jonas
71803418e5
Shelly device management app with mDNS/subnet discovery, inventory, configuration, and mass operations for Gen1/Gen2+ devices. Includes .gitignore excluding runtime data (device DB, user config), AI conversation history, build artifacts, and common Python/OS patterns.
528 lines
19 KiB
Python
528 lines
19 KiB
Python
"""
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Compute/Resource Dashboard Template (Snowflake Edition)
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A resource consumption dashboard demonstrating:
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- Snowflake connection via st.connection("snowflake")
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- Parameterized queries for safe data loading
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- Multiple metric cards in a grid layout
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- @st.fragment for independent widget updates
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- Popover filters for each metric card
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- Chart/table view toggle
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- Time range filtering (1M, 6M, 1Y, QTD, YTD, All)
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This template uses synthetic data generated in Snowflake. Replace the
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synthetic queries with your actual table queries in production.
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"""
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from datetime import date, timedelta
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import re
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import pandas as pd
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import streamlit as st
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import altair as alt
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st.set_page_config(
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page_title="Compute Dashboard (Snowflake)",
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page_icon=":material/bolt:",
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layout="wide",
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)
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# =============================================================================
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# Constants
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# =============================================================================
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TIME_RANGES = ["1M", "6M", "1Y", "QTD", "YTD", "All"]
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ACCOUNT_TYPES = ["Paying", "Trial", "Internal"]
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INSTANCE_TYPES = ["Standard", "High Memory", "High CPU", "GPU"]
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REGIONS = ["us-west-2", "us-east-1", "eu-west-1", "ap-northeast-1"]
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CHART_HEIGHT = 350
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# Base values for synthetic data generation
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BASE_VALUES = {
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"account_type": {"Paying": 8000, "Trial": 2000, "Internal": 1000},
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"instance_type": {"Standard": 5000, "High Memory": 3000, "High CPU": 2000, "GPU": 1500},
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"region": {"us-west-2": 4000, "us-east-1": 3500, "eu-west-1": 2500, "ap-northeast-1": 1500},
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}
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# =============================================================================
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# Snowflake Connection and Data Loading
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# =============================================================================
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def get_snowflake_connection():
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"""Get Snowflake connection via st.connection.
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Displays an error and stops the app if the connection fails.
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"""
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try:
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return st.connection("snowflake")
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except Exception as e:
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st.error(f"Failed to connect to Snowflake: {e}")
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st.info(
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"Make sure you have configured your Snowflake connection in "
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"`.streamlit/secrets.toml` or via environment variables."
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)
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st.stop()
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# =============================================================================
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# IMPORTANT: Use parameterized queries in production
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# =============================================================================
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#
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# This demo uses synthetic data generated via SQL. In production, always use
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# parameterized queries to prevent SQL injection:
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#
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# # GOOD: Parameterized query (safe)
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# conn = st.connection("snowflake")
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# df = conn.query(
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# "SELECT * FROM metrics WHERE category = :category AND ds >= :start_date",
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# params={"category": selected_category, "start_date": start_date}
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# )
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#
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# # BAD: f-string interpolation (SQL injection risk)
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# df = conn.query(f"SELECT * FROM metrics WHERE category = '{user_input}'")
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#
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# The synthetic data generation below uses f-strings only because the values
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# are hardcoded constants, not user input. Never use f-strings with user input.
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def _validate_sql_identifier(name: str) -> str:
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"""Validate that a string is a safe SQL identifier (letters, digits, underscores).
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Raises ValueError if the name contains unexpected characters. This prevents
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SQL injection if the function is ever modified to accept dynamic input.
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"""
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if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", name):
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raise ValueError(f"Invalid SQL identifier: {name!r}")
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return name
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def build_synthetic_query(category_col: str, categories: list[str], base_values: dict[str, int]) -> str:
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"""Build SQL query for synthetic data.
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WARNING: This function uses f-strings for demo purposes only.
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The categories are hardcoded constants defined in this file, not user input.
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In production, always use parameterized queries with conn.query(..., params={}).
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"""
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# Validate the column name used as a SQL identifier (appears unquoted in SQL)
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_validate_sql_identifier(category_col)
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# Category values appear as string literals in SQL VALUES clause.
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# Escape single quotes to prevent SQL injection.
