├── .gitignore
├── .streamlit
└── config.toml
├── Home.py
├── LICENSE
├── Pipfile
├── Pipfile.lock
├── README.md
├── pages
├── 01_🔹_Compute_insights.py
├── 02_🔹_Storage_insights.py
├── 03_🔹_Data_Transfer_insights.py
└── 04_🤔_About_this_app.py
└── utils
├── charts.py
├── gui.py
├── processing.py
├── snowflake_connector.py
└── sql.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Streamlit secrets
2 | .streamlit/secrets.toml
3 |
4 | .DS_Store
5 |
--------------------------------------------------------------------------------
/.streamlit/config.toml:
--------------------------------------------------------------------------------
1 | [runner]
2 | fastReruns = true
3 |
4 | [theme]
5 | primaryColor = "#1c83e1"
6 |
--------------------------------------------------------------------------------
/Home.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 |
3 | st.set_page_config(page_title="Usage Insights app", page_icon="🌀", layout="centered")
4 |
5 | from utils import gui
6 |
7 | gui.icon("🌀")
8 |
9 | # Make sure session state is preserved
10 | for key in st.session_state:
11 | st.session_state[key] = st.session_state[key]
12 |
13 | st.title("Welcome to the Usage Insights app!")
14 | st.sidebar.text(f"Account: {st.secrets.sf_usage_app.account}")
15 | st.sidebar.info("Choose a page!")
16 | st.markdown(
17 | """
18 | This app provides insights on a demo Snowflake account usage.
19 |
20 | ### Get started!
21 |
22 | 👈 Select a page in the sidebar!
23 | """
24 | )
25 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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/Pipfile:
--------------------------------------------------------------------------------
1 | [[source]]
2 | url = "https://pypi.org/simple"
3 | verify_ssl = true
4 | name = "pypi"
5 |
6 | [requires]
7 | python_version = "3.9"
8 |
9 | [packages]
10 | streamlit = ">=1.10.0" # Requires multipage app
11 | requests = "*"
12 | matplotlib = "*"
13 | millify = "*"
14 | plost = {editable = true, ref = "streamlit-style", git = "https://github.com/jrieke/plost"} # Docs https://share.streamlit.io/jrieke/plost/streamlit-style/
15 | numpy = "*"
16 | snowflake-connector-python = ">=2.7.8"
17 | sqlparse = "*"
18 |
19 | [dev-packages]
20 | black = "*" # Pretty formatting of Python code
21 | pynvim = "*" # Allows nvim users to use black formatting
22 | pre-commit = "*"
23 | flake8 = "*"
24 | flake8-bugbear = "*"
25 | typed-ast = ">=1.5.0"
26 | mypy = "*"
27 |
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | > [!NOTE]
2 | > Discover many more Streamlit & Snowflake apps in the [snowflake-demo-streamlit](https://github.com/Snowflake-Labs/snowflake-demo-streamlit) repository
3 |
4 | ---
5 |
6 | # Streamlit demo: 🌀 The Snowflake Usage Insights app
7 |
8 | This app provides insights on a demo Snowflake account usage.
9 |
10 | It was made using :balloon: [Streamlit](https://www.streamlit.io) and the :snowflake: [Snowflake Python connector](https://github.com/snowflakedb/snowflake-connector-python)!
11 |
12 | 
13 |
14 | ### Can I use this app with my own Snowflake account?
15 |
16 | This demo app currently only supports basic username / password authentication
17 | to connect to your Snowflake account. For security purposes, we recommend cloning this app and running it **locally** instead of on Streamlit Cloud. If you're interested, head over to the next section!
18 |
19 | ### Run this app locally
20 | Follow these steps:
21 |
22 | 1. **Set up dependencies.** Get [Pipenv](https://pipenv-fork.readthedocs.io/en/latest/install.html#pragmatic-installation-of-pipenv),
23 | clone/fork this repository and within the directory, run:
24 | ```
25 | pipenv clean && pipenv install --python 3.9
26 | ```
27 |
28 | 2. **Set up credentials.** Create a file `.streamlit/secrets.toml` and fill in your Snowflake account
29 | credentials. The file should look like this:
30 | ```
31 | [sf_usage_app]
32 | user = "..."
33 | account = "..."
34 | password = "..."
35 | warehouse = "..."
36 | ```
37 |
38 | 3. **Run the app locally.** Simply run:
39 | ```
40 | pipenv run streamlit run Home.py
41 | ```
42 |
43 | 🎊 Your browser should now be opened with the Streamlit app running locally!
44 |
45 | ### Contribute
46 |
47 | Feel free to contribute! Simply make sure to:
48 | 1. Install development dependencies `pipenv install --python 3.9 --dev`
49 | 2. Set up pre-commit hooks `pipenv run pre-commit install`
50 |
51 | ### Questions? Comments?
52 |
53 | Please ask in the [Streamlit community](https://discuss.streamlit.io).
