Snowpark API

https://docs.snowflake.com/en/developer-guide/snowpark/
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### Setup Data for DataFrame Examples

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.DataFrame.html

Creates a temporary table and a temporary stage with a CSV file to support subsequent DataFrame examples.

```python
>>> session.sql("create or replace temp table prices(product_id varchar, amount number(10, 2))").collect()
[Row(status='Table PRICES successfully created.')]
>>> session.sql("insert into prices values ('id1', 10.0), ('id2', 20.0)").collect()
[Row(number of rows inserted=2)]
>>> # Create a CSV file to demo load
>>> import tempfile
>>> with tempfile.NamedTemporaryFile(mode="w+t") as t:
...     t.writelines(["id1, Product A", "\n" "id2, Product B"])
...     t.flush()
...     create_stage_result = session.sql("create or replace temp stage test_stage").collect()
...     put_result = session.file.put(t.name, "@test_stage/test_dir")
```

--------------------------------

### Setup Jupyter Notebooks

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/setup

Commands to install and launch the Jupyter Notebook environment.

```bash
pip install notebook
```

```bash
jupyter notebook
```

--------------------------------

### Start Echo Service Locally

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-4

Run the Tutorial 1 Echo service locally. Ensure the 'requests' library is installed if not already present.

```python
SERVER_PORT=8000 python3 echo_service.py

```

--------------------------------

### Setup Snowflake table for examples

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.read_snowflake.html

Create a temporary table in Snowflake to demonstrate index and column selection behavior.

```python
>>> session = pd.session
>>> table_name = "RESULT_2"
>>> create_result = session.sql(f"CREATE TEMP TABLE {table_name} (A int, B int, C int)").collect()
>>> insert_result = session.sql(f"INSERT INTO {table_name} VALUES(1, 2, 3)").collect()
>>> session.table(table_name).show()
-------------------
|"A"  |"B"  |"C"  |
-------------------
|1    |2    |3    |
-------------------
```

--------------------------------

### Setup Snowflake table for SQL query examples

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.read_snowflake.html

Create a table with multiple rows for testing SQL query execution.

```python
>>> session = pd.session
>>> create_result = session.sql(f"CREATE OR REPLACE TEMP TABLE {table_name} (A int, B int, C int)").collect()
>>> insert_result = session.sql(f"INSERT INTO {table_name} VALUES(1, 2, 3),(-1, -2, -3)").collect()
>>> session.table(table_name).show()
-------------------
|"A"  |"B"  |"C"  |
-------------------
|1    |2    |3    |
|-1   |-2   |-3   |
-------------------
```

--------------------------------

### Setup for Reading Local JSON

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.read_json.html

Imports necessary libraries and creates a temporary directory to store the JSON file for local reading examples.

```python
import tempfile
import json
temp_dir = tempfile.TemporaryDirectory()
temp_dir_name = temp_dir.name
```

--------------------------------

### Example Docker Image Build

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/run-job-as-task

An example of building a Docker image with a specific repository URL and image name.

```bash
docker build --rm --platform linux/amd64 -t myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/my_task_job_image:latest .
```

--------------------------------

### Setup Sample Data for Joins

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

SQL statements to create and populate tables used for demonstrating join operations.

```sql
create or replace table sample_a (
  id_a integer,
  name_a varchar,
  value integer
);
insert into sample_a (id_a, name_a, value) values
  (10, 'A1', 5),
  (40, 'A2', 10),
  (80, 'A3', 15),
  (90, 'A4', 20)
;
create or replace table sample_b (
  id_b integer,
  name_b varchar,
  id_a integer,
  value integer
);
insert into sample_b (id_b, name_b, id_a, value) values
  (4000, 'B1', 40, 10),
  (4001, 'B2', 10, 5),
  (9000, 'B3', 80, 15),
  (9099, 'B4', null, 200)
;
create or replace table sample_c (
  id_c integer,
  name_c varchar,
  id_a integer,
  id_b integer
);
insert into sample_c (id_c, name_c, id_a, id_b) values
  (1012, 'C1', 10, null),
  (1040, 'C2', 40, 4000),
  (1041, 'C3', 40, 4001)
;
```

--------------------------------

### Example Docker Build Command

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-4

An example of the `docker build` command with a specific repository URL and image name.

```bash
docker build --rm --platform linux/amd64 -t myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/service_to_service:latest .
```

--------------------------------

### Verify Snowpark Submit Installation

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-using-submit

Checks the installation by displaying the help menu.

```bash
snowpark-submit -h
```

--------------------------------

### Install Snowpark Connect for Spark in Python

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-local-ide

Create a virtual environment and install the required packages for Python development.

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade --force-reinstall 'snowpark-connect[jdk]'
pip install pyspark==3.5.6
```

--------------------------------

### Install Snowpark Connect for Spark

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-reference

Install the snowpark-connect package using pip.

```bash
pip install snowpark-connect

```

--------------------------------

### Download Files from a Stage

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

Downloads files from a stage using the get method, with an example of filtering files via a regular expression pattern.

```scala
// Download files with names that match a regular expression pattern.
val getOptions = Map("PATTERN" -> s"'.*file_.*.csv.gz'")
val getResults = session.file.get("@myStage", "file:///tmp", getOptions)
```

--------------------------------

### Create and populate sample_product_data table

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

SQL statements to create and insert data into the sample_product_data table used for DataFrame examples.