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safe_categories = [cat.replace("'", "''") for cat in categories]
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# Build VALUES clause for categories with their base values
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values_rows = ", ".join(
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f"('{cat}', {base_values.get(orig, 1000)})"
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for cat, orig in zip(safe_categories, categories)
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)
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return f"""
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WITH categories AS (
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SELECT column1 AS category, column2 AS base_val
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FROM VALUES {values_rows}
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),
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date_series AS (
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SELECT DATEADD(day, -seq4(), CURRENT_DATE() - 1) AS ds
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FROM TABLE(GENERATOR(ROWCOUNT => 730))
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),
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base_data AS (
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SELECT
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ds,
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category,
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base_val * POWER(1.002, DATEDIFF(day, DATEADD(year, -2, CURRENT_DATE()), ds)) AS base_trend,
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CASE WHEN DAYOFWEEK(ds) IN (0, 6) THEN 0.4 ELSE 1.0 END AS seasonality,
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1 + (RANDOM() / 10000000000000000000.0 - 0.5) * 0.4 AS noise
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FROM date_series
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CROSS JOIN categories
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WHERE ds >= DATEADD(year, -2, CURRENT_DATE())
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)
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SELECT
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ds,
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category AS {category_col},
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GREATEST(0, ROUND(base_trend * seasonality * noise, 2)) AS daily_credits,
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ROUND(AVG(GREATEST(0, base_trend * seasonality * noise)) OVER (
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PARTITION BY category
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ORDER BY ds ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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), 2) AS credits_7d_ma
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FROM base_data
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ORDER BY ds, {category_col}
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"""
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@st.cache_data(ttl=3600, show_spinner="Loading account type data...")
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def load_account_type_data() -> pd.DataFrame:
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"""Load credits by account type from Snowflake."""
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conn = get_snowflake_connection()
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query = build_synthetic_query("account_type", ACCOUNT_TYPES, BASE_VALUES["account_type"])
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df = conn.query(query)
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df.columns = df.columns.str.lower()
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return df
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@st.cache_data(ttl=3600, show_spinner="Loading instance type data...")
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def load_instance_type_data() -> pd.DataFrame:
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"""Load credits by instance type from Snowflake."""
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conn = get_snowflake_connection()
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query = build_synthetic_query("instance_type", INSTANCE_TYPES, BASE_VALUES["instance_type"])
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df = conn.query(query)
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df.columns = df.columns.str.lower()
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return df
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@st.cache_data(ttl=3600, show_spinner="Loading region data...")
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def load_region_data() -> pd.DataFrame:
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"""Load credits by region from Snowflake."""
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conn = get_snowflake_connection()
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query = build_synthetic_query("region", REGIONS, BASE_VALUES["region"])
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df = conn.query(query)
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df.columns = df.columns.str.lower()
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return df
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# =============================================================================
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# Chart Utilities
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# =============================================================================
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def filter_by_time_range(df: pd.DataFrame, x_col: str, time_range: str) -> pd.DataFrame:
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"""Filter dataframe by time range."""
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if time_range == "All" or df.empty:
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return df
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df = df.copy()
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df[x_col] = pd.to_datetime(df[x_col])
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max_date = df[x_col].max()
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if time_range == "1M":
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min_date = max_date - timedelta(days=30)
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elif time_range == "6M":
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min_date = max_date - timedelta(days=180)
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elif time_range == "1Y":
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min_date = max_date - timedelta(days=365)
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elif time_range == "QTD":
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quarter_month = ((max_date.month - 1) // 3) * 3 + 1
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min_date = pd.Timestamp(date(max_date.year, quarter_month, 1))
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elif time_range == "YTD":
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min_date = pd.Timestamp(date(max_date.year, 1, 1))
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else:
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return df
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return df[df[x_col] >= min_date]
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def create_line_chart(
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df: pd.DataFrame,
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x_col: str,
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y_col: str,
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color_col: str,
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height: int,
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show_percent: bool = False,
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) -> alt.Chart:
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"""Create a line chart."""