54 |
--------------------------------------------------------------------------------
/pages/01_🔹_Compute_insights.py:
--------------------------------------------------------------------------------
1 | import plost
2 | import streamlit as st
3 |
4 | st.set_page_config(
5 | page_title="Usage Insights app - Compute", page_icon="🔹", layout="centered"
6 | )
7 |
8 | from utils import charts, gui, processing
9 | from utils import snowflake_connector as sf
10 | from utils import sql as sql
11 |
12 |
13 | def main():
14 |
15 | # Date selector widget
16 | with st.sidebar:
17 | date_from, date_to = gui.date_selector()
18 |
19 | # Header
20 | gui.icon("🔹")
21 | st.title("Compute insights")
22 |
23 | # ----------------------
24 | # ---- Service type ----
25 | # ----------------------
26 |
27 | gui.space(1)
28 | st.subheader("Service type")
29 |
30 | # Get data
31 | query = sql.CONSUMPTION_PER_SERVICE_TYPE_QUERY
32 | df = sf.sql_to_dataframe(
33 | query.format(date_from=date_from, date_to=date_to)
34 | )
35 |
36 | # Add filtering widget per Service type
37 | all_values = df["SERVICE_TYPE"].unique().tolist()
38 | selected_value = st.selectbox(
39 | "Choose service type",
40 | ["All"] + all_values,
41 | 0,
42 | )
43 |
44 | if selected_value == "All":
45 | selected_value = all_values
46 | else:
47 | selected_value = [selected_value]
48 |
49 | # Filter df accordingly
50 | df = df[df["SERVICE_TYPE"].isin(selected_value)]
51 |
52 | # Get consumption
53 | consumption = int(df["CREDITS_USED"].sum())
54 |
55 | if df.empty:
56 | st.caption("No data found.")
57 | elif consumption == 0:
58 | st.caption("No consumption found.")
59 | else:
60 | # Sum of credits used
61 | credits_used_html = gui.underline(
62 | text=gui.pretty_print_credits(consumption),
63 | )
64 | credits_used_html += " were used"
65 |
66 | gui.space(1)
67 | st.write(credits_used_html, unsafe_allow_html=True)
68 |
69 | gui.space(1)
70 | gui.subsubheader(
71 | "**Compute** spend over time",
72 | "Aggregated by day",
73 | )
74 |
75 | # Resample by day
76 | df_resampled = processing.resample_by_day(
77 | df,
78 | date_column="START_TIME",
79 | )
80 |
81 | # Bar chart
82 | bar_chart = charts.get_bar_chart(
83 | df=df_resampled,
84 | date_column="START_TIME",
85 | value_column="CREDITS_USED",
86 | )
87 |
88 | st.altair_chart(bar_chart, use_container_width=True)
89 |
90 | # Group by
91 | agg_config = {"CREDITS_USED": "sum"}
92 | df_grouped = (
93 | df.groupby(["NAME", "SERVICE_TYPE"]).agg(agg_config).reset_index()
94 | )
95 |
96 | # Sort and pretty print credits
97 | df_grouped_top_10 = df_grouped.sort_values(
98 | by="CREDITS_USED", ascending=False
99 | ).head(10)
100 |
101 | df_grouped_top_10["CREDITS_USED"] = df_grouped_top_10[
102 | "CREDITS_USED"
103 | ].apply(gui.pretty_print_credits)
104 |
105 | gui.subsubheader(
106 | "**Compute** spend",
107 | " Grouped by NAME",
108 | "Top 10",
109 | )
110 |
111 | st.dataframe(
112 | gui.dataframe_with_podium(
113 | df_grouped_top_10,
114 | )[["NAME", "SERVICE_TYPE", "CREDITS_USED"]],
115 | width=600,
116 | )
117 |
118 | gui.space(1)
119 | gui.hbar()
120 |
121 | # -------------------
122 | # ---- Warehouse ----
123 | # -------------------
124 |
125 | st.subheader("Warehouse")
126 |
127 | # Get data
128 | warehouse_usage_hourly = sf.sql_to_dataframe(
129 | sql.WAREHOUSE_USAGE_HOURLY.format(
130 | date_from=date_from,
131 | date_to=date_to,
132 | )
133 | )
134 |
135 | # Add filtering widget per Warehouse name
136 | warehouses = warehouse_usage_hourly.WAREHOUSE_NAME.unique()
137 | selected_warehouse = st.selectbox(
138 | "Choose warehouse",
139 | warehouses.tolist(),
140 | )
141 |
142 | # Filter accordingly
143 | warehouse_usage_hourly_filtered = warehouse_usage_hourly[
144 | warehouse_usage_hourly.WAREHOUSE_NAME.eq(selected_warehouse)
145 | ]
146 |
147 | # Resample so that all the period has data (fill with 0 if needed)
148 | warehouse_usage_hourly_filtered = processing.resample_date_period(
149 | warehouse_usage_hourly_filtered,
150 | date_from,
151 | date_to,
152 | value_column="CREDITS_USED_COMPUTE",
153 | )
154 |
155 | gui.subsubheader("Time-histogram of **warehouse usage**")
156 |
157 | plost.time_hist(
158 | data=warehouse_usage_hourly_filtered,
159 | date="START_TIME",
160 | x_unit="day",
161 | y_unit="hours",
162 | color={
163 | "field": "CREDITS_USED_COMPUTE",
164 | "scale": {
165 | "scheme": charts.ALTAIR_SCHEME,
166 | },
167 | },
168 | aggregate=None,
169 | legend=None,
170 | )
171 |
172 | gui.space(1)
173 | gui.hbar()
174 |
175 | # -----------------
176 | # ---- Queries ----
177 | # -----------------
178 |
179 | st.subheader("Queries")
180 |
181 | # Get data
182 | queries_data = sf.get_queries_data(
183 | date_from,
184 | date_to,
185 | )
186 |
187 | # Add filtering widget per Warehouse name
188 | warehouses = queries_data.WAREHOUSE_NAME.dropna().unique().tolist()
189 | selected_warehouse = st.selectbox(
190 | "Choose warehouse",
191 | warehouses,
192 | )
193 |
194 | # Filter accordingly
195 | queries_data = queries_data[
196 | queries_data.WAREHOUSE_NAME.eq(selected_warehouse)
197 | ]
198 |
199 | gui.subsubheader(
200 | "Histogram of **queries duration** (in secs)", "Log scale"
201 | )
202 |
203 | # Histogram
204 | histogram = charts.get_histogram_chart(
205 | df=queries_data,
206 | date_column="DURATION_SECS",
207 | )
208 |
209 | st.altair_chart(histogram, use_container_width=True)
210 |
211 | # Top-3 longest queries
212 | queries_podium_df = gui.dataframe_with_podium(
213 | queries_data, "DURATION_SECS"
214 | ).head(3)
215 |
216 | # Only show if at least three queries!