```sql
CREATE OR REPLACE TABLE sample_product_data (id INT, parent_id INT, category_id INT, name VARCHAR, serial_number VARCHAR, key INT, "3rd" INT);
INSERT INTO sample_product_data VALUES
    (1, 0, 5, 'Product 1', 'prod-1', 1, 10),
    (2, 1, 5, 'Product 1A', 'prod-1-A', 1, 20),
    (3, 1, 5, 'Product 1B', 'prod-1-B', 1, 30),
    (4, 0, 10, 'Product 2', 'prod-2', 2, 40),
    (5, 4, 10, 'Product 2A', 'prod-2-A', 2, 50),
    (6, 4, 10, 'Product 2B', 'prod-2-B', 2, 60),
    (7, 0, 20, 'Product 3', 'prod-3', 3, 70),
    (8, 7, 20, 'Product 3A', 'prod-3-A', 3, 80),
    (9, 7, 20, 'Product 3B', 'prod-3-B', 3, 90),
    (10, 0, 50, 'Product 4', 'prod-4', 4, 100),
    (11, 10, 50, 'Product 4A', 'prod-4-A', 4, 100),
    (12, 10, 50, 'Product 4B', 'prod-4-B', 4, 100);
```

--------------------------------

### Example Docker Push Command

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-4

An example of the `docker push` command to upload an image to a specific repository.

```bash
docker push myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/service_to_service:latest
```

--------------------------------

### Example Docker Image Push

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-7-callers-rights

An example of the `docker push` command, showing how to push a tagged Docker image to a specific repository URL.

```bash
docker push myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/query_service:latest
```

--------------------------------

### Install Snowpark Submit CLI

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-using-submit

Installs the Snowpark Submit package via pip.

```bash
pip install snowpark-submit
```

--------------------------------

### Install Snowpark and Oracle DB Libraries

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/reading-data-from-external-sources

Install the necessary Python packages for Snowpark and Oracle connectivity.

```bash
pip install snowflake-snowpark-python[pandas]
pip install oracledb
```

--------------------------------

### Example Docker Image Push

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/run-job-as-task

An example of pushing a Docker image to a specific repository URL and image name.

```bash
docker push myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/my_task_job_image:latest
```

--------------------------------

### Install Snowpark Python with local testing support

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/testing-locally

Install the library with the required localtest optional dependency.

```bash
pip install "snowflake-snowpark-python[localtest]"
```

--------------------------------

### Create a DataFrame for AsyncJob examples

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.AsyncJob

Initializes a DataFrame to be used in subsequent asynchronous operation examples.

```python
>>> from snowflake.snowpark.functions import when_matched, when_not_matched
>>> from snowflake.snowpark.types import IntegerType, StringType, StructField, StructType
>>> df = session.create_dataframe([[float(4), 3, 5], [2.0, -4, 7], [3.0, 5, 6],[4.0,6,8]], schema=["a", "b", "c"])
```

--------------------------------

### Create sample table

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/tutorials/testing-tutorial

Run the setup script to create the necessary database, schema, and table for testing.

```bash
python setup/create_table.py
```

--------------------------------

### Install Snowpark with Optional Packages

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/setup

Install Snowpark along with additional data analysis libraries like Pandas and PyArrow.

```bash
conda install snowflake-snowpark-python pandas pyarrow
```

```bash
pip install "snowflake-snowpark-python[pandas]"
```

--------------------------------

### Initial Query Example

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-7-callers-rights

This is an example of a query that a service might attempt to execute. It assumes the service does not have direct permissions to the target table or warehouse.

```sql
SELECT * FROM ingress_user_db.ingress_user_schema.ingress_user_table;
```

--------------------------------

### Get ETAG of a File

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.fl_get_etag.html

Use `fl_get_etag` to retrieve the ETAG of a file stored in a stage. This example demonstrates creating a temporary stage, uploading a file, and then using `fl_get_etag` to get its ETAG.

```python
import json
# Create a temp stage.
_ = session.sql("create or replace temp stage mystage").collect()
# Upload a file to a stage.
r = session.file.put("tests/resources/testCSV.csv", "@mystage", auto_compress=False, overwrite=True)
df = session.range(1).select(fl_get_etag(to_file("@mystage/testCSV.csv")).alias("file"))
len(df.collect()[0][0])
```

--------------------------------

### Accessing Nanoseconds

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.Series.dt.nanosecond.html

This example demonstrates how to use `.dt.nanosecond` to get the nanosecond component from a Series of datetimes.

```APIDOC
## Series.dt.nanosecond

### Description

Access the nanoseconds of the datetime.

### Method

Attribute access

### Parameters

This attribute does not take any parameters.

### Returns

A Series containing the nanosecond component (0-999,999,999) of each datetime value.

### Example

```python
import pandas as pd

datetime_series = pd.Series(
    pd.date_range("2000-01-01", periods=3, freq="ns")
)
print(datetime_series.dt.nanosecond)
```

### Output

```
0    0
1    1
2    2
dtype: int32
```
```

--------------------------------

### Clone the tutorial repository

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/tutorials/testing-tutorial

Use git to download the project repository containing the testing tutorial files.

```bash
git clone https://github.com/Snowflake-Labs/sftutorial-snowpark-testing
```

--------------------------------

### Get Absolute Value of a Column

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.abs.html

Use the abs function to get the absolute value of a numeric column in a DataFrame. This example demonstrates creating a DataFrame, selecting the absolute value of a column, and displaying the result.

```python
df = session.create_dataframe([[-1]], schema=["a"])
df.select(abs(col("a")).alias("result")).show()
```

--------------------------------

### Run the sample application

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/quickstart-sbt

Command to execute the main class using sbt.

```bash
sbt "runMain Main"
```

--------------------------------

### Start Snowpark Server and Get Session

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-reference

Starts a Snowpark server in daemon mode and retrieves a session. This pattern is useful when the server needs to outlive individual sessions or when multiple sessions share the same server.

```python
from snowflake.snowpark_connect import start_session, get_session

start_session(is_daemon=True, tcp_port=15002)

spark = get_session()
spark.sql("SELECT current_timestamp()").show()

```

--------------------------------

### Using get and get_path Functions

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

Use get or get_path as alternatives to apply when field names or paths are irregular.

```scala
df.select(get(col("src"), lit("dealership"))).show()
df.select(col("src")("dealership")).show()
```

```scala
df.select(get_path(col("src"), lit("vehicle[0].make"))).show()
df.select(col("src")("vehicle")(0)("make")).show()
```

--------------------------------

### Accessing day_of_year

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.DatetimeIndex.day_of_year

This example demonstrates how to use the `day_of_year` property to get the ordinal day of the year from a `DatetimeIndex`.

```APIDOC
## DatetimeIndex.day_of_year

### Description
Returns the ordinal day of the year for each element in the `DatetimeIndex`.

### Method
Property access

### Parameters
None

### Returns
An Index containing integers indicating the ordinal day of the year.

### Example
```python
import pandas as pd

idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
                        "2/1/2020 11:00:00+00:00"])
print(idx.dayofyear)
# Expected output: Index([1, 32], dtype='int64')
```
```

--------------------------------

### Run the tutorial application

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-tutorial

Commands to execute the tutorial code for Python, Java, and Scala environments.

```python
python tutorial.py
```

```java
mvn compile exec:java -Dexec.mainClass="Tutorial"
```

```scala
sbt "runMain Tutorial"
```

--------------------------------

### Get H3 Cell ID Coverage for a Geography

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.h3_try_coverage_strings.html

This example demonstrates how to use the h3_try_coverage_strings function to get H3 cell IDs covering a polygon. Ensure you import necessary functions like to_geography and lit.

```python
>>> from snowflake.snowpark.functions import to_geography
>>> df = session.create_dataframe([
...     "POLYGON((-122.4194 37.7749, -122.4094 37.7749, -122.4094 37.7849, -122.4194 37.7849, -122.4194 37.7749))"
... ], schema=["geog"])
>>> df.select(h3_try_coverage_strings(to_geography(df["geog"])), lit(9)).alias("coverage")).collect()
[Row(COVERAGE='[
  "89283082803ffff",
  "8928308280bffff",
  "8928308280fffff",
  "89283082843ffff",
  "89283082847ffff",
  "8928308284fffff",
  "89283082857ffff",
  "89283082863ffff",
  "89283082867ffff",
  "8928308286bffff",
  "8928308286fffff",
  "89283082873ffff",
  "89283082877ffff",
  "8928308287bffff",
  "89283082aa7ffff",
  "89283082ab7ffff",
  "89283082b9bffff"
]')
```

--------------------------------

### Get the start point of a LINESTRING using st_startpoint

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.st_startpoint.html

Demonstrates how to use st_startpoint with both geography and geometry data types to extract the starting point of a LINESTRING. Ensure to import necessary functions like to_geography and to_geometry.

```python
>>> from snowflake.snowpark.functions import to_geography, to_geometry
>>> df_geography = session.create_dataframe([["LINESTRING(1 1, 2 2, 3 3, 4 4)"]], schema=["linestring"])
>>> df_geography.select(st_startpoint(to_geography(df_geography["linestring"])).alias("startpoint")).collect()
[Row(STARTPOINT='{
  "coordinates": [
    1.000000000000000e+00,
    1.000000000000000e+00
  ],
  "type": "Point"
}')]
```

```python
>>> df_geometry = session.create_dataframe([["LINESTRING(1 1, 2 2, 3 3, 4 4)"]], schema=["linestring"])
>>> df_geometry.select(st_startpoint(to_geometry(df_geometry["linestring"])).alias("startpoint")).collect()
[Row(STARTPOINT='{
  "coordinates": [
    1.000000000000000e+00,
    1.000000000000000e+00
  ],
  "type": "Point"
}')]
```

--------------------------------

### Accessing the hour component

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.Series.dt.hour.html

This example demonstrates how to use the `.dt.hour` accessor to get the hour from a Series of datetime objects.

```APIDOC
## Series.dt.hour

### Description
Returns a series of the hours of the datetime.

### Method
Attribute Access

### Endpoint
N/A (Instance method)

### Parameters
None

### Request Example
```python
import pandas as pd

datetime_series = pd.Series(
    pd.date_range("2000-01-01", periods=3, freq="h")
)
print(datetime_series.dt.hour)
```

### Response
#### Success Response
Returns a Series containing the hour (0-23) for each datetime element.

#### Response Example
```
0    0
1    1
2    2
dtype: int8
```
```

--------------------------------

### Example Docker Image Build

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-7-callers-rights

An example of the `docker build` command, demonstrating how to specify the repository URL and image name for building a Docker image.