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y_format = ".1%" if show_percent else ",.0f"
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return (
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alt.Chart(df)
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.mark_line()
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.encode(
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x=alt.X(f"{x_col}:T", title=None),
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y=alt.Y(f"{y_col}:Q", title="Credits", axis=alt.Axis(format=y_format)),
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color=alt.Color(f"{color_col}:N", legend=alt.Legend(orient="bottom")),
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tooltip=[
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alt.Tooltip(f"{x_col}:T", title="Date", format="%Y-%m-%d"),
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alt.Tooltip(f"{color_col}:N", title=color_col.replace("_", " ").title()),
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alt.Tooltip(f"{y_col}:Q", title="Credits", format=y_format),
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],
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)
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.properties(height=height)
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.interactive()
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)
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def create_bar_chart(
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df: pd.DataFrame,
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x_col: str,
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y_col: str,
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color_col: str,
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height: int,
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show_percent: bool = False,
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) -> alt.Chart:
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"""Create a stacked bar chart."""
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y_format = ".1%" if show_percent else ",.0f"
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return (
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alt.Chart(df)
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.mark_bar()
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.encode(
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x=alt.X(f"{x_col}:T", title=None),
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y=alt.Y(
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f"{y_col}:Q",
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title="Credits",
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stack="normalize" if show_percent else True,
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axis=alt.Axis(format=y_format),
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),
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color=alt.Color(f"{color_col}:N", legend=alt.Legend(orient="bottom")),
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tooltip=[
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alt.Tooltip(f"{x_col}:T", title="Date", format="%Y-%m-%d"),
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alt.Tooltip(f"{color_col}:N"),
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alt.Tooltip(f"{y_col}:Q", format=",.0f"),
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],
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)
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.properties(height=height)
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)
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# =============================================================================
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# Page Header Component
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# =============================================================================
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def render_page_header(title: str):
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"""Render page header with title and reset button."""
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with st.container(
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horizontal=True, horizontal_alignment="distribute", vertical_alignment="center"
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):
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st.markdown(title)
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if st.button(":material/restart_alt: Reset", type="tertiary"):
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st.session_state.clear()
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st.rerun()
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# =============================================================================
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# Metric Card Components (using @st.fragment)
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# =============================================================================
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@st.fragment
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def account_type_metric():
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"""Account type metric card with independent state."""
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data = load_account_type_data()
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with st.container(border=True):
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with st.container(horizontal=True, horizontal_alignment="distribute", vertical_alignment="center"):
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st.markdown("**Credits by account type**")
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view_mode = st.segmented_control(
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"View",
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options=[":material/show_chart:", ":material/table:"],
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default=":material/show_chart:",
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key="acct_view",
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label_visibility="collapsed",
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)
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with st.popover("Filters", type="tertiary"):
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selected_types = st.pills(
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"Account types",
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options=ACCOUNT_TYPES,
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default=["Paying"],
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selection_mode="multi",
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key="acct_types",
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)
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line_options = st.pills(
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"Lines",
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options=["Daily", "7-day MA"],
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default=["7-day MA"],
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selection_mode="multi",
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key="acct_lines",
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)
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chart_type = st.segmented_control(
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"Chart type",
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options=[":material/show_chart: Line", ":material/bar_chart: Bar"],
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default=":material/show_chart: Line",
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key="acct_chart",
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)
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show_percent = st.toggle(
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"Show %", value=False, key="acct_pct",
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disabled="Line" in (chart_type or ""),
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)
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time_range = st.segmented_control(
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"Time range",
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options=TIME_RANGES,
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default="All",
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key="acct_time",
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)
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# Filter data
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selected_types = selected_types or ["Paying"]
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line_options = line_options or ["7-day MA"]
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filtered = data[data["account_type"].isin(selected_types)]
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filtered = filter_by_time_range(filtered, "ds", time_range)
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y_col = "credits_7d_ma" if "7-day MA" in line_options else "daily_credits"
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if "table" in (view_mode or ""):
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st.dataframe(filtered, height=CHART_HEIGHT, hide_index=True)
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else:
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if "Bar" in (chart_type or ""):
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st.altair_chart(
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create_bar_chart(filtered, "ds", y_col, "account_type", CHART_HEIGHT, show_percent),
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)
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else:
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st.altair_chart(
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create_line_chart(filtered, "ds", y_col, "account_type", CHART_HEIGHT),
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)
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@st.fragment
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def instance_type_metric():
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"""Instance type metric card with independent state."""
