From 7e9860f736211e7b9831a318d900b970ec2ecc12 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Burak=20Kabakc=C4=B1?= Date: Fri, 23 Aug 2024 01:33:40 +0100 Subject: [PATCH] fix show commands --- README.md | 14 +- poetry.lock | 2630 +++++++++++++++++++++----- pyproject.toml | 4 +- testing.ipynb | 156 ++ universql/catalog/snowflake.py | 2 +- universql/lake/cloud.py | 2 +- universql/server.py | 13 +- universql/util.py | 7 +- universql/warehouse/duckdb/duckdb.py | 2 +- 9 files changed, 2298 insertions(+), 532 deletions(-) create mode 100644 testing.ipynb diff --git a/README.md b/README.md index b8cc0be..434e2cf 100644 --- a/README.md +++ b/README.md @@ -177,8 +177,8 @@ On Macbook Pro M2 2023, the performance is comparable to Snowflake up to 2B rows ### Latency -The latency relies on your network bandwidth. Your local disk is used for caching the data in data lake and the cache is populated lazily as you query the data and persisted across multiple runs. -Cold runs will likely to be slower than running the query on Snowflake as the data needs to be downloaded from the data lake with UniverSQL whereas Snowflake runs the compute in the same cloud region. +The latency relies on your network bandwidth. Your local disk is used for caching the data in a data lake and the cache is populated lazily as you query the data and persists across multiple runs. +Cold runs will likely be slower than running the query on Snowflake as the data needs to be downloaded from the data lake with UniverSQL whereas Snowflake runs the compute in the same cloud region. The subsequent queries (hot run) on the same table will be served from the cache. If the Iceberg table is updated, only the new data is downloaded from the data lake. The same data is never downloaded more than once. @@ -186,9 +186,9 @@ Iceberg supports predicate pushdown, which helps with partitioned tables to redu ## Signed certificates are required in Snowflake SQL V1 API -Snowflake V1 API requires valid CA certificate, which is [not possible with self-signed certificates](https://letsencrypt.org/docs/certificates-for-localhost/). +Snowflake V1 API requires a valid CA certificate, which is [not possible with self-signed certificates](https://letsencrypt.org/docs/certificates-for-localhost/). -If you don't need to expose UniverSQL to public internet with a public tunnel service, UniverSQL ships SSL certificate of [localhostcomputing.com](https://localhostcomputing.com) domain in the binary, which has [DNS record to 127.0.0.1](https://mxtoolbox.com/SuperTool.aspx?action=a%3alocalhostcomputing.com&run=toolpage). +If you don't need to expose UniverSQL to the public internet with a public tunnel service, UniverSQL ships SSL certificate of [localhostcomputing.com](https://localhostcomputing.com) domain in the binary, which has [DNS record to 127.0.0.1](https://mxtoolbox.com/SuperTool.aspx?action=a%3alocalhostcomputing.com&run=toolpage). It gives you free https connection to your local server and it's the default host. > [!NOTE] @@ -208,7 +208,7 @@ For Data lake, S3 and GCS supported. You have two alternatives: -1. Create a dynamic iceberg table replicating from your native Snowflake table. This approach requires warehouse but the usage will be minimum as dynamic tables are serverless, with the caveat to have some lag provided in `TARGET_LAG` option. +1. Create a dynamic iceberg table replicating your native Snowflake table. This approach requires a warehouse but the usage will be minimal as dynamic tables are serverless, with the caveat of having some lag provided in `TARGET_LAG` option. ```sql CREATE DYNAMIC ICEBERG TABLE my_iceberg_snowflake_table @@ -220,7 +220,7 @@ You have two alternatives: INITIALIZE = on_create AS SELECT * FROM my_snowflake_table; ``` -Dynamic tables is the recommended approach **if your natives tables have more than 2B+ of rows** so that you can filter / aggregate them before pulling them into your local environment. If your native tables are small enough, consider switching them to use Iceberg from Native. +Dynamic tables are the recommended approach **if your native tables have more than 1B+ of rows** so that you can filter/aggregate them before pulling them into your local environment. If your native tables are small enough, consider switching them to Iceberg from Native. 