```bash
docker build --rm --platform linux/amd64 -t myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/query_service:latest .
```

--------------------------------

### SQL Statements for Table Setup

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes

Provides a consolidated block of SQL statements to create and populate the 'sample_product_data' table. Use this in a SQL worksheet for setup.

```sql
CREATE OR REPLACE TABLE sample_product_data
  (id INT, parent_id INT, category_id INT, name VARCHAR, serial_number VARCHAR, key INT, "3rd" INT);

INSERT INTO sample_product_data VALUES
  (1, 0, 5, 'Product 1', 'prod-1', 1, 10),
  (2, 1, 5, 'Product 1A', 'prod-1-A', 1, 20),
  (3, 1, 5, 'Product 1B', 'prod-1-B', 1, 30),
  (4, 0, 10, 'Product 2', 'prod-2', 2, 40),
  (5, 4, 10, 'Product 2A', 'prod-2-A', 2, 50),
  (6, 4, 10, 'Product 2B', 'prod-2-B', 2, 60),
  (7, 0, 20, 'Product 3', 'prod-3', 3, 70),
  (8, 7, 20, 'Product 3A', 'prod-3-A', 3, 80),
  (9, 7, 20, 'Product 3B', 'prod-3-B', 3, 90),
  (10, 0, 50, 'Product 4', 'prod-4', 4, 100),
  (11, 10, 50, 'Product 4A', 'prod-4-A', 4, 100),
  (12, 10, 50, 'Product 4B', 'prod-4-B', 4, 100);

SELECT count(*) FROM sample_product_data;
```

--------------------------------

### Accessing microseconds from a datetime Series

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.Series.dt.microsecond.html

This example demonstrates how to use the `.dt.microsecond` accessor to get the microsecond values from a Series of datetimes.

```APIDOC
## Series.dt.microsecond

### Description

Returns the microseconds of the datetime in the Series.

### Method

Attribute Access

### Parameters

This attribute does not take any parameters.

### Request Example

```python
import pandas as pd

datetime_series = pd.Series(
    pd.date_range("2000-01-01", periods=3, freq="us")
)
datetime_series.dt.microsecond
```

### Response

#### Success Response

Returns a Series of integers representing the microseconds.

#### Response Example

```
0    0
1    1
2    2
dtype: int64
```
```

--------------------------------

### Initialize a new Scala project

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/quickstart-sbt

Use the sbt new command with a template to scaffold the project.

```bash
sbt new scala/hello-world.g8
```

--------------------------------

### Get H3 Cells Covering a Geography

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.h3_coverage.html

This example demonstrates how to use the h3_coverage function to get H3 cell IDs for a polygon. It converts a WKT string to a GEOGRAPHY object and then applies h3_coverage at resolution 8. The result is collected and printed.

```python
>>> from snowflake.snowpark.functions import to_geography, lit
>>> df = session.create_dataframe([
...     ["POLYGON((-122.481889 37.826683,-122.479487 37.808548,-122.474150 37.808904,-122.476510 37.826935,-122.481889 37.826683))"]
... ], schema=["polygon_wkt"])
>>> result = df.select(h3_coverage(to_geography(df["polygon_wkt"]), lit(8)).alias("h3_cells")).collect()
>>> result
[Row(H3_CELLS='[
  613196571539931135,
  613196571542028287,
  613196571548319743,
  613196571550416895,
  613196571560902655,
  613196571598651391
]')]
```

--------------------------------

### Run tests with local or live session modes

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/testing-locally

Command-line examples for executing tests in either local or live mode using the configured fixture.

```bash
# Using local mode:
pytest --snowflake-session local

# Using live mode
pytest
```

--------------------------------

### Displaying Table Metadata

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

Example of table structure containing quoted identifiers.

```sql
describe table quoted;
+------------------------+ ...
| name                   | ...
|------------------------+ ...
| name_with_"air"_quotes | ...
| "column_name_quoted"   | ...
+------------------------+ ...
```

--------------------------------

### Get SessionBuilder Object

Source: https://docs.snowflake.com/en/developer-guide/snowpark/java/creating-session

Obtain a SessionBuilder object to configure and create a Snowpark session. This is the starting point for establishing a connection to Snowflake.

```java
import com.snowflake.snowpark_java.*;

... 
// Get a SessionBuilder object.
SessionBuilder builder = Session.builder();

```

--------------------------------

### Basic Snowpark Java Connection and Query

Source: https://docs.snowflake.com/en/developer-guide/snowpark/java/quickstart-intellij

This example demonstrates how to establish a Snowpark session using connection properties and execute a simple 'show tables' SQL command. Replace placeholder values with your Snowflake connection details.

