docs/latest/querying/datasource.html)
topic provides a comprehensive overview of datasources supported by Druid
SQL.\n",
+ "\n",
+ "In Druid SQL, table datasources reside in the `druid` schema. This is the
default schema, so table datasources can be referenced as either
`druid.dataSourceName` or `dataSourceName`.\n",
+ "\n",
+ "For example, run the next cell to return the rows of the column named
`channel` from the `wikipedia-sql-tutorial` table. Because this tutorial is
running in Jupyter, make sure to limit the size of your query results for
display purposes using the SQL LIMIT clause."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91dd255a-4d55-493e-a067-4cef5c659657",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT \\\"channel\\\" FROM druid.wikipedia-sql-tutorial
LIMIT 7\"})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbeb5a63",
+ "metadata": {
+ "deletable": true,
+ "tags": []
+ },
+ "source": [
+ "## Data types\n",
+ "\n",
+ "Druid maps SQL data types onto native types at query runtime.\n",
+ "The following native types are supported for Druid columns:\n",
+ "\n",
+ "* STRING: UTF-8 encoded strings and string arrays\n",
+ "* LONG: 64-bit signed int\n",
+ "* FLOAT: 32-bit float\n",
+ "* DOUBLE: 64-bit float\n",
+ "* COMPLEX: represents non-standard data types, such as nested JSON,
hyperUnique and approxHistogram aggregators, and DataSketches aggregators\n",
+ "\n",
+ "Druid exposes table and column metadata through
[INFORMATION_SCHEMA](https://druid.apache.org/docs/latest/querying/sql-metadata-tables.html#information-schema)
tables. Run the following query to retrieve metadata for the
`wikipedia-sql-tutorial` datasource. In the response body, each JSON object
correlates to a column in the table.\n",
+ "Check the objects' `DATA_TYPE` property for SQL data types. You should
see TIMESTAMP, BIGINT, and VARCHAR SQL data types. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9227d6c-1d8c-4169-b13b-a08625c4011f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT * FROM INFORMATION_SCHEMA.COLUMNS WHERE
\\\"TABLE_SCHEMA\\\" = 'druid' AND \\\"TABLE_NAME\\\" =
'wikipedia-sql-tutorial' LIMIT 7\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c59ca797-dd91-442b-8d02-67b711b3fcc6",
+ "metadata": {},
+ "source": [
+ "Druid natively interprets VARCHAR as STRING and BIGINT and TIMESTAMP SQL
data types as LONG. For reference on how SQL data types map onto Druid native
types, see [Standard
types](https://druid.apache.org/docs/latest/querying/sql-data-types.html#standard-types).\n",
+ "\n",
+ "### Timestamp values\n",
+ "\n",
+ "Druid stores timestamp values as the number of milliseconds since the
Unix epoch.\n",
+ "Primary timestamps are stored in a column named `__time`.\n",
+ "If a dataset doesn't have a timestamp, Druid uses the default value of
`1970-01-01 00:00:00`.\n",
+ "\n",
+ "Druid time functions perform best when used with the `__time` column.\n",
+ "By default, time functions use the UTC time zone.\n",
+ "For more information about timestamp handling, see [Date and time
functions](https://druid.apache.org/docs/latest/querying/sql-scalar.html#date-and-time-functions).\n",
+ "\n",
+ "Run the following cell to see Druid SQL `TIME_IN_INTERVAL` scalar
function at work. This query checks whether a timestamp is contained within a
specified interval. The results are grouped by date."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7e3d62a-1325-4992-8bcd-c0f1925704bc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT TIME_IN_INTERVAL(__time,
'2016-06-27T12:00:00/2016-06-27T13:00:00'), COUNT(*) FROM
\\\"wikipedia-sql-tutorial\\\" \\\n",
+ " GROUP BY TIME_IN_INTERVAL(__time,
'2016-06-27T12:00:00/2016-06-27T13:00:00')\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f7cfdfae-ccba-49ba-a70f-63d0bd3527b2",
+ "metadata": {},
+ "source": [
+ "### NULL values\n",
+ "\n",
+ "Druid supports SQL compatible NULL handling, allowing string columns to
distinguish empty strings from NULL and numeric columns to contain NULL rows.