2. (coming soon) You can use `universql.execute` function to run queries directly in Snowflake and return the result as a table. You can join native Snowflake table with your local files as follows: @@ -246,5 +246,5 @@ The workaround is to use a public tunnel service to expose your local server to ## No support for Snowflake SQL V2 API yet -While SQL V1 API is internal, most Snowflake clients are using SQL V1 API, including JDBC, Python, ODBC etc. Feel free to help supporting [SQL V2 API](https://docs.snowflake.com/en/developer-guide/sql-api/intro) by contributing to the project. It should be easy enough as we already use Arrow interface for the V1 API, which is the interface for V2. +While SQL V1 API is internal, most Snowflake clients are using SQL V1 API, including JDBC, Python, ODBC etc. Feel free to help support [SQL V2 API](https://docs.snowflake.com/en/developer-guide/sql-api/intro) by contributing to the project. It should be easy enough as we already use Arrow interface for the V1 API, which is the interface for V2. diff --git a/poetry.lock b/poetry.lock index 1f343e4..6bfe3a1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,119 +2,134 @@ [[package]] name = "aiobotocore" -version = "2.13.1" +version = "2.13.3" description = "Async client for aws services using botocore and aiohttp" optional = false python-versions = ">=3.8" files = [ - {file = "aiobotocore-2.13.1-py3-none-any.whl", hash = "sha256:1bef121b99841ee3cc788e4ed97c332ba32353b1f00e886d1beb3aae95520858"}, - {file = "aiobotocore-2.13.1.tar.gz", hash = "sha256:134f9606c2f91abde38cbc61c3241113e26ff244633e0c31abb7e09da3581c9b"}, + {file = "aiobotocore-2.13.3-py3-none-any.whl", hash = "sha256:1272f765fd9414e1a68f8add71978367db94e17e36c3bf629cf1153eb5141fb9"}, + {file = "aiobotocore-2.13.3.tar.gz", hash = "sha256:ac5620f93cc3e7c2aef7c67ba2bb74035ff8d49ee2325821daed13b3dd82a473"}, ] [package.dependencies] aiohttp = ">=3.9.2,<4.0.0" 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-snowflake-connector-python = {extras = ["pandas", "secure-local-storage"], version = "^3.11.0"} +snowflake-connector-python = {extras = ["pandas", "secure-local-storage"], version = "^3.12.0"} eval-type-backport = "^0.2.0" pip-system-certs = "^4.0" # same with fakesnow @@ -28,7 +28,9 @@ sqlalchemy = "^2.0.31" fastapi-utils = "^0.7.0" fakesnow = "^0.9.20" humanize = "^4.10.0" + [tool.poetry.dev-dependencies] +jupyter = "^1.0.0" pylint = ">=2.11.1" [build-system] diff --git a/testing.ipynb b/testing.ipynb new file mode 100644 index 0000000..916c9ee --- /dev/null +++ b/testing.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-23T00:26:59.054767Z", + "start_time": "2024-08-23T00:26:57.825716Z" + } + }, + "outputs": [], + "source": [ + "import snowflake.connector\n", + "\n", + "con = snowflake.connector.connect(account='lt51601.europe-west2.gcp', database='MY_CUSTOM_APP', connection_name='default', host='localhostcomputing.com', port='8084', role='ACCOUNTADMIN')\n", + "cur = con.cursor()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7165f75bf836dbe3", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-23T00:26:02.643488Z", + "start_time": "2024-08-23T00:26:02.378622Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "con.cursor().execute(\"show tables\").fetch_pandas_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8131abc5f2b2c071", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-22T23:45:21.528820Z", + "start_time": "2024-08-22T23:45:19.533566Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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count_star()
