diff --git a/README.md b/README.md
index 8650a78..2d3e4ab 100644
--- a/README.md
+++ b/README.md
@@ -10,6 +10,9 @@ UniverSQL relies on Snowflake and Polaris for access control and data catalog so
[![Demo](https://s3.ezgif.com/tmp/ezgif-3-7afce8acdb.gif)](https://www.youtube.com/watch?v=s1fpSEE-pAc)
+#### [Watch on YouTube](https://www.youtube.com/watch?v=s1fpSEE-pAc)
+
+
# How it works?
* Snowflake SQL API implementation to handle the Snowflake connections, acting as a proxy between DuckDB and Snowflake.
diff --git a/testing.ipynb b/testing.ipynb
index 079b0a7..5d7398b 100644
--- a/testing.ipynb
+++ b/testing.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 23,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
@@ -23,14 +23,17 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 24,
"id": "a28646d5a8560160",
"metadata": {
"ExecuteTime": {
"end_time": "2024-08-23T14:30:35.029029Z",
"start_time": "2024-08-23T14:30:34.783445Z"
},
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
@@ -112,7 +115,7 @@
"
| \n",
" | \n",
" 600000 | \n",
- " 42540544 | \n",
+ " 42494464 | \n",
" ACCOUNTADMIN | \n",
" ... | \n",
" ON | \n",
@@ -220,7 +223,7 @@
"\n",
" bytes owner ... change_tracking is_external \\\n",
"0 43210240 ACCOUNTADMIN ... ON N \n",
- "1 42540544 ACCOUNTADMIN ... ON N \n",
+ "1 42494464 ACCOUNTADMIN ... ON N \n",
"2 58829312 ACCOUNTADMIN ... ON N \n",
"3 42525678592 ACCOUNTADMIN ... OFF N \n",
"4 0 ACCOUNTADMIN ... OFF N \n",
@@ -242,7 +245,7 @@
"[5 rows x 22 columns]"
]
},
- "execution_count": 27,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -253,40 +256,17 @@
},
{
"cell_type": "code",
- "execution_count": 69,
- "id": "ea7059a9dc949865",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-08-25T02:20:47.093505Z",
- "start_time": "2024-08-25T02:20:34.445351Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": " C_MKTSEGMENT count\n0 AUTOMOBILE 119008\n1 BUILDING 120568\n2 FURNITURE 119872\n3 HOUSEHOLD 120756\n4 MACHINERY 119796",
- "text/html": "\n\n
\n \n \n | \n C_MKTSEGMENT | \n count | \n
\n \n \n \n 0 | \n AUTOMOBILE | \n 119008 | \n
\n \n 1 | \n BUILDING | \n 120568 | \n
\n \n 2 | \n FURNITURE | \n 119872 | \n
\n \n 3 | \n HOUSEHOLD | \n 120756 | \n
\n \n 4 | \n MACHINERY | \n 119796 | \n
\n \n
\n
"
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "con.cursor().execute(\"\"\"SELECT \"TPCH_SF1\".\"DYNAMIC_ICEBERG_TABLE_CUSTOMER\".\"C_MKTSEGMENT\" AS \"C_MKTSEGMENT\", COUNT(*) AS \"count\" FROM \"MY_ICEBERG_JINJAT\".\"TPCH_SF1\".\"DYNAMIC_ICEBERG_TABLE_CUSTOMER\" GROUP BY \"TPCH_SF1\".\"DYNAMIC_ICEBERG_TABLE_CUSTOMER\".\"C_MKTSEGMENT\" ORDER BY \"TPCH_SF1\".\"DYNAMIC_ICEBERG_TABLE_CUSTOMER\".\"C_MKTSEGMENT\" ASC\"\"\").fetch_pandas_all()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "b157465faf38b029",
+ "execution_count": 26,
+ "id": "d01f1e0b-d4b7-4b16-b1a8-adf73b874dde",
"metadata": {
"ExecuteTime": {
- "end_time": "2024-08-23T02:34:57.320352Z",
- "start_time": "2024-08-23T02:34:56.948628Z"
+ "end_time": "2024-08-23T14:34:49.413Z",
+ "start_time": "2024-08-23T14:34:49.012550Z"
},
- "collapsed": false
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
},
"outputs": [
{
@@ -317,28 +297,28 @@
" \n",
" \n",
" 0 | \n",
- " AUTOMOBILE | \n",
- " 119008 | \n",
+ " MACHINERY | \n",
+ " 119796 | \n",
"
\n",
" \n",
" 1 | \n",
- " HOUSEHOLD | \n",
- " 120756 | \n",