217 | if len(queries_podium_df) >= 3:
218 | with st.expander("🔎 Zoom into top-3 longest queries in detail"):
219 | for query in queries_podium_df.itertuples():
220 | st.caption(f"{query.Index} {query.DURATION_SECS_PP}")
221 | st.code(query.QUERY_TEXT_PP, "sql")
222 |
223 | gui.space(1)
224 | st.write("Time-histograms of **aggregate queries duration** (in secs)")
225 |
226 | # Resample so that all the period has data (fill with 0 if needed)
227 | queries_data = processing.resample_date_period(
228 | queries_data, date_from, date_to, "DURATION_SECS"
229 | )
230 |
231 | num_days_selected = (date_to - date_from).days
232 | if num_days_selected > 14:
233 | st.caption("Week-day histogram")
234 | plost.time_hist(
235 | data=queries_data,
236 | date="START_TIME",
237 | x_unit="week",
238 | y_unit="day",
239 | color={
240 | "field": "DURATION_SECS",
241 | "scale": {"scheme": charts.ALTAIR_SCHEME},
242 | },
243 | aggregate="sum",
244 | legend=None,
245 | )
246 |
247 | st.caption("Day-hour histogram")
248 | plost.time_hist(
249 | data=queries_data,
250 | date="START_TIME",
251 | x_unit="day",
252 | y_unit="hours",
253 | color={
254 | "field": "DURATION_SECS",
255 | "scale": {"scheme": charts.ALTAIR_SCHEME},
256 | },
257 | aggregate="sum",
258 | legend=None,
259 | )
260 |
261 | gui.space(1)
262 | gui.subsubheader(
263 | "**Query optimization**: longest and most frequent queries",
264 | "Log scales (🖱️ hover for real values!)",
265 | )
266 |
267 | queries_agg = sf.sql_to_dataframe(
268 | sql.QUERIES_COUNT_QUERY.format(
269 | date_from=date_from,
270 | date_to=date_to,
271 | num_min=1,
272 | limit=10_000,
273 | warehouse_name=selected_warehouse,
274 | )
275 | )
276 |
277 | queries_agg = processing.apply_log1p(
278 | df=queries_agg, columns=["EXECUTION_MINUTES", "NUMBER_OF_QUERIES"]
279 | )
280 |
281 | scatter_chart = charts.get_scatter_chart(df=queries_agg)
282 |
283 | st.altair_chart(
284 | scatter_chart,
285 | use_container_width=True,
286 | )
287 |
288 | gui.space(1)
289 | gui.hbar()
290 |
291 | # -------------
292 | # --- Users ---
293 | # -------------
294 |
295 | st.subheader("Users")
296 |
297 | # Get data
298 | users_data = sf.sql_to_dataframe(
299 | sql.USERS_QUERY.format(
300 | date_from=date_from,
301 | date_to=date_to,
302 | )
303 | )
304 |
305 | gui.subsubheader("**Users** with the **largest** number of credits spent")
306 |
307 | # Bar chart
308 | plost.bar_chart(
309 | data=users_data,
310 | bar="USER_NAME",
311 | value="APPROXIMATE_CREDITS_USED",
312 | color=gui.BLUE_COLOR,
313 | direction="horizontal",
314 | height=200,
315 | use_container_width=True,
316 | )
317 |
318 |
319 | if __name__ == "__main__":
320 | main()
321 |
--------------------------------------------------------------------------------
/pages/02_🔹_Storage_insights.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 |
3 | st.set_page_config(
4 | page_title="Usage Insights app - Storage", page_icon="🔹", layout="centered"
5 | )
6 |
7 | from utils import charts, gui, processing
8 | from utils import snowflake_connector as sf
9 | from utils import sql
10 |
11 |
12 | def main():
13 |
14 | # Date selector widget
15 | with st.sidebar:
16 | date_from, date_to = gui.date_selector()
17 |
18 | # Header
19 | gui.icon("🔹")
20 | st.title("Storage insights")
21 |
22 | # Get data
23 | query = sql.STORAGE_QUERY
24 | df = sf.sql_to_dataframe(
25 | query.format(date_from=date_from, date_to=date_to)
26 | )
27 |
28 | # Get consumption
29 | consumption = df["DATABASE_BYTES"].sum()
30 |
31 | if df.empty:
32 | st.caption("No data found.")
33 | st.stop()
34 | if consumption == 0:
35 | st.caption("No consumption!")