```java
import com.snowflake.snowpark_java.*;
import java.util.HashMap;
import java.util.Map;

public class HelloSnowpark {
  public static void main(String[] args) {
    // Replace the <placeholders> below.
    Map<String, String> properties = new HashMap<>();
    properties.put("URL", "https://<account_identifier>.snowflakecomputing.com:443");
    properties.put("USER", "<user name>");
    properties.put("PASSWORD", "<password>");
    properties.put("ROLE", "<role name>");
    properties.put("WAREHOUSE", "<warehouse name>");
    properties.put("DB", "<database name>");
    properties.put("SCHEMA", "<schema name>");
    Session session = Session.builder().configs(properties).create();
    session.sql("show tables").show();
  }
}
```

--------------------------------

### Merge rows into a table using Scala

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

Examples demonstrating how to perform conditional inserts and updates on a target table based on a source DataFrame.

```scala
val mergeResult = target.merge(source, target("id") === source("id"))
                      .whenNotMatched.insert(Seq(source("id"), source("value")))
                      .collect()
```

```scala
val mergeResult = target.merge(source, target("id") === source("id"))
                      .whenMatched.update(Map("value" -> source("value")))
                      .collect()
```

--------------------------------

### Configure readiness and liveness probes in specification

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference

Example specification file demonstrating both readinessProbe and livenessProbe configurations.

```yaml
spec:
  containers:
  - name: main
    image: <image_name>
    readinessProbe:
      port: 8080
      path: /ready
      periodSeconds: 10       # optional, defaults to 5
      failureThreshold: 3     # optional, defaults to 3
    livenessProbe:
      port: 8080
      path: /health
      periodSeconds: 10       # optional, defaults to 10
      failureThreshold: 3     # optional, defaults to 3
  endpoints:
  - name: main-endpoint
    port: 8080

```

--------------------------------

### Failed Self-Join Attempts

Source: https://docs.snowflake.com/en/developer-guide/snowpark/scala/working-with-dataframes

These examples demonstrate incorrect approaches to self-joins that result in column ambiguity errors.

```scala
// This fails because columns named "id" and "parent_id"
// are in the left and right DataFrames in the join.
val df = session.table("sample_product_data");
val dfJoined = df.join(df, col("id") === col("parent_id"))
```

```scala
// This fails because columns named "id" and "parent_id"
// are in the left and right DataFrames in the join.
val df = session.table("sample_product_data");
val dfJoined = df.join(df, df("id") === df("parent_id"))
```

--------------------------------

### Get SRID of a GEOGRAPHY or GEOMETRY object

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.st_srid.html

Use st_srid to retrieve the SRID from a spatial object. This example demonstrates creating a point and then extracting its SRID.

```python
from snowflake.snowpark.functions import st_makepoint
df = session.create_dataframe([[37.5, 45.5]], schema=["x", "y"])
df.select(st_srid(st_makepoint(df["x"], df["y"])).alias("srid")).collect()
```

--------------------------------

### Create Sample Product Data Table

Source: https://docs.snowflake.com/en/developer-guide/snowpark/java/working-with-dataframes

SQL statements to create and populate the sample_product_data table used in subsequent Snowpark examples.

```sql
CREATE OR REPLACE TABLE sample_product_data (id INT, parent_id INT, category_id INT, name VARCHAR, serial_number VARCHAR, key INT, "3rd" INT, amount NUMBER(12, 2), quantity INT, product_date DATE);
INSERT INTO sample_product_data VALUES
    (1, 0, 5, 'Product 1', 'prod-1', 1, 10, 1.00, 15, TO_DATE('2021.01.01', 'YYYY.MM.DD')),
    (2, 1, 5, 'Product 1A', 'prod-1-A', 1, 20, 2.00, 30, TO_DATE('2021.02.01', 'YYYY.MM.DD')),
    (3, 1, 5, 'Product 1B', 'prod-1-B', 1, 30, 3.00, 45, TO_DATE('2021.03.01', 'YYYY.MM.DD')),
    (4, 0, 10, 'Product 2', 'prod-2', 2, 40, 4.00, 60, TO_DATE('2021.04.01', 'YYYY.MM.DD')),
    (5, 4, 10, 'Product 2A', 'prod-2-A', 2, 50, 5.00, 75, TO_DATE('2021.05.01', 'YYYY.MM.DD')),
    (6, 4, 10, 'Product 2B', 'prod-2-B', 2, 60, 6.00, 90, TO_DATE('2021.06.01', 'YYYY.MM.DD')),
    (7, 0, 20, 'Product 3', 'prod-3', 3, 70, 7.00, 105, TO_DATE('2021.07.01', 'YYYY.MM.DD')),
    (8, 7, 20, 'Product 3A', 'prod-3-A', 3, 80, 7.25, 120, TO_DATE('2021.08.01', 'YYYY.MM.DD')),
    (9, 7, 20, 'Product 3B', 'prod-3-B', 3, 90, 7.50, 135, TO_DATE('2021.09.01', 'YYYY.MM.DD')),
    (10, 0, 50, 'Product 4', 'prod-4', 4, 100, 7.75, 150, TO_DATE('2021.10.01', 'YYYY.MM.DD')),
    (11, 10, 50, 'Product 4A', 'prod-4-A', 4, 100, 8.00, 165, TO_DATE('2021.11.01', 'YYYY.MM.DD')),
    (12, 10, 50, 'Product 4B', 'prod-4-B', 4, 100, 8.50, 180, TO_DATE('2021.12.01', 'YYYY.MM.DD'));
```

--------------------------------

### Get a view by name

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.catalog.Catalog.get_view.html

This example demonstrates how to retrieve a view using its name. If the database and schema are not specified, the view will be looked for in the current database and schema.

```APIDOC
## get_view

### Description
Get the view by name in given database and schema. If database or schema are not provided, get the view in the current database and schema.

### Method Signature
`Catalog.get_view(_view_name : str, _*, _database : Optional[Union[str, DatabaseModel]] = None, _schema : Optional[Union[str, SchemaModel]] = None) -> View`

### Parameters
#### Path Parameters
* **view_name** (str) - Required - name of the view.
* **database** (Optional[Union[str, DatabaseModel]]) - Optional - database name or `Database` object. Defaults to None.
* **schema** (Optional[Union[str, SchemaModel]]) - Optional - schema name or `Schema` object. Defaults to None.
```

--------------------------------

### Create Service with Min Ready Instances

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/scaling-services

Configure a service to be considered ready with fewer instances than the minimum, useful during maintenance or upgrades.

```sql
CREATE SERVICE echo_service
   IN COMPUTE POOL tutorial_compute_pool
   FROM @tutorial_stage
   SPECIFICATION_FILE='echo_spec.yaml'
   MIN_INSTANCES=2
   MAX_INSTANCES=4
   MIN_READY_INSTANCES=1;
```

--------------------------------

### Get Bitmap Bucket Number

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.bitmap_bucket_number.html

Use bitmap_bucket_number to determine the bucket number for values in a DataFrame column. This example shows how to apply the function and collect the results.

```python
>>> df = session.create_dataframe([1, 2, 3, 32768, 32769], schema=["a"])
>>> df.select(bitmap_bucket_number(col("a")).alias("bucket_number")).collect()
[Row(BUCKET_NUMBER=1), Row(BUCKET_NUMBER=1), Row(BUCKET_NUMBER=1), Row(BUCKET_NUMBER=1), Row(BUCKET_NUMBER=2)]
```

--------------------------------

### Get Spark Session from Running Server

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-reference

Return a SparkSession connected to an already running Snowpark Connect for Spark server. The server must have been previously started.

```python
def get_session(
    url: str | None = None,
    conf: SparkConf = None,
) -> SparkSession

```

--------------------------------

### Clean up tutorial resources

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-tutorial

SQL commands to drop tables and remove stage files created during the tutorial.

```sql
DROP TABLE IF EXISTS sales;
DROP TABLE IF EXISTS sales_with_tax;
REMOVE @~/tutorial_output/;
```

--------------------------------

### Set JAVA_HOME Environment Variable (macOS/Linux)

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-jdbc

Configure the JAVA_HOME environment variable to point to your Java installation's root directory. This is required for client-side setup.

```bash
export JAVA_HOME=/path/to/your/jdk/home

```

--------------------------------

### Configure Connection Dictionary with Environment Variables

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/spcs-execute-sql

Example of constructing a connection configuration dictionary using environment variables, including database and schema.

```python
{
"account": SNOWFLAKE_ACCOUNT,
"host": SNOWFLAKE_HOST,
"authenticator": "oauth",
"token": get_login_token(),
"warehouse": SNOWFLAKE_WAREHOUSE,
"database": SNOWFLAKE_DATABASE,
"schema": SNOWFLAKE_SCHEMA
}
...

```

--------------------------------

### Example Docker Build Command

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1

An example of the `docker build` command using a sample repository URL and image name. This command constructs the Docker image for the echo service.

```bash
docker build --rm --platform linux/amd64 -t myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/my_echo_service_image:latest .
```

--------------------------------

### Get Rightmost Substring using right()

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.right.html

Use the `right` function to extract a specified number of characters from the end of a string column. This example demonstrates its usage with a DataFrame.

```python
df = session.create_dataframe([["abc"], ["def"]], schema=["a"])
df.select(right(col("a"), 2).alias("result")).show()
```

--------------------------------

### List All Services

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-7-callers-rights

Execute the SHOW SERVICES command to get a list of all services created in your account.

```sql
SHOW SERVICES;

```

--------------------------------

### Dockerfile for Python Service

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/advanced/tutorial-4

This Dockerfile sets up a Python 3.10 environment, copies the service script, installs the 'requests' library, and defines the command to run the Python script when the container starts.

```dockerfile
ARG BASE_IMAGE=python:3.10-slim-buster
FROM $BASE_IMAGE
COPY service_to_service.py ./
RUN pip install --upgrade pip && \
  pip install requests
CMD ["python3", "service_to_service.py"]

```

--------------------------------

### Create a Session from connection parameters

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.Session.html

Initialize a session by providing a dictionary of connection parameters such as user, password, and account details.

```python
>>> connection_parameters = {
...     "user": "<user_name>",
...     "password": "<password>",
...     "account": "<account_name>",
...     "role": "<role_name>",
...     "warehouse": "<warehouse_name>",
...     "database": "<database_name>",
...     "schema": "<schema_name>",
... }
>>> session = Session.builder.configs(connection_parameters).create() 
```

--------------------------------

### Get Maximum Y Coordinate of Spatial Objects

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.st_ymax.html

Use `st_ymax` with `to_geography` to extract the maximum Y coordinate from different spatial data types. This example demonstrates its application on POINT and LINESTRING data.

```python
>>> from snowflake.snowpark.functions import to_geography
>>> df = session.create_dataframe([
...     ["POINT(-180 0)"],
...     ["POINT(180 0)"],
...     ["LINESTRING(-60 30,60 30)"],
...     ["LINESTRING(-60 -30,60 -30)"]
... ], schema=["geog"])
>>> df.select(st_ymax(to_geography(df["geog"])).alias("ymax")).collect()
[Row(YMAX=0.0), Row(YMAX=0.0), Row(YMAX=49.106605350869174), Row(YMAX=-29.999999999999943)]
```

--------------------------------

### Get index locations between times

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.DatetimeIndex.indexer_between_time

Use indexer_between_time to find the positions of datetime values within a specific time range. You can control whether the start and end times are included in the selection.

```python
>>> idx = pd.date_range("2023-01-01", periods=4, freq="h")
>>> idx
DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
               '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
              dtype='datetime64[ns]', freq=None)
>>> idx.indexer_between_time("00:00", "2:00", include_end=False)  
array([0, 1])
```

--------------------------------

### Get Stage Name from File

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.fl_get_stage.html

Use `fl_get_stage` to retrieve the stage name from a file object. This example shows how to create a temporary stage, upload a file, and then extract the stage name using `fl_get_stage`.

```python
import json
# Create a temp stage.
_ = session.sql("create or replace temp stage mystage").collect()
# Upload a file to a stage.
r = session.file.put("tests/resources/testCSV.csv", "@mystage", auto_compress=False, overwrite=True)
df = session.range(1).select(fl_get_stage(to_file("@mystage/testCSV.csv")).alias("file"))
df.collect()[0][0].split(".")[-1]
```

--------------------------------

### Create Database-Scoped Objects for Tutorials

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/common-setup

Execute this script using the 'test_role' to create schemas, image repositories, and stages within your tutorial database. This prepares storage for service code and specification files.

```sql
USE ROLE test_role;
USE DATABASE tutorial_db;
USE WAREHOUSE tutorial_warehouse;

CREATE SCHEMA IF NOT EXISTS data_schema;
CREATE IMAGE REPOSITORY IF NOT EXISTS tutorial_repository;
CREATE STAGE IF NOT EXISTS tutorial_stage
  DIRECTORY = ( ENABLE = true );
```

--------------------------------

### Get Column Name

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/java/com/snowflake/snowpark_java/types/ColumnIdentifier.html

Returns the name of the column. The formatting rules for the returned name depend on whether the original name was quoted and its starting characters. Consult Snowflake's identifier syntax documentation for detailed rules.

```java
public java.lang.String name()
```

--------------------------------

### Using WhenMatchedClause.delete() in a Merge Operation

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.WhenMatchedClause.delete.html

This example demonstrates how to use `when_matched().delete()` to remove rows from a target table that match rows in a source DataFrame during a merge operation. It shows the setup of DataFrames, the merge call, and verification of the deleted rows.

```python
from snowflake.snowpark.functions import when_matched
target_df = session.create_dataframe([(10, "old"), (10, "too_old"), (11, "old")], schema=["key", "value"])
target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary")
target = session.table("my_table")
source = session.create_dataframe([(10, "new")], schema=["key", "value"])
target.merge(source, target["key"] == source["key"], [when_matched().delete()])
target.collect() # the rows are deleted
```

--------------------------------

### Slice from a start position

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.Series.str.slice.html

Extracts substrings starting from the specified `start` index to the end of each string in the Series.

```python
>>> s.str.slice(start=1)
0        oala
1          og
2    hameleon
dtype: object
```

--------------------------------

### Example Docker Push Command

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1

An example of the `docker push` command, demonstrating how to upload the built image to the specified repository. This makes the image available for Snowpark Container Services.

```bash
docker push myorg-myacct.registry.snowflakecomputing.com/tutorial_db/data_schema/tutorial_repository/my_echo_service_image:latest
```

--------------------------------

### Configure readinessProbe in specification

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference

Example specification file demonstrating the readinessProbe configuration within the containers field.

```yaml
spec:
  containers:
  - name: echo
    image: <image_name>
    env:
      SERVER_PORT: 8088
      CHARACTER_NAME: Bob
    readinessProbe:
      port: 8088
      path: /healthcheck
      periodSeconds: 10       # optional, defaults to 5
      failureThreshold: 3     # optional, defaults to 3
  endpoints:
  - name: echo-endpoint
    port: 8088

```

--------------------------------

### Install SQL Server ODBC Driver using Homebrew

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/reading-data-from-external-sources

Installs the Microsoft ODBC Driver for SQL Server and related tools using Homebrew. Ensure you have Homebrew installed.

```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew update
HOMEBREW_ACCEPT_EULA=Y brew install msodbcsql mssql-tools

```

--------------------------------

### Install psycopg2

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/reading-data-from-external-sources

Install the psycopg2 library, which is required to connect to PostgreSQL from Snowpark.

```bash
pip install psycopg2

```

--------------------------------

### Compile and Run Scala Application

Source: https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-local-ide

SBT command to run the Scala Snowpark Connect example.

```bash
sbt "runMain SnowparkConnectExample"
```

--------------------------------

### Create a DataFrame

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.DataFrame.sample.html

Initializes a sample DataFrame for demonstration purposes.

```python
>>> df = pd.DataFrame({"num_legs": [2, 4, 8, 0],
...                    "num_wings": [2, 0, 0, 0],
...                    "num_specimen_seen": [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

```

--------------------------------

### Install pandas on Snowflake

Source: https://docs.snowflake.com/en/developer-guide/snowpark/python/pandas-on-snowflake

Install the snowflake-snowpark-python package with Modin support using pip.

```bash
pip install "snowflake-snowpark-python[modin]"
```

--------------------------------

### Set Current Database and Schema in Session

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/spcs-execute-sql

Example of setting the current database and schema for a session using SQL commands.

```sql
USE DATABASE tutorial_db;
...
USE SCHEMA data_schema;

```

--------------------------------

### Initialize a Series for take operations

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/modin/pandas_api/modin.pandas.Series.take.html

Create a sample Series to demonstrate positional indexing.

```python
>>> ser = pd.Series([-1, 5, 6, 2, 4])
>>> ser
0   -1
1    5
2    6
3    2
4    4
dtype: int64
```

--------------------------------

### regexp_instr Examples

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.regexp_instr.html

Examples demonstrating the usage of the regexp_instr function with different parameter combinations.

```APIDOC
## regexp_instr Function Usage

### Description

The `regexp_instr` function returns the starting position of the nth occurrence of a regular expression pattern within a subject string. It supports various optional parameters to customize the search.

### Parameters

- `subject` (Column): The string to search within.
- `pattern` (Column): The regular expression pattern to search for.
- `position` (int, optional): The position in the subject string to start the search from. Defaults to 1.
- `occurrence` (int, optional): The occurrence of the pattern to find. Defaults to 1.
- `option` (str, optional): A string of characters that specify the matching behavior. For example, 'c' for case-sensitive matching, 'i' for case-insensitive matching.
- `regexp_parameters` (Column, optional): A column containing regular expression parameters, similar to `option`.
- `group_num` (int, optional): The capturing group number within the pattern to return the position of. Defaults to 0 (returns the position of the entire pattern match).

### Examples

#### Skipping position, occurrence, and option - with regexp_parameters only
```python
df9 = session.create_dataframe([["Hello World", "hello", "i"]], schema=["subject", "pattern", "regexp_parameters"])
df9.select(regexp_instr(col("subject"), col("pattern"), regexp_parameters=col("regexp_parameters")).alias("skip_to_regexp_params")).collect()
# Expected Output: [Row(SKIP_TO_REGEXP_PARAMS=1)]
```

#### Skipping position, occurrence, option, and regexp_parameters - with group_num only
```python
df10 = session.create_dataframe([["Hello (world) (Test)", "(w+)", 1]], schema=["subject", "pattern", "group_num"])
df10.select(regexp_instr(col("subject"), col("pattern"), group_num=col("group_num")).alias("skip_to_group_num")).collect()
# Expected Output: [Row(SKIP_TO_GROUP_NUM=1)]
```

#### Skipping position and occurrence - with option and regexp_parameters
```python
df11 = session.create_dataframe([["Hello World", "Hello", 1, "i"]], schema=["subject", "pattern", "option", "regexp_parameters"])
df11.select(regexp_instr(col("subject"), col("pattern"), option=col("option"), regexp_parameters=col("regexp_parameters")).alias("skip_position_occurrence_with_params")).collect()
# Expected Output: [Row(SKIP_POSITION_OCCURRENCE_WITH_PARAMS=6)]
```

#### Skipping position, occurrence, and option - with regexp_parameters and group_num
```python
df12 = session.create_dataframe([["Hello (World) (Test)", "(w+)", "c", 1]], schema=["subject", "pattern", "regexp_parameters", "group_num"])
df12.select(regexp_instr(col("subject"), col("pattern"), regexp_parameters=col("regexp_parameters"), group_num=col("group_num")).alias("skip_to_params_and_group")).collect()
# Expected Output: [Row(SKIP_TO_PARAMS_AND_GROUP=1)]
```
```

--------------------------------

### USING Parameter Syntax for Specification Variables

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-services

Shows the general syntax for the `USING` parameter in a `CREATE SERVICE` command, which is used to provide values for specification variables.

```sql
USING( var_name=>var_value, [var_name=>var_value, ... ] );

```

--------------------------------

### Create a Window Object with Start Time Offset

Source: https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.functions.window.html

Demonstrates creating a window object with a specified start time offset. The window's start and end times are adjusted based on the 'start_time' parameter.

```python
import datetime
from snowflake.snowpark.functions import window
df = session.createDataFrame(
     [(datetime.datetime.strptime("2024-10-31 09:05:00.000", "%Y-%m-%d %H:%M:%S.%f"),)],
     schema=["time"]
)
df.select(window(df.time, "5 minutes", start_time="2 minutes")).show()
```

--------------------------------

### Create Service with Instance Constraints

Source: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/specification-reference

Example of creating a service with defined instance counts and compute pool association.

```sql
CREATE SERVICE echo_service
  MIN_INSTANCES=2
  MAX_INSTANCES=2
  IN COMPUTE POOL tutorial_compute_pool
  FROM @<stage_path>
  SPEC=<spec-file-stage-path>;
```
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