To store and query data in SQL compatible mode, explicitly set the
`useDefaultValueForNull` property to `false` in
`_common/common.runtime.properties`. See [Configuration
reference](https://druid.apache.org/docs/latest/configuration/index.html) for
common configuration properties.\n",
+ "\n",
+ "When `useDefaultValueForNull` is set to `true` (default behavior), Druid
stores NULL values as `0` for numeric columns and as `''` for string columns."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29c24856",
+ "metadata": {
+ "deletable": true,
+ "tags": []
+ },
+ "source": [
+ "## SELECT statement syntax\n",
+ "\n",
+ "Druid SQL supports SELECT statements with the following structure:\n",
+ "\n",
+ "``` mysql\n",
+ "[ EXPLAIN PLAN FOR ]\n",
+ "[ WITH tableName [ ( column1, column2, ... ) ] AS ( query ) ]\n",
+ "SELECT [ ALL | DISTINCT ] { * | exprs }\n",
+ "FROM { <table> | (<subquery>) | <o1> [ INNER | LEFT ] JOIN <o2> ON
condition }\n",
+ "[ WHERE expr ]\n",
+ "[ GROUP BY [ exprs | GROUPING SETS ( (exprs), ... ) | ROLLUP (exprs) |
CUBE (exprs) ] ]\n",
+ "[ HAVING expr ]\n",
+ "[ ORDER BY expr [ ASC | DESC ], expr [ ASC | DESC ], ... ]\n",
+ "[ LIMIT limit ]\n",
+ "[ OFFSET offset ]\n",
+ "[ UNION ALL <another query> ]\n",
+ "```\n",
+ "\n",
+ "As a general rule, use the LIMIT clause with `SELECT *` to limit the
number of rows returned. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cf212e5-fb3f-4206-acdd-46ef1da327ab",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## WHERE clause\n",
+ "\n",
+ "Druid SQL uses the [SQL WHERE
clause](https://druid.apache.org/docs/latest/querying/sql.html#where) of a
SELECT statement to fetch data based on a particular condition.\n",
+ "\n",
+ "In most cases, specifying a time range when using the WHERE clause
improves query performance.\n",
+ "This is because Druid partitions data by time and having a time range
allows Druid to skip over unrelated data.\n",
+ "\n",
+ "Druid supports range filtering on columns that contain long millisecond
values, with the boundaries specified as ISO 8601 time intervals. This is
suitable for the `__time` column, long metric columns, and dimensions with
values that can be parsed as long milliseconds.\n",
+ " \n",
+ "For example, the following cell uses a comparison operator on the
`__time` field to filter results from a certain time range. The cell limits the
results to seven rows for display purposes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ffca2962-da31-4b9c-adbc-882e35386916",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT * FROM \\\"wikipedia-sql-tutorial\\\" WHERE __time
>= TIMESTAMP '2015-09-12 23:33:55' LIMIT 7\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5160db26-7e8d-40f7-8588-b7eabfc08355",
+ "metadata": {},
+ "source": [
+ "### Comparison operators\n",
+ "\n",
+ "Druid SQL supports the following comparison operators. You can use these
operators in conjunction with the WHERE clause to compare expressions.\n",
+ "\n",
+ "- equal to (=)\n",
+ "- greater than(>)\n",
+ "- less than (<)\n",
+ "- greater than or equal (>=)\n",
+ "- less than or equal (<=)\n",
+ "- not equal to( <>)\n",
+ "\n",
+ "For example, the next cell returns the first seven records that match the
following criteria:\n",
+ "- `cityName` is not an empty string\n",
+ "- `countryIsoCode` value equals to `US`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9afa74b9-ef9f-4b36-a7bb-88e4498a48ef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT * FROM \\\"wikipedia-sql-tutorial\\\" WHERE
\\\"cityName\\\" <> '' AND \\\"countryIsoCode\\\" = 'US' LIMIT 7\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd24470d-25c2-4031-a711-8477d69c9e94",
+ "metadata": {