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message = f"Unable to run Snowflake query {e.sfqid}: \n {compiled_sql} \n {e.msg}" + message = f"Unable to run Snowflake query {e.sfqid}: \n {e.msg}" raise SnowflakeError(e.sfqid, message, e.sqlstate) def close(self): diff --git a/universql/lake/cloud.py b/universql/lake/cloud.py index 28a8d66..7866fee 100644 --- a/universql/lake/cloud.py +++ b/universql/lake/cloud.py @@ -4,7 +4,6 @@ from fsspec.core import logger from fsspec.utils import setup_logging from pyiceberg.io import PY_IO_IMPL -from pyiceberg.io.fsspec import FsspecFileIO from pyiceberg.table import StaticTable from universql.lake.fsspec_util import MonitoredSimpleCacheFileSystem @@ -34,6 +33,7 @@ def gcs(cache_storage, project=None, token=None): def iceberg(data): + from pyiceberg.io.fsspec import FsspecFileIO io = FsspecFileIO(data) get_fs = io.get_fs io.get_fs = lambda name: MonitoredSimpleCacheFileSystem( diff --git a/universql/server.py b/universql/server.py index bafa18f..a0a7184 100644 --- a/universql/server.py +++ b/universql/server.py @@ -1,8 +1,6 @@ from __future__ import annotations import base64 -import gzip -import json import logging import os import signal @@ -39,11 +37,8 @@ @app.post("/session/v1/login-request") async def login_request(request: Request) -> JSONResponse: - request_body = await request.body() - if request.headers.get('content-encoding') == 'gzip': - request_body = gzip.decompress(request_body) + body = await unpack_request_body(request) - body = json.loads(request_body) login_data = body.get('data') client_environment = login_data.get('CLIENT_ENVIRONMENT') credentials = {key: client_environment[key] for key in ["schema", "warehouse", "role", "user", "database"] if @@ -127,7 +122,7 @@ async def delete_session(request: Request): @app.post("/telemetry/send") async def telemetry_request(request: Request) -> JSONResponse: - json = await request.json() + req = await unpack_request_body(request) return Response(status_code=200) @@ -193,8 +188,8 @@ async def home(request: Request) -> JSONResponse: return JSONResponse({"success": True, "status": "X-Duck is ducking 🐥"}) -@app.get("/monitoring/queries/{query_id:str}") # type: ignore[arg-type] -async def query_monitoring_query(self, request: Request) -> JSONResponse: +@app.get("/monitoring/queries/{query_id:str}") +async def query_monitoring_query(request: Request) -> JSONResponse: query_id = request.path_params["query_id"] if query_id not in query_results: return JSONResponse({"success": False, "message": "query not found"}) diff --git a/universql/util.py b/universql/util.py index 5153a50..8d76d29 100644 --- a/universql/util.py +++ b/universql/util.py @@ -245,7 +245,10 @@ class TextLiteral: async def unpack_request_body(request: Request) -> dict: body = await request.body() - uz = gzip.decompress(body) + if request.headers.get('content-encoding') == 'gzip': + uz = gzip.decompress(body) + else: + uz = body return json.loads(uz) @@ -324,6 +327,8 @@ def get_friendly_time_since(start_time): def prepend_to_lines(input_string, prepend_string=" ", vertical_string='------'): + if len(input_string) > 300: + input_string = input_string[0:300] + '[striped due to max 200 character limit]' lines = input_string.split('\n') modified_lines = [prepend_string + line for line in lines] modified_string = '\n'.join(modified_lines) diff --git a/universql/warehouse/duckdb/duckdb.py b/universql/warehouse/duckdb/duckdb.py index 0352512..2a0a908 100644 --- a/universql/warehouse/duckdb/duckdb.py +++ b/universql/warehouse/duckdb/duckdb.py @@ -100,7 +100,7 @@ def _do_query(self, raw_query: str) -> (str, List, pyarrow.Table): if last_compute == Compute.LOCAL and should_run_locally: for ast in queries: - if should_run_on_catalog(ast) and not should_run_locally: + if should_run_on_catalog(ast) or not should_run_locally: self.do_snowflake_query(queries, raw_query, start_time, local_error_message) last_compute = Compute.SNOWFLAKE else: