Or create ~/.snowflake/config.toml manually


```toml
[connections.default]
account = "orgname-accountname"
user = "USERNAME"
authenticator = "SNOWFLAKE_JWT"
private_key_path = "~/.snowflake/rsa_key.p8"

Test Connection

snow connection test -c default
snow sql -q "SELECT CURRENT_USER(), CURRENT_ACCOUNT()"

When to Use This Skill

Use when:

Building applications on Snowflake platform
Using Cortex AI functions in SQL queries
Developing Native Apps for Marketplace
Setting up JWT key-pair authentication
Working with Snowpark Python

Don't use when:

Building Streamlit apps (use streamlit-snowflake skill)
Need data engineering/ETL patterns
Working with BI tools (Tableau, Looker)

Cortex AI Functions

Snowflake Cortex provides LLM capabilities directly in SQL. Functions are in the SNOWFLAKE.CORTEX schema.

Core Functions

Function	Purpose	GA Status
`COMPLETE` / `AI_COMPLETE`	Text generation from prompt	GA Nov 2025
`SUMMARIZE` / `AI_SUMMARIZE`	Summarize text	GA
`TRANSLATE` / `AI_TRANSLATE`	Translate between languages	GA Sep 2025
`SENTIMENT` / `AI_SENTIMENT`	Sentiment analysis	GA Jul 2025
`AI_FILTER`	Natural language filtering	GA Nov 2025
`AI_CLASSIFY`	Categorize text/images	GA Nov 2025
`AI_AGG`	Aggregate insights across rows	GA Nov 2025

COMPLETE Function

-- Simple prompt
SELECT SNOWFLAKE.CORTEX.COMPLETE(
    'llama3.1-70b',
    'Explain quantum computing in one sentence'
) AS response;

-- With conversation history
SELECT SNOWFLAKE.CORTEX.COMPLETE(
    'llama3.1-70b',
    [
        {'role': 'system', 'content': 'You are a helpful assistant'},
        {'role': 'user', 'content': 'What is Snowflake?'}
    ]
) AS response;

-- With options
SELECT SNOWFLAKE.CORTEX.COMPLETE(
    'mistral-large2',
    'Summarize this document',
    {'temperature': 0.3, 'max_tokens': 500}
) AS response;

Available Models:

llama3.1-70b, llama3.1-8b, llama3.2-3b
mistral-large2, mistral-7b
snowflake-arctic
gemma-7b
claude-3-5-sonnet (200K context)

Model Context Windows (Updated 2025):

Model	Context Window	Best For
Claude 3.5 Sonnet	200,000 tokens	Large documents, long conversations
Llama3.1-70b	128,000 tokens	Complex reasoning, medium documents
Llama3.1-8b	8,000 tokens	Simple tasks, short text
Llama3.2-3b	8,000 tokens	Fast inference, minimal text
Mistral-large2	Variable	Check current docs
Snowflake Arctic	Variable	Check current docs

Token Math: ~4 characters = 1 token. A 32,000 character document ≈ 8,000 tokens.

Error: Input exceeds context window limit → Use smaller model or chunk your input.

SUMMARIZE Function

-- Single text
SELECT SNOWFLAKE.CORTEX.SUMMARIZE(article_text) AS summary
FROM articles
LIMIT 10;

-- Aggregate across rows (no context window limit)
SELECT AI_SUMMARIZE_AGG(review_text) AS all_reviews_summary
FROM product_reviews
WHERE product_id = 123;

TRANSLATE Function

-- Translate to English (auto-detect source)
SELECT SNOWFLAKE.CORTEX.TRANSLATE(
    review_text,
    '',      -- Empty = auto-detect source language
    'en'     -- Target language
) AS translated
FROM international_reviews;

-- Explicit source language
SELECT AI_TRANSLATE(
    description,
    'es',    -- Source: Spanish
    'en'     -- Target: English
) AS translated
FROM spanish_products;

AI_FILTER (Natural Language Filtering)

Performance: As of September 2025, AI_FILTER includes automatic optimization delivering 2-10x speedup and up to 60% token reduction for suitable queries.

-- Filter with plain English
SELECT * FROM customer_feedback
WHERE AI_FILTER(
    feedback_text,
    'mentions shipping problems or delivery delays'
);

-- Combine with SQL predicates for maximum optimization
-- Query planner applies standard filters FIRST, then AI on smaller dataset
SELECT * FROM support_tickets
WHERE created_date > '2025-01-01'  -- Standard filter applied first
  AND AI_FILTER(description, 'customer is angry or frustrated');

Best Practice: Always combine AI_FILTER with traditional SQL predicates (date ranges, categories, etc.) to reduce the dataset before AI processing. This maximizes the automatic optimization benefits.