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data = load_instance_type_data()
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with st.container(border=True):
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with st.container(horizontal=True, horizontal_alignment="distribute", vertical_alignment="center"):
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st.markdown("**Credits by instance type**")
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view_mode = st.segmented_control(
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"View",
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options=[":material/show_chart:", ":material/table:"],
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default=":material/show_chart:",
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key="inst_view",
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label_visibility="collapsed",
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)
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with st.popover("Filters", type="tertiary"):
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selected_types = st.pills(
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"Instance types",
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options=INSTANCE_TYPES,
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default=INSTANCE_TYPES,
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selection_mode="multi",
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key="inst_types",
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)
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line_options = st.pills(
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"Lines",
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options=["Daily", "7-day MA"],
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default=["7-day MA"],
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selection_mode="multi",
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key="inst_lines",
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)
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chart_type = st.segmented_control(
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"Chart type",
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options=[":material/show_chart: Line", ":material/bar_chart: Bar"],
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default=":material/show_chart: Line",
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key="inst_chart",
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)
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show_percent = st.toggle(
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"Show %", value=False, key="inst_pct",
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disabled="Line" in (chart_type or ""),
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)
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time_range = st.segmented_control(
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"Time range",
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options=TIME_RANGES,
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default="All",
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key="inst_time",
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)
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# Filter data
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selected_types = selected_types or INSTANCE_TYPES
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line_options = line_options or ["7-day MA"]
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filtered = data[data["instance_type"].isin(selected_types)]
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filtered = filter_by_time_range(filtered, "ds", time_range)
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y_col = "credits_7d_ma" if "7-day MA" in line_options else "daily_credits"
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if "table" in (view_mode or ""):
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st.dataframe(filtered, height=CHART_HEIGHT, hide_index=True)
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else:
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if "Bar" in (chart_type or ""):
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st.altair_chart(
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create_bar_chart(filtered, "ds", y_col, "instance_type", CHART_HEIGHT, show_percent),
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)
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else:
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st.altair_chart(
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create_line_chart(filtered, "ds", y_col, "instance_type", CHART_HEIGHT),
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)
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@st.fragment
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def region_metric():
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"""Region metric card with independent state."""
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data = load_region_data()
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with st.container(border=True):
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with st.container(horizontal=True, horizontal_alignment="distribute", vertical_alignment="center"):
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st.markdown("**Credits by region**")
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view_mode = st.segmented_control(
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"View",
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options=[":material/show_chart:", ":material/table:"],
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default=":material/show_chart:",
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key="region_view",
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label_visibility="collapsed",
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)
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with st.popover("Filters", type="tertiary"):
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selected_regions = st.pills(
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"Regions",
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options=REGIONS,
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default=REGIONS,
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selection_mode="multi",
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key="region_select",
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)
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line_options = st.pills(
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"Lines",
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options=["Daily", "7-day MA"],
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default=["7-day MA"],
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selection_mode="multi",
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key="region_lines",
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)
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chart_type = st.segmented_control(
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"Chart type",
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options=[":material/show_chart: Line", ":material/bar_chart: Bar"],
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default=":material/bar_chart: Bar",
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key="region_chart",
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)
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show_percent = st.toggle(
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"Show %", value=False, key="region_pct",
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disabled="Line" in (chart_type or ""),
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)
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time_range = st.segmented_control(
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"Time range",
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options=TIME_RANGES,
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default="All",
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key="region_time",
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)
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# Filter data
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selected_regions = selected_regions or REGIONS
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line_options = line_options or ["7-day MA"]
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filtered = data[data["region"].isin(selected_regions)]
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filtered = filter_by_time_range(filtered, "ds", time_range)
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y_col = "credits_7d_ma" if "7-day MA" in line_options else "daily_credits"
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if "table" in (view_mode or ""):
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st.dataframe(filtered, height=CHART_HEIGHT, hide_index=True)
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else:
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if "Bar" in (chart_type or ""):
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st.altair_chart(
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create_bar_chart(filtered, "ds", y_col, "region", CHART_HEIGHT, show_percent),
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)
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else:
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st.altair_chart(
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create_line_chart(filtered, "ds", y_col, "region", CHART_HEIGHT),
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)
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# =============================================================================
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# Page Layout
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|
# =============================================================================
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|
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# Check Snowflake connection
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get_snowflake_connection()
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|
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render_page_header("# :material/bolt: Compute Dashboard")
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|
st.caption(":material/cloud: Powered by Snowflake")
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|
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|
# Row 1: Two metrics
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|
col1, col2 = st.columns(2)
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|
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|
with col1:
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|
account_type_metric()
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|
|
|
with col2:
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|
instance_type_metric()
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|
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|
# Row 2: One metric (full width for region breakdown)
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|
region_metric()
|