36 | st.stop()
37 |
38 | # Resample by day
39 | df_resampled = processing.resample_by_day(
40 | df,
41 | date_column="USAGE_DATE",
42 | )
43 |
44 | # Average bytes used per day
45 | credits_used_html = "Average "
46 | credits_used_html += gui.underline(
47 | text=gui.pretty_print_bytes(int(df_resampled.DATABASE_BYTES.mean())),
48 | color=gui.BLUE_COLOR,
49 | )
50 | credits_used_html += " were used per day"
51 |
52 | gui.space(1)
53 | st.write(credits_used_html, unsafe_allow_html=True)
54 |
55 | gui.space(1)
56 | gui.subsubheader(
57 | "**Storage** spend over time",
58 | "Aggregated by day",
59 | )
60 |
61 | # Bar chart
62 | chart = charts.get_bar_chart(
63 | df=df_resampled,
64 | date_column="USAGE_DATE",
65 | value_column="DATABASE_BYTES",
66 | )
67 |
68 | st.altair_chart(chart, use_container_width=True)
69 |
70 | # st.write(
71 | # df_resampled.groupby(
72 | # ["OBJECT_NAME", "USAGE_DATE"]
73 | # ).DATABASE_BYTES.mean()
74 | # )
75 |
76 | # Group by
77 | df_grouped = (
78 | df.groupby(["OBJECT_NAME", "USAGE_DATE"])
79 | .DATABASE_BYTES.mean()
80 | .reset_index()
81 | .groupby("OBJECT_NAME")
82 | .mean()
83 | .reset_index()
84 | )
85 | # agg_config = {"DATABASE_BYTES": "sum"}
86 | # df_grouped = df.groupby("OBJECT_NAME").agg(agg_config).reset_index()
87 |
88 | # Sort and pretty print credits
89 | df_grouped_top_10 = df_grouped.sort_values(
90 | by="DATABASE_BYTES", ascending=False
91 | ).head(10)
92 |
93 | df_grouped_top_10["AVG_DAILY_STORAGE_SIZE"] = df_grouped_top_10[
94 | "DATABASE_BYTES"
95 | ].apply(gui.pretty_print_bytes)
96 |
97 | # Pretty print BYTES
98 | # value_pretty_print_column = "DATABASE_BYTES_PP"
99 | # df_grouped[value_pretty_print_column] = df_grouped["DATABASE_BYTES"].apply(
100 | # gui.pretty_print_bytes
101 | # )
102 | # df_grouped["AVG_DAILY_STORAGE_SIZE"] = df_grouped[
103 | # "DATABASE_BYTES_PP"
104 | # ].apply(lambda x: x + " " * 10)
105 |
106 | # df_grouped = df_grouped.sort_values(
107 | # by="DATABASE_BYTES",
108 | # ascending=False,
109 | # )
110 |
111 | gui.subsubheader(
112 | "**Storage** spend per day",
113 | "Average",
114 | "Grouped by OBJECT_NAME",
115 | "Top 10",
116 | )
117 |
118 | st.dataframe(
119 | gui.dataframe_with_podium(
120 | df_grouped_top_10[["OBJECT_NAME", "AVG_DAILY_STORAGE_SIZE"]],
121 | ),
122 | )
123 |
124 |
125 | if __name__ == "__main__":
126 | main()
127 |
--------------------------------------------------------------------------------
/pages/03_🔹_Data_Transfer_insights.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 |
3 | st.set_page_config(
4 | page_title="Usage Insights app - Data Transfer",
5 | page_icon="🔹",
6 | layout="centered",
7 | )
8 |
9 | from utils import charts, gui, processing
10 | from utils import snowflake_connector as sf
11 | from utils import sql
12 |
13 |
14 | def main():
15 |
16 | # Date selector widget
17 | with st.sidebar:
18 | date_from, date_to = gui.date_selector()
19 |
20 | # Header
21 | gui.icon("🔹")
22 | st.title("Data Transfer insights")
23 |
24 | # Get data
25 | query = sql.DATA_TRANSFER_QUERY
26 | df = sf.sql_to_dataframe(
27 | query.format(
28 | date_from=date_from,
29 | date_to=date_to,
30 | )
31 | )
32 |
33 | gui.space(1)
34 | st.subheader("Target region")
35 |
36 | # Add filtering widget
37 | all_values = df["TARGET_REGION"].unique().tolist()
38 | selected_value = st.selectbox(
39 | "Choose target region",
40 | ["All"] + all_values,
41 | 0,
42 | )
43 |
44 | if selected_value == "All":
45 | selected_value = all_values
46 | else:
47 | selected_value = [selected_value]
48 |
49 | # Filter df accordingly
50 | df = df[df["TARGET_REGION"].isin(selected_value)]
51 |
52 | # Get consumption
53 | consumption = int(df["BYTES_TRANSFERRED"].sum())
54 |
55 | if df.empty:
56 | st.caption("No data found.")
57 | st.stop()
58 | if consumption == 0:
59 | st.caption("No consumption!")
60 | st.stop()
61 |
62 | # Sum of credits used
63 | credits_used_html = gui.underline(
64 | text=gui.pretty_print_bytes(consumption),
65 | color=gui.BLUE_COLOR,
66 | )
67 | credits_used_html += " were used"
68 |
69 | gui.space(1)
70 | st.write(credits_used_html, unsafe_allow_html=True)
71 |
72 | gui.space(1)
73 | gui.subsubheader(
74 | "**Data Transfer** spend over time",
75 | "Aggregated by day",
76 | )
77 |
78 | # Resample by day
79 | df_resampled = processing.resample_by_day(
80 | df,
81 | date_column="START_TIME",
82 | )
83 |
84 | # Bar chart
85 | chart = charts.get_bar_chart(
86 | df=df_resampled,
87 | date_column="START_TIME",
88 | value_column="BYTES_TRANSFERRED",
89 | )
90 |
91 | st.altair_chart(chart, use_container_width=True)
92 |
93 | # Group by
94 | agg_config = {"BYTES_TRANSFERRED": "sum"}
95 | df_grouped = (
96 | df.groupby(["TRANSFER_TYPE", "TARGET_CLOUD", "TARGET_REGION"])
97 | .agg(agg_config)
98 | .reset_index()
99 | )
100 |
101 | # Sort and pretty print credits
102 | df_grouped_top_10 = df_grouped.sort_values(
103 | by="BYTES_TRANSFERRED", ascending=False
104 | ).head(10)
105 |
106 | df_grouped_top_10["BYTES_TRANSFERRED"] = df_grouped_top_10[
107 | "BYTES_TRANSFERRED"
108 | ].apply(gui.pretty_print_bytes)
109 |
110 | gui.subsubheader(
111 | "**Storage** spend",
112 | " Grouped by TRANSFER_TYPE",
113 | "Top 10",
114 | )
115 |
116 | st.dataframe(
117 | gui.dataframe_with_podium(
118 | df_grouped_top_10[
119 | [
120 | "TRANSFER_TYPE",
121 | "TARGET_CLOUD",
122 | "TARGET_REGION",
123 | "BYTES_TRANSFERRED",
124 | ]
125 | ]
126 | ),
127 | width=600,
128 | )
129 |
130 |
131 | if __name__ == "__main__":
132 | main()
133 |
--------------------------------------------------------------------------------
/pages/04_🤔_About_this_app.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 |
3 | st.set_page_config(
4 | page_title="Usage Insights app - About", page_icon="🤔", layout="centered"
5 | )
6 |
7 | from utils import gui
8 |
9 | gui.icon("🤔")
10 | st.title("About this app")
11 |
12 | st.write(
13 | """
14 | ### How does this app work?