+ " AUTOMOBILE | \n",
+ " 119008 | \n",
"
\n",
" \n",
" 2 | \n",
- " BUILDING | \n",
- " 120568 | \n",
+ " FURNITURE | \n",
+ " 119872 | \n",
"
\n",
" \n",
" 3 | \n",
- " MACHINERY | \n",
- " 119796 | \n",
+ " HOUSEHOLD | \n",
+ " 120756 | \n",
"
\n",
" \n",
" 4 | \n",
- " FURNITURE | \n",
- " 119872 | \n",
+ " BUILDING | \n",
+ " 120568 | \n",
"
\n",
" \n",
"\n",
@@ -346,32 +326,31 @@
],
"text/plain": [
" C_MKTSEGMENT count_star()\n",
- "0 AUTOMOBILE 119008\n",
- "1 HOUSEHOLD 120756\n",
- "2 BUILDING 120568\n",
- "3 MACHINERY 119796\n",
- "4 FURNITURE 119872"
+ "0 MACHINERY 119796\n",
+ "1 AUTOMOBILE 119008\n",
+ "2 FURNITURE 119872\n",
+ "3 HOUSEHOLD 120756\n",
+ "4 BUILDING 120568"
]
},
- "execution_count": 18,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "con.cursor().execute('select C_MKTSEGMENT, count(*) from my_iceberg_jinjat.tpch_sf1.\"DYNAMIC_ICEBERG_TABLE_CUSTOMER\" group by all').fetch_pandas_all()"
+ "con.cursor().execute(\"select C_MKTSEGMENT, count(*) from my_iceberg_jinjat.tpch_sf1.dynamic_iceberg_table_customer group by all\").fetch_pandas_all()"
]
},
{
"cell_type": "code",
- "execution_count": 31,
- "id": "d01f1e0b-d4b7-4b16-b1a8-adf73b874dde",
+ "execution_count": 27,
+ "id": "88d8a111-9e2e-4738-87e9-e43c53c0f4f5",
"metadata": {
"ExecuteTime": {
- "end_time": "2024-08-23T14:34:49.413Z",
- "start_time": "2024-08-23T14:34:49.012550Z"
- },
- "collapsed": false
+ "end_time": "2024-08-23T14:35:05.027142Z",
+ "start_time": "2024-08-23T14:34:51.583357Z"
+ }
},
"outputs": [
{
@@ -395,68 +374,37 @@
" \n",
" \n",
" | \n",
- " C_MKTSEGMENT | \n",
" count_star() | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
- " MACHINERY | \n",
- " 119796 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " AUTOMOBILE | \n",
- " 119008 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " FURNITURE | \n",
- " 119872 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " BUILDING | \n",
- " 120568 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " HOUSEHOLD | \n",
- " 120756 | \n",
+ " 1500000000 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " C_MKTSEGMENT count_star()\n",
- "0 MACHINERY 119796\n",
- "1 AUTOMOBILE 119008\n",
- "2 FURNITURE 119872\n",
- "3 BUILDING 120568\n",
- "4 HOUSEHOLD 120756"
+ " count_star()\n",
+ "0 1500000000"
]
},
- "execution_count": 31,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "con.cursor().execute(\"select C_MKTSEGMENT, count(*) from my_iceberg_jinjat.tpch_sf1.dynamic_iceberg_table_customer group by all\").fetch_pandas_all()"
+ "con.cursor().execute(\"select count(*) from my_iceberg_jinjat.tpch_sf1.orders_iceberg_tpch_sf1000\").fetch_pandas_all()"
]
},
{
"cell_type": "code",
- "execution_count": 32,
- "id": "88d8a111-9e2e-4738-87e9-e43c53c0f4f5",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2024-08-23T14:35:05.027142Z",
- "start_time": "2024-08-23T14:34:51.583357Z"
- }
- },
+ "execution_count": 28,
+ "id": "5be1a409-d43e-4e5a-bae1-df3bc32f9a8d",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -561,7 +509,7 @@
"[1000 rows x 2 columns]"
]
},
- "execution_count": 32,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -573,10 +521,8 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3bf62429db6fdb4f",
- "metadata": {
- "collapsed": false
- },
+ "id": "22864020-2b0b-46b5-94be-c74a6e691fbb",
+ "metadata": {},
"outputs": [],
"source": []
}