Throttling: During peak usage, AI function requests may be throttled with retry-able errors. Implement exponential backoff for production applications (see Known Issue #10).

AI_CLASSIFY

-- Categorize support tickets
SELECT
    ticket_id,
    AI_CLASSIFY(
        description,
        ['billing', 'technical', 'shipping', 'other']
    ) AS category
FROM support_tickets;

Billing

Cortex AI functions bill based on tokens:

~4 characters = 1 token
Both input AND output tokens are billed
Rates vary by model (larger models cost more)

Cost Management at Scale (Community-sourced):

Real-world production case study showed a single AI_COMPLETE query processing 1.18 billion records cost nearly $5K in credits. Cost drivers to watch:

Cross-region inference: Models not available in your region incur additional data transfer costs
Warehouse idle time: Unused compute still bills, but aggressive auto-suspend adds resume overhead
Large table joins: Complex queries with AI functions multiply costs

-- This seemingly simple query can be expensive at scale
SELECT
    product_id,
    AI_COMPLETE('mistral-large2', 'Summarize: ' || review_text) as summary
FROM product_reviews  -- 1 billion rows
WHERE created_date > '2024-01-01';

-- Cost = (input tokens + output tokens) × row count × model rate
-- At scale, this adds up fast

Best Practices:

Filter datasets BEFORE applying AI functions
Right-size warehouses (don't over-provision)
Monitor credit consumption with QUERY_HISTORY views
Consider batch processing instead of row-by-row AI operations

Source: The Hidden Cost of Snowflake Cortex AI (Community blog with billing evidence)

Authentication

JWT Key-Pair Authentication

Critical: Snowflake uses TWO account identifier formats:

Format	Example	Used For
Organization-Account	`irjoewf-wq46213`	REST API URLs, connection config
Account Locator	`NZ90655`	JWT claims (`iss`, `sub`)

These are NOT interchangeable!

Discover Your Account Locator

SELECT CURRENT_ACCOUNT();  -- Returns: NZ90655

Generate RSA Key Pair

# Generate private key (PKCS#8 format required)
openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out ~/.snowflake/rsa_key.p8 -nocrypt

# Generate public key
openssl rsa -in ~/.snowflake/rsa_key.p8 -pubout -out ~/.snowflake/rsa_key.pub

# Get fingerprint for JWT claims
openssl rsa -in ~/.snowflake/rsa_key.p8 -pubout -outform DER | \
  openssl dgst -sha256 -binary | openssl enc -base64

Register Public Key with User

-- In Snowflake worksheet (requires ACCOUNTADMIN or SECURITYADMIN)
ALTER USER my_user SET RSA_PUBLIC_KEY='MIIBIjANBgkq...';

JWT Claim Format

iss: ACCOUNT_LOCATOR.USERNAME.SHA256:fingerprint
sub: ACCOUNT_LOCATOR.USERNAME

Example:

iss: NZ90655.JEZWEB.SHA256:jpZO6LvU2SpKd8tE61OGfas5ZXpfHloiJd7XHLPDEEA=
sub: NZ90655.JEZWEB

SPCS Container Authentication (v4.2.0+)

New in January 2026: Connector automatically detects and uses SPCS service identifier tokens when running inside Snowpark Container Services.

# No special configuration needed inside SPCS containers
import snowflake.connector

# Auto-detects SPCS_TOKEN environment variable
conn = snowflake.connector.connect()

This enables seamless authentication from containerized Snowpark services without explicit credentials.

Source: Release v4.2.0

Snow CLI Commands

Project Management

# Initialize project
snow init

# Execute SQL
snow sql -q "SELECT 1"
snow sql -f query.sql

# View logs
snow logs

Native App Commands

# Development
snow app run              # Deploy and run locally
snow app deploy           # Upload to stage only
snow app teardown         # Remove app

# Versioning
snow app version create V1_0
snow app version list
snow app version drop V1_0

# Publishing
snow app publish --version V1_0 --patch 0

# Release Channels
snow app release-channel list
snow app release-channel add-version --channel ALPHA --version V1_0
snow app release-directive set default --version V1_0 --patch 0 --channel DEFAULT

Streamlit Commands

snow streamlit deploy --replace
snow streamlit deploy --replace --open

Stage Commands

snow stage list
snow stage copy @my_stage/file.txt ./local/

Native App Development

Project Structure

my_native_app/
├── snowflake.yml           # Project config
├── manifest.yml            # App manifest
├── setup_script.sql        # Installation script
├── app/
│   └── streamlit/
│       ├── environment.yml
│       └── streamlit_app.py
└── scripts/
    └── setup.sql

snowflake.yml

definition_version: 2

native_app:
  name: my_app
  package:
    name: my_app_pkg
    distribution: external    # For marketplace
  application:
    name: my_app
  source_stage: stage/dev
  artifacts:
    - src: manifest.yml
      dest: manifest.yml
    - src: setup_script.sql
      dest: setup_script.sql
    - src: app/streamlit/environment.yml
      dest: streamlit/environment.yml
    - src: app/streamlit/streamlit_app.py
      dest: streamlit/streamlit_app.py
  enable_release_channels: true  # For ALPHA/BETA channels

manifest.yml

manifest_version: 1

artifacts:
  setup_script: setup_script.sql
  default_streamlit: streamlit/streamlit_app.py

# Note: Do NOT include privileges section - Native Apps can't declare privileges

External Access Integration

Native Apps calling external APIs need this setup:

-- 1. Create network rule (in a real database, NOT app package)
CREATE DATABASE IF NOT EXISTS MY_APP_UTILS;

CREATE OR REPLACE NETWORK RULE MY_APP_UTILS.PUBLIC.api_rule
  MODE = EGRESS
  TYPE = HOST_PORT
  VALUE_LIST = ('api.example.com:443');

-- 2. Create integration
CREATE OR REPLACE EXTERNAL ACCESS INTEGRATION my_app_integration
  ALLOWED_NETWORK_RULES = (MY_APP_UTILS.PUBLIC.api_rule)
  ENABLED = TRUE;

-- 3. Grant to app
GRANT USAGE ON INTEGRATION my_app_integration
  TO APPLICATION MY_APP;

-- 4. CRITICAL: Attach to Streamlit (must repeat after EVERY deploy!)
ALTER STREAMLIT MY_APP.config_schema.my_streamlit
  SET EXTERNAL_ACCESS_INTEGRATIONS = (my_app_integration);

Warning: Step 4 resets on every snow app run. Must re-run after each deploy!

Shared Data Pattern

When your Native App needs data from an external database:

-- 1. Create shared_data schema in app package
CREATE SCHEMA IF NOT EXISTS MY_APP_PKG.SHARED_DATA;

-- 2. Create views referencing external database
CREATE OR REPLACE VIEW MY_APP_PKG.SHARED_DATA.MY_VIEW AS
SELECT * FROM EXTERNAL_DB.SCHEMA.TABLE;

-- 3. Grant REFERENCE_USAGE (CRITICAL!)
GRANT REFERENCE_USAGE ON DATABASE EXTERNAL_DB
  TO SHARE IN APPLICATION PACKAGE MY_APP_PKG;

-- 4. Grant access to share
GRANT USAGE ON SCHEMA MY_APP_PKG.SHARED_DATA
  TO SHARE IN APPLICATION PACKAGE MY_APP_PKG;
GRANT SELECT ON ALL VIEWS IN SCHEMA MY_APP_PKG.SHARED_DATA
  TO SHARE IN APPLICATION PACKAGE MY_APP_PKG;

In setup_script.sql, reference shared_data.view_name (NOT the original database).

Marketplace Publishing

Security Review Workflow

# 1. Deploy app
snow app run

# 2. Create version
snow app version create V1_0

# 3. Check security review status
snow app version list
# Wait for review_status = APPROVED

# 4. Set release directive
snow app release-directive set default --version V1_0 --patch 0 --channel DEFAULT

# 5. Create listing in Snowsight Provider Studio (UI only)

Security Review Statuses

Status	Meaning	Action
`NOT_REVIEWED`	Scan hasn't run	Check DISTRIBUTION is EXTERNAL
`IN_PROGRESS`	Scan running	Wait
`APPROVED`	Passed	Can publish
`REJECTED`	Failed	Fix issues or appeal
`MANUAL_REVIEW`	Human reviewing	Wait (can take days)

Triggers manual review: External access integrations, Streamlit components, network calls.

Provider Studio Fields

Field	Max Length	Notes
Title	72 chars	App name
Subtitle	128 chars	One-liner
Description	10,000 chars	HTML editor
Business Needs	6 max	Select from dropdown
Quick Start Examples	10 max	Title + Description + SQL
Data Dictionary	Required	Mandatory for data listings (2025)

Paid Listing Prerequisites

#	Requirement
1	Full Snowflake account (not trial)
2	ACCOUNTADMIN role
3	Provider Profile approved
4	Stripe account configured
5	Provider & Consumer Terms accepted
6	Contact Marketplace Ops

Note: Cannot convert free listing to paid. Must create new listing.

Snowpark Python

Session Setup

from snowflake.snowpark import Session

connection_params = {
    "account": "orgname-accountname",
    "user": "USERNAME",
    "password": "PASSWORD",  # Or use private_key_path
    "warehouse": "COMPUTE_WH",
    "database": "MY_DB",
    "schema": "PUBLIC"
}

session = Session.builder.configs(connection_params).create()

DataFrame Operations

# Read table
df = session.table("MY_TABLE")

# Filter and select
result = df.filter(df["STATUS"] == "ACTIVE") \
           .select("ID", "NAME", "CREATED_AT") \
           .sort("CREATED_AT", ascending=False)

# Execute
result.show()

# Collect to Python
rows = result.collect()

Row Access (Common Gotcha)

# WRONG - dict() doesn't work on Snowpark Row
config = dict(result[0])

# CORRECT - Access columns explicitly
row = result[0]
config = {
    'COLUMN_A': row['COLUMN_A'],
    'COLUMN_B': row['COLUMN_B'],
}

DML Statistics (v4.2.0+)

New in January 2026: SnowflakeCursor.stats property exposes granular DML statistics for operations where rowcount is insufficient (e.g., CTAS queries).

# Before v4.2.0 - rowcount returns -1 for CTAS
cursor.execute("CREATE TABLE new_table AS SELECT * FROM source WHERE active = true")
print(cursor.rowcount)  # Returns -1 (not helpful!)

# After v4.2.0 - stats property shows actual row counts
cursor.execute("CREATE TABLE new_table AS SELECT * FROM source WHERE active = true")
print(cursor.stats)  # Returns {'rows_inserted': 1234, 'duplicates': 0, ...}

Source: Release v4.2.0

UDFs and Stored Procedures

from snowflake.snowpark.functions import udf, sproc

# Register UDF
@udf(name="my_udf", replace=True)
def my_udf(x: int) -> int:
    return x * 2

# Register Stored Procedure
@sproc(name="my_sproc", replace=True)
def my_sproc(session: Session, table_name: str) -> str:
    df = session.table(table_name)
    count = df.count()
    return f"Row count: {count}"

REST API (SQL API v2)

The REST API is the foundation for programmatic Snowflake access from Cloudflare Workers.

Endpoint

https://{org-account}.snowflakecomputing.com/api/v2/statements

Required Headers (CRITICAL)

ALL requests must include these headers - missing Accept causes silent failures:

const headers = {
  'Authorization': `Bearer ${jwt}`,
  'Content-Type': 'application/json',
  'Accept': 'application/json',  // REQUIRED - "null" error if missing
  'User-Agent': 'MyApp/1.0',
};

Async Query Handling

Even simple queries return async (HTTP 202). Always implement polling:

// Submit returns statementHandle, not results
const submit = await fetch(url, { method: 'POST', headers, body });
const { statementHandle } = await submit.json();

// Poll until complete
while (true) {
  const status = await fetch(`${url}/${statementHandle}`, { headers });
  if (status.status === 200) break;  // Complete
  if (status.status === 202) {
    await sleep(2000);  // Still running
    continue;
  }
}

Workers Subrequest Limits

Plan	Limit	Safe Polling
Free	50	45 attempts @ 2s = 90s max
Paid	1,000	100 attempts @ 500ms = 50s max

Fetch Timeouts

Workers fetch() has no default timeout. Always use AbortController:

const response = await fetch(url, {
  signal: AbortSignal.timeout(30000),  // 30 seconds
  headers,
});

Cancel on Timeout

Cancel queries when timeout occurs to avoid warehouse costs:

POST /api/v2/statements/{statementHandle}/cancel

See templates/snowflake-rest-client.ts for complete implementation.

Known Issues

1. Account Identifier Confusion

Symptom: JWT auth fails silently, queries don't appear in Query History.

Cause: Using org-account format in JWT claims instead of account locator.

Fix: Use SELECT CURRENT_ACCOUNT() to get the actual account locator.

2. External Access Reset

Symptom: API calls fail after snow app run.

Cause: External access integration attachment resets on every deploy.

Fix: Re-run ALTER STREAMLIT ... SET EXTERNAL_ACCESS_INTEGRATIONS after each deploy.

3. Release Channel Syntax

Symptom: ALTER APPLICATION PACKAGE ... SET DEFAULT RELEASE DIRECTIVE fails.

Cause: Legacy SQL syntax doesn't work with release channels enabled.

Fix: Use snow CLI: snow app release-directive set default --version V1_0 --patch 0 --channel DEFAULT

4. Artifact Nesting

Symptom: Files appear in streamlit/streamlit/ instead of streamlit/.

Cause: Directory mappings in snowflake.yml nest the folder name.

Fix: List individual files explicitly in artifacts, not directories.

5. REFERENCE_USAGE Missing

Symptom: "A view that is added to the shared content cannot reference objects from other databases"

Cause: Missing GRANT REFERENCE_USAGE ON DATABASE for shared data.

Fix: Always grant REFERENCE_USAGE before snow app run when using external databases.

6. REST API Missing Accept Header

Symptom: "Unsupported Accept header null is specified" on polling requests.

Cause: Initial request had Accept: application/json but polling request didn't.

Fix: Use consistent headers helper function for ALL requests (submit, poll, cancel).

7. Workers Fetch Hangs Forever

Symptom: Worker hangs indefinitely waiting for Snowflake response.

Cause: Cloudflare Workers' fetch() has no default timeout.

Fix: Always use AbortSignal.timeout(30000) on all Snowflake requests.

8. Too Many Subrequests

Symptom: "Too many subrequests" error during polling.

Cause: Polling every 1 second × 600 attempts = 600 subrequests exceeds limits.

Fix: Poll every 2-5 seconds, limit to 45 (free) or 100 (paid) attempts.

9. Warehouse Not Auto-Resuming (Perceived)

Symptom: Queries return statementHandle but never complete (code 090001 indefinitely).

Cause: 090001 means "running" not error. Warehouse IS resuming, just takes time.

Fix: Auto-resume works. Wait longer or explicitly resume first: POST /api/v2/warehouses/{wh}:resume

10. Memory Leaks in Connector 4.x (Active Issue)

Error: Long-running Python applications show memory growth over time Source: GitHub Issue #2727, #2725 Affects: snowflake-connector-python 4.0.0 - 4.2.0

Why It Happens:

SessionManager uses defaultdict which prevents garbage collection
SnowflakeRestful.fetch() holds references that leak during query execution

Prevention: Reuse connections rather than creating new ones repeatedly. Fix is in progress via PR #2741 and PR #2726.

# AVOID - creates new connection each iteration
for i in range(1000):
    conn = snowflake.connector.connect(...)
    cursor = conn.cursor()
    cursor.execute("SELECT 1")
    cursor.close()
    conn.close()

# BETTER - reuse connection
conn = snowflake.connector.connect(...)
cursor = conn.cursor()
for i in range(1000):
    cursor.execute("SELECT 1")
cursor.close()
conn.close()

Status: Fix expected in connector v4.3.0 or later

11. AI Function Throttling During Peak Usage

Error: "Request throttled due to high usage. Please retry." Source: Snowflake Cortex Documentation Affects: All Cortex AI functions (COMPLETE, FILTER, CLASSIFY, etc.)

Why It Happens: AI/LLM requests may be throttled during high usage periods to manage platform capacity. Throttled requests return errors and require manual retries.

Prevention: Implement retry logic with exponential backoff:

import time
import snowflake.connector

def execute_with_retry(cursor, query, max_retries=3):
    for attempt in range(max_retries):
        try:
            return cursor.execute(query).fetchall()
        except snowflake.connector.errors.DatabaseError as e:
            if "throttled" in str(e).lower() and attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                time.sleep(wait_time)
            else:
                raise

Status: Documented behavior, no fix planned

References

Related Skills

streamlit-snowflake - Streamlit in Snowflake apps

snowflake-platform

Snowflake Platform Skill

Quick Start

Install Snowflake CLI

Configure Connection