15 |
16 | - Using [Streamlit](https://www.streamlit.io) and the [Snowflake Python
17 | connector](https://github.com/snowflakedb/snowflake-connector-python).
18 | - Check out our public repository 🐙
19 | [snowflake-usage-app](https://github.com/streamlit/snowflake-usage-app) to
20 | learn more about it!
21 |
22 | ### Questions? Comments?
23 |
24 | Please ask in the [Streamlit community](https://discuss.streamlit.io).
25 | """
26 | )
27 |
--------------------------------------------------------------------------------
/utils/charts.py:
--------------------------------------------------------------------------------
1 | import altair as alt
2 | import pandas as pd
3 | import streamlit as st
4 |
5 | from utils import gui
6 |
7 | ALTAIR_AXIS_CONFIG = dict(
8 | gridColor="#e6eaf1",
9 | tickColor="#e6eaf1",
10 | domainColor="#e6eaf1",
11 | labelColor="#828797",
12 | titleFontWeight="bold",
13 | )
14 |
15 | ALTAIR_SCHEME = "blues"
16 |
17 |
18 | @st.cache_data(ttl=60 * 60 * 12)
19 | def get_bar_chart(
20 | df: pd.DataFrame,
21 | date_column: str,
22 | value_column: str,
23 | color: str = gui.BLUE_COLOR,
24 | ) -> alt.Chart:
25 |
26 | config = {
27 | "x": alt.X(f"yearmonthdate({date_column})", title="Day"),
28 | "y": alt.Y(f"sum({value_column})", title="Consumption"),
29 | "tooltip": (date_column, value_column),
30 | }
31 |
32 | chart = (
33 | alt.Chart(df)
34 | .mark_bar()
35 | .encode(**config)
36 | .configure_mark(opacity=1, color=color)
37 | .configure_axis(**ALTAIR_AXIS_CONFIG)
38 | .interactive()
39 | )
40 |
41 | return chart
42 |
43 |
44 | @st.cache_data(ttl=60 * 60 * 12)
45 | def get_histogram_chart(
46 | df: pd.DataFrame,
47 | date_column: str,
48 | ) -> alt.Chart:
49 |
50 | chart = (
51 | alt.Chart(df)
52 | .mark_bar()
53 | .encode(
54 | x=alt.X(
55 | date_column,
56 | bin=alt.BinParams(
57 | maxbins=100,
58 | ),
59 | ),
60 | y=alt.Y(
61 | "count()",
62 | title="Count of Records",
63 | scale=alt.Scale(type="symlog"),
64 | ),
65 | tooltip=[date_column, "count()"],
66 | )
67 | .configure_mark(
68 | color=gui.BLUE_COLOR,
69 | )
70 | .configure_axis(**ALTAIR_AXIS_CONFIG)
71 | .interactive()
72 | )
73 |
74 | return chart
75 |
76 |
77 | @st.cache_data(ttl=60 * 60 * 12)
78 | def get_scatter_chart(
79 | df: pd.DataFrame,
80 | ) -> alt.Chart:
81 | chart = (
82 | alt.Chart(df)
83 | .mark_circle(
84 | size=200,
85 | )
86 | .encode(
87 | x=alt.X(
88 | "NUMBER_OF_QUERIES_LOG:Q",
89 | title="Number of queries (log scale)",
90 | scale=alt.Scale(
91 | domain=[
92 | df.NUMBER_OF_QUERIES_LOG.min() - 0.5,
93 | df.NUMBER_OF_QUERIES_LOG.max() + 0.5,
94 | ]
95 | ),
96 | ),
97 | y=alt.Y(
98 | "EXECUTION_MINUTES_LOG:Q",
99 | title="Execution minutes (log scale)",
100 | scale=alt.Scale(
101 | domain=[
102 | df.EXECUTION_MINUTES_LOG.min() - 0.5,
103 | df.EXECUTION_MINUTES_LOG.max() + 0.5,
104 | ]
105 | ),
106 | ),
107 | tooltip=[
108 | "QUERY_TEXT",
109 | "NUMBER_OF_QUERIES",
110 | "EXECUTION_MINUTES",
111 | ],
112 | )
113 | .configure_mark(opacity=0.7, color=gui.BLUE_COLOR)
114 | .configure_axis(**ALTAIR_AXIS_CONFIG)
115 | .interactive()
116 | )
117 |
118 | return chart
119 |
120 |
121 | if __name__ == "__main__":
122 | pass
123 |
--------------------------------------------------------------------------------
/utils/gui.py:
--------------------------------------------------------------------------------
1 | import datetime
2 | import math
3 |
4 | import pandas as pd
5 | import sqlparse
6 | import streamlit as st
7 | from millify import millify
8 |
9 | BLUE_COLOR = "#1c83e1"
10 |
11 |
12 | def icon(emoji: str):
13 | """Shows an emoji as a Notion-style page icon."""
14 | st.write(
15 | f'{emoji}',
16 | unsafe_allow_html=True,
17 | )
18 |
19 |
20 | def space(num_lines: int = 1):
21 | """Adds empty lines to the Streamlit app."""
22 | for _ in range(num_lines):
23 | st.write("")
24 |
25 |
26 | def hbar():
27 | """Adds a horizontal bar"""
28 | st.write("---")
29 |
30 |
31 | def subsubheader(*args):
32 | text = " · ".join(tuple(args))
33 | st.write(text)
34 |
35 |
36 | def underline(text: str, color: str = BLUE_COLOR) -> str:
37 | """Underlines input text using HTML"""
38 | style = "font-size:18px; text-decoration-style: dotted; "
39 | style += "text-underline-offset: 5px; "
40 | style += f"text-decoration-color:{color};"
41 | return f""" {text} """
42 |
43 |
44 | def dataframe_with_podium(
45 | df: pd.DataFrame, sort_by: str = None
46 | ) -> pd.DataFrame:
47 | """Replaces dataframe indices 1, 2, 3 with medals 🥇, 🥈, 🥉"""
48 |
49 | if sort_by:
50 | # Sort dataframe and take top-10
51 | sorted_df = (
52 | df.sort_values(by=sort_by, ascending=False)
53 | .reset_index(drop=True)
54 | .head(10)
55 | )
56 | else:
57 | sorted_df = df.head(10).copy()
58 |
59 | # Replace index to highlight the podium (gold, metal, bronze)
60 | new_index = ["🥇", "🥈", "🥉"] + list(map(str, range(4, 11)))
61 | sorted_df.index = new_index[: len(sorted_df)]
62 | return sorted_df
63 |
64 |
65 | def date_selector() -> tuple[datetime.date, datetime.date]:
66 | """Adds a date selector with a few different options."""
67 |
68 | DATE_RANGE_OPTIONS = [
69 | "Last 7 days",
70 | "Last 28 days",
71 | "Last 3 months",
72 | "Last 6 months",
73 | "Last 12 months",
74 | "All time",
75 | "Custom",
76 | ]
77 |
78 | if "date_range" in st.session_state:
79 | index = DATE_RANGE_OPTIONS.index(st.session_state.date_range)
80 | else:
81 | index = 0
82 |
83 | date_range = st.selectbox(
84 | "Date range",
85 | options=[
86 | "Last 7 days",
87 | "Last 28 days",
88 | "Last 3 months",
89 | "Last 6 months",
90 | "Last 12 months",
91 | "All time",
92 | "Custom",
93 | ],
94 | index=index,
95 | key="date_range",
96 | )
97 |
98 | if date_range != "Custom":
99 | date_to = datetime.date.today()
100 | if date_range == "Last 7 days":
101 | date_from = date_to - datetime.timedelta(days=7)
102 | elif date_range == "Last 28 days":
103 | date_from = date_to - datetime.timedelta(days=28)
104 | elif date_range == "Last 3 months":
105 | date_from = date_to - datetime.timedelta(weeks=12)
106 | elif date_range == "Last 6 months":
107 | date_from = date_to - datetime.timedelta(weeks=24)
108 | elif date_range == "Last 12 months":
109 | date_from = date_to - datetime.timedelta(days=365)
110 | else:
111 | date_from = datetime.date(year=2016, month=1, day=1)
112 |
113 | if "custom" in st.session_state:
114 | value = st.session_state.custom
115 | else:
116 | value = (
117 | date_to - datetime.timedelta(days=7),
118 | date_to,
119 | )
120 |
121 | if date_range == "Custom":
122 | date_from, date_to = st.date_input(
123 | "Choose start and end date",
124 | value=value,
125 | key="custom",
126 | )
127 |
128 | st.caption(f"Your selection is from **{date_from}** to **{date_to}**")
129 |
130 | return date_from, date_to
131 |
132 |
133 | def pretty_print_credits(credits: float) -> str:
134 | return f"{millify(credits, 1)} Credits"
135 |
136 |
137 | def pretty_print_bytes(size_bytes: int, binary: bool = False) -> str:
138 | basis = 1024 if binary else 1000
139 | if size_bytes == 0:
140 | return "0B"
141 | size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
142 | i = int(math.floor(math.log(size_bytes, basis)))
143 | p = math.pow(basis, i)
144 | s = round(size_bytes / p, 2)
145 | return "%s %s" % (s, size_name[i]) + " " * 8
146 |
147 |
148 | def pretty_print_seconds(seconds: int) -> str:
149 | minutes, seconds = divmod(seconds, 60)
150 | hours, minutes = divmod(minutes, 60)
151 |
152 | pretty_print = ""
153 | if hours:
154 | pretty_print += f"{hours} hr{'s' if hours > 1 else ''} "
155 | if minutes:
156 | pretty_print += f"{minutes} min{'s' if minutes > 1 else ''} "
157 | if seconds:
158 | pretty_print += f"{seconds} sec{'s' if seconds > 1 else ''}"
159 | return pretty_print
160 |
161 |
162 | def pretty_print_sql_query(query: str) -> str:
163 | return sqlparse.format(
164 | query,
165 | reindent=True,
166 | keyword_case="upper",
167 | )
168 |
169 |
170 | if __name__ == "__main__":
171 | pass
172 |
--------------------------------------------------------------------------------
/utils/processing.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import pandas as pd
3 | import streamlit as st
4 |
5 |
6 | @st.cache_data
7 | def resample_by_day(df: pd.DataFrame, date_column: str) -> pd.DataFrame:
8 | """Resample a dataframe's date_column by day, summing values."""