+ "deletable": true,
+ "editable": true,
+ "tags": []
+ },
+ "source": [
+ "### Logical operators\n",
+ "\n",
+ "Druid's handling of logical operators is comparable to SQL with a few
exceptions. For example, if an IN list contains NULL, the IN operator matches
NULL values. This behavior is different from the SQL IN operator, which does
not match NULL values. For a complete list of logical SQL operators supported
by Druid SQL, see [Logical
operators](https://druid.apache.org/docs/latest/querying/sql-operators.html#logical-operators)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2baf21b9-74d1-4df6-862f-afbaeef1812b",
+ "metadata": {},
+ "source": [
+ "## GROUP BY clause\n",
+ "\n",
+ "Druid SQL uses the [SQL GROUP
BY](https://druid.apache.org/docs/latest/querying/sql.html#group-by) clause to
separate items into groups, where each group is composed of rows with identical
values. \n",
+ "The GROUP BY clause is often used with [aggregation
functions](https://druid.apache.org/docs/latest/querying/sql-aggregations.html),
such as COUNT or SUM, to produce summary values for each group.\n",
+ "\n",
+ "For example, the following cell counts all of the entries separated by
the field `channel`. The output is limited to seven rows and has two fields:
`channel` and `counts`. For each unique value of `channel`, Druid aggregates
all rows having that value, counts the number of entries in the group, and
assigns the results to a field called `counts`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "228ae0e4-355e-4b4d-8253-fb2e46715559",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT channel, COUNT(*) AS counts FROM
\\\"wikipedia-sql-tutorial\\\" GROUP BY channel LIMIT 7\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eab67db4-a0f3-4177-b5be-1fef355bf33f",
+ "metadata": {},
+ "source": [
+ "You can further define the groups by specifying multiple dimensions.\n",
+ "Druid SQL supports using numbers in GROUP BY and ORDER BY clauses to
refer to column positions in the SELECT clause.\n",
+ "Similar to SQL, Druid SQL uses one-based indexing to reference elements
in SQL statements.\n",
+ "\n",
+ "For example, the next cell aggregates entries grouped by fields
`cityName` and `countryName`.\n",
+ "The output has three fields: `cityName`, `countryName`, and `counts`. For
each unique combination of `cityName` and `countryName`, Druid aggregates all
rows and averages the entries in the group.\n",
+ "The output is limited to seven rows for display purposes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca4b3bae-2b02-4c90-98a5-cb7806b6e649",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "endpoint = \"/druid/v2/sql\"\n",
+ "print(druid_host+endpoint)\n",
+ "http_method = \"POST\"\n",
+ "\n",
+ "payload = json.dumps({\n",
+ " \"query\":\"SELECT cityName, countryName, COUNT(*) AS counts FROM
\\\"wikipedia-sql-tutorial\\\" GROUP BY 1, 2 LIMIT 7\"\n",
+ "})\n",
+ "headers = {'Content-Type': 'application/json'}\n",
+ "\n",
+ "response = requests.request(http_method, druid_host+endpoint,
headers=headers, data=payload)\n",
+ "print(json.dumps(json.loads(response.text), indent=4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c2e56af-3fdf-40f1-869b-822ac8aafbc8",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Query types\n",
+ "\n",
+ "Druid uses the [Apache Calcite](https://calcite.apache.org/) data
management framework to translate SQL statements into Druid’s native JSON-based
queries on the Broker. The queries are then executed by the Druid cluster.\n",
+ "Native queries are low level, designed to be lightweight and complete
very quickly.\n",
Review Comment:
Added a link to the native queries doc.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