9 | return (
10 | df.set_index(date_column).resample("1D").sum().reset_index(date_column)
11 | )
12 |
13 |
14 | @st.cache_data
15 | def resample_date_period(
16 | df: pd.DataFrame, date_from: str, date_to: str, value_column: str
17 | ) -> pd.DataFrame:
18 | """Resample a dataframe to make sure there are values in value_column
19 | for the full date period from date_from to date_to."""
20 |
21 | empty_df = pd.DataFrame()
22 | empty_df["START_TIME"] = pd.date_range(date_from, date_to, freq="H")
23 | empty_df[value_column] = 0
24 | return empty_df.append(
25 | df,
26 | ignore_index=True,
27 | )
28 |
29 |
30 | @st.cache_data
31 | def apply_log1p(df: pd.DataFrame, columns: list) -> pd.DataFrame:
32 | """Apply log1p on input columns and store into new columns in input df
33 | with suffix _LOG."""
34 | log_columns = [f"{column}_LOG" for column in columns]
35 | df[log_columns] = df[columns].apply(np.log1p)
36 | return df
37 |
38 |
39 | if __name__ == "__main__":
40 | pass
41 |
--------------------------------------------------------------------------------
/utils/snowflake_connector.py:
--------------------------------------------------------------------------------
1 | import datetime
2 | from typing import Any, Dict
3 |
4 | import pandas as pd
5 | import streamlit as st
6 | from snowflake.connector import connect
7 | from snowflake.connector.connection import SnowflakeConnection
8 |
9 | from utils import gui, sql
10 |
11 | TIME_TO_LIVE = 60 * 60 * 6 # 6 hours caching
12 |
13 |
14 | # Share the connector across all users connected to the app
15 | @st.cache_resource
16 | def get_connector(
17 | secrets_key: str = "snowflake",
18 | input_params: Dict[str, Any] = None,
19 | use_browser=True,
20 | ) -> SnowflakeConnection:
21 | """Get a connector to Snowflake. By default, the connector will look
22 | for credentials found under st.secrets["snowflake"].
23 |
24 | Args:
25 | secrets_key (str, optional): Streamlit secrets key for the credentials.
26 | Defaults to 'snowflake'
27 |
28 | params (dict, optional): Connector parameters.
29 | Overrides Streamlit secrets. Defaults to None.
30 |
31 | local_development (bool, optional): If True, this will open a
32 | tab in your browser to collect requirements. Defaults to True.
33 |
34 | Returns:
35 | SnowflakeConnection: Snowflake connector object.
36 | """
37 |
38 | # Default params
39 | params: Dict[str, Any] = {
40 | **st.secrets[secrets_key],
41 | "client_session_keep_alive": True,
42 | "client_store_temporary_credential": True,
43 | }
44 |
45 | # Override default params with input params
46 | if input_params:
47 | for key in input_params.keys():
48 | params[key] = input_params[key]
49 |
50 | # This will open a tab in your browser and sign you in
51 | if use_browser:
52 | params["authenticator"] = "externalbrowser"
53 |
54 | connector = connect(**params)
55 | return connector
56 |
57 |
58 | snowflake_connector = get_connector(
59 | secrets_key="sf_usage_app",
60 | use_browser=False,
61 | )
62 |
63 | cur = snowflake_connector.cursor()
64 | cur.execute(f"use warehouse {st.secrets.sf_usage_app.warehouse};")
65 |
66 |
67 | @st.cache_data(ttl=TIME_TO_LIVE)
68 | def sql_to_dataframe(sql_query: str) -> pd.DataFrame:
69 | data = pd.read_sql(
70 | sql_query,
71 | snowflake_connector,
72 | )
73 | return data
74 |
75 |
76 | @st.cache_data(ttl=TIME_TO_LIVE)
77 | def get_queries_data(
78 | date_from: datetime.date,
79 | date_to: datetime.date,
80 | ):
81 | queries_data = sql_to_dataframe(
82 | sql.QUERIES_QUERY.format(
83 | date_from=date_from,
84 | date_to=date_to,
85 | )
86 | )
87 | queries_data["DURATION_SECS"] = round(
88 | (queries_data.TOTAL_ELAPSED_TIME) / 1000
89 | )
90 | queries_data["DURATION_SECS_PP"] = queries_data.DURATION_SECS.apply(
91 | gui.pretty_print_seconds
92 | )
93 | queries_data["QUERY_TEXT_PP"] = queries_data.QUERY_TEXT.apply(
94 | gui.pretty_print_sql_query
95 | )
96 | return queries_data
97 |
98 |
99 | if __name__ == "__main__":
100 | pass
101 |
--------------------------------------------------------------------------------
/utils/sql.py:
--------------------------------------------------------------------------------
1 | QUERIES_QUERY = """
2 | select *
3 | from snowflake.account_usage.query_history
4 | where START_TIME >= convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
5 | and START_TIME < convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz);
6 | """
7 |
8 | QUERIES_COUNT_QUERY = """
9 | select QUERY_TEXT,
10 | count(*) as number_of_queries,
11 | sum(TOTAL_ELAPSED_TIME)/1000 as execution_seconds,
12 | sum(TOTAL_ELAPSED_TIME)/(1000*60) as execution_minutes,
13 | sum(TOTAL_ELAPSED_TIME)/(1000*60*60) as execution_hours
14 | from SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY Q
15 | where 1=1
16 | and Q.START_TIME >= convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
17 | and Q.START_TIME <= convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz)
18 | and TOTAL_ELAPSED_TIME > 0 --only get queries that actually used compute
19 | and WAREHOUSE_NAME = '{warehouse_name}'
20 |
21 | group by 1
22 | having count(*) >= {num_min} --configurable/minimal threshold
23 |
24 | order by 2 desc
25 | limit {limit}; --configurable upper bound threshold
26 | """
27 |
28 | CONSUMPTION_PER_SERVICE_TYPE_QUERY = """
29 | select date_trunc('hour', convert_timezone('UTC', start_time)) as start_time,
30 | name,
31 | service_type,
32 | round(sum(credits_used), 1) as credits_used,
33 | round(sum(credits_used_compute), 1) as credits_compute,
34 | round(sum(credits_used_cloud_services), 1) as credits_cloud
35 | from snowflake.account_usage.metering_history
36 | where start_time >= convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
37 | and start_time < convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz)
38 | group by 1, 2, 3;
39 | """
40 |
41 | WAREHOUSE_USAGE_HOURLY = """
42 | // Credits used by [hour, warehouse] (past 7 days)
43 | select START_TIME ,
44 | WAREHOUSE_NAME ,
45 | CREDITS_USED_COMPUTE
46 | from SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
47 | where START_TIME >= convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
48 | and start_time < convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz)
49 | and WAREHOUSE_ID > 0 // Skip pseudo-VWs such as "CLOUD_SERVICES_ONLY"
50 | order by 1 desc,
51 | 2
52 | """
53 |
54 | STORAGE_QUERY = """
55 | select convert_timezone('UTC', usage_date) as usage_date,
56 | database_name as object_name,
57 | 'database' as object_type,
58 | max(AVERAGE_DATABASE_BYTES) as database_bytes,
59 | max(AVERAGE_FAILSAFE_BYTES) as failsafe_bytes,
60 | 0 as stage_bytes
61 | from snowflake.account_usage.database_storage_usage_history
62 | where usage_date >= date_trunc('day', ('{date_from}T00:00:00Z')::timestamp_ntz)
63 | and usage_date < date_trunc('day', ('{date_to}T00:00:00Z')::timestamp_ntz)
64 | group by 1, 2, 3
65 | union all
66 | select convert_timezone('UTC', usage_date) as usage_date,
67 | 'Stages' as object_name,
68 | 'stage' as object_type,
69 | 0 as database_bytes,
70 | 0 as failsafe_bytes,
71 | max(AVERAGE_STAGE_BYTES) as stage_bytes
72 | from snowflake.account_usage.stage_storage_usage_history
73 | where usage_date >= date_trunc('day', ('{date_from}T00:00:00Z')::timestamp_ntz)
74 | and usage_date < date_trunc('day', ('{date_to}T00:00:00Z')::timestamp_ntz)
75 | group by 1, 2, 3;
76 | """
77 |
78 | DATA_TRANSFER_QUERY = """
79 | select date_trunc('hour', convert_timezone('UTC', start_time)) as start_time,
80 | target_cloud,
81 | target_region,
82 | transfer_type,
83 | sum(bytes_transferred) as bytes_transferred
84 | from snowflake.account_usage.data_transfer_history
85 | where start_time >= convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
86 | and start_time < convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz)
87 | group by 1, 2, 3, 4;
88 | """
89 |
90 | USERS_QUERY = """
91 | with USER_HOUR_EXECUTION_CTE as
92 | (select USER_NAME ,
93 | WAREHOUSE_NAME ,
94 | DATE_TRUNC('hour', START_TIME) as START_TIME_HOUR ,
95 | SUM(EXECUTION_TIME) as USER_HOUR_EXECUTION_TIME
96 | from "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY"
97 | where WAREHOUSE_NAME is not null
98 | and EXECUTION_TIME > 0
99 | and START_TIME > convert_timezone('UTC', 'UTC', ('{date_from}T00:00:00Z')::timestamp_ltz)
100 | and START_TIME <= convert_timezone('UTC', 'UTC', ('{date_to}T00:00:00Z')::timestamp_ltz)
101 | group by 1,
102 | 2,
103 | 3),
104 | HOUR_EXECUTION_CTE as
105 | (select START_TIME_HOUR ,
106 | WAREHOUSE_NAME ,
107 | SUM(USER_HOUR_EXECUTION_TIME) as HOUR_EXECUTION_TIME
108 | from USER_HOUR_EXECUTION_CTE
109 | group by 1,
110 | 2),
111 | APPROXIMATE_CREDITS as
112 | (select A.USER_NAME ,
113 | C.WAREHOUSE_NAME ,
114 | (A.USER_HOUR_EXECUTION_TIME/B.HOUR_EXECUTION_TIME)*C.CREDITS_USED as APPROXIMATE_CREDITS_USED
115 | from USER_HOUR_EXECUTION_CTE A
116 | join HOUR_EXECUTION_CTE B on A.START_TIME_HOUR = B.START_TIME_HOUR
117 | and B.WAREHOUSE_NAME = A.WAREHOUSE_NAME
118 | join "SNOWFLAKE"."ACCOUNT_USAGE"."WAREHOUSE_METERING_HISTORY" C on C.WAREHOUSE_NAME = A.WAREHOUSE_NAME
119 | and C.START_TIME = A.START_TIME_HOUR)
120 | select USER_NAME,
121 | WAREHOUSE_NAME,
122 | SUM(APPROXIMATE_CREDITS_USED) as APPROXIMATE_CREDITS_USED
123 | from APPROXIMATE_CREDITS
124 | group by 1,
125 | 2
126 | order by 3 desc;
127 | """
128 |
129 | if __name__ == "__main__":
130 | pass
131 |
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