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Python: Top Three Tips for Optimal Performance","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis Quickstart guide contains key tips for optimal performance when using Snowpark Python.  The guide is broken up into multiple labs, each covering a key concept that can improve performance and/or efficiency while running workloads in Snowpark.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUnderstanding of \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowpark/reference/python/dataframe.html\"\u003ESnowpark Python Dataframes\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EUnderstanding of \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs\"\u003EPython UDFs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EFamilarity with using Notebook IDEs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, you will learn how to make optimized decisions when using Snowpark Python.  These choices will be compared with others to show performance improvements.  Each concept is broken up into a lab, listed below:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/\"\u003ELab 1\u003C/a\u003E: Using Snowpark dataframes perform ~8X faster compared to Pandas dataframes\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/\"\u003ELab 2\u003C/a\u003E: Using Vectorized UDFs can improve numerical computations by 30-40%\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/\"\u003ELab 3\u003C/a\u003E: Using Cachetools library to improve performance up to 20x (~20 mins)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003EAnaconda Packages enabled by ORGADMIN\u003C/a\u003E. If you do not have a Snowflake account, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003Efree trial account\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA Snowflake account login with ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to 1) Register for a free trial, 2) Use a different role that has the ability to create database, schema, tables, stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you are able to create the mentioned objects.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Ch3\u003EPython Environment Setup\u003C/h3\u003E\n","\u003Cp\u003ELet's set up the Python environment necessary to run this quickstart:\u003C/p\u003E\n","\u003Cp\u003EFirst, clone the source code for this repo to your local environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance\ncd sfguide-snowpark-python-top-three-tips-for-optimal-performance\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ESnowpark Python via Anaconda\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you are using a machine wth Apple M1 chip, follow \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/setup\"\u003Ethese instructions\u003C/a\u003E to create the virtual environment and install Snowpark Python instead of what's described below.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIf you are using \u003Ca href=\"https://www.anaconda.com/products/distribution\"\u003EAnaconda\u003C/a\u003E on your local machine, create a conda env for this quickstart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda env create -f conda_env.yml\nconda activate pysnowpark\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConda will automatically install \u003Ccode\u003Esnowflake-snowpark-python\u003C/code\u003E and all other dependencies for you.\u003C/p\u003E\n","\u003Cp\u003ENow, launch Jupyter Notebook on your local machine:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ejupyter notebook\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETroubleshooting \u003Ccode\u003Epyarrow\u003C/code\u003E related issues\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EIf you have \u003Ccode\u003Epyarrow\u003C/code\u003E library already installed, uninstall it before installing Snowpark.\u003C/li\u003E\u003Cli\u003EIf you do not have \u003Ccode\u003Epyarrow\u003C/code\u003E installed, you do not need to install it yourself; installing Snowpark automatically installs the appropriate version.\u003C/li\u003E\u003Cli\u003EDo not reinstall a different version of \u003Ccode\u003Epyarrow\u003C/code\u003E after installing Snowpark.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESnowflake Environment Setup\u003C/h3\u003E\n","\u003Cp\u003ECreate a fresh database in your Snowflake account for the following labs.  Login to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-quick-tour\"\u003ESnowsight\u003C/a\u003E and issue the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE DATABASE SNOWPARK_BEST_PRACTICES_LABS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe'll also standardize the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses\"\u003Ewarehouse\u003C/a\u003E we will use for the following labs.  Run the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE WAREHOUSE compute_wh WAREHOUSE_SIZE=SMALL INITIALLY_SUSPENDED=TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEdit the credentials.json file\u003C/h3\u003E\n","\u003Cp\u003EThroughout all the following labs, we will need to connect your notebook IDE to Snowflake.  This is done via a credentials file.  Edit the \u003Ccode\u003Ecredentials.json\u003C/code\u003E file found in the cloned repo with your Snowflake account information.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProvide your account information, username, and password.\u003C/li\u003E\u003Cli\u003EEnsure the role you are specifying upon login will have access to the database you just created.\u003C/li\u003E\u003Cli\u003ESpecify default warehouse as \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E as created above.\u003C/li\u003E\u003Cli\u003ESpecify the default database as \u003Ccode\u003ESNOWPARK_BEST_PRACTICES_LABS\u003C/code\u003E and default schema as \u003Ccode\u003EPUBLIC\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ELab 1 (Snowpark Dataframes) - Overview\u003C/h2\u003E\n","\u003Cp\u003ESnowpark is essentially a wrapper that converts Spark or Pandas APIs into SQL when executing and data engineers don't need to spend excessive time to find out how it works behind scenes. This notebook guides you through why we should use Snowpark Dataframe and avoid using Pandas Data Frame.\u003C/p\u003E\n","\u003Cp\u003EThough Pandas has tons of really good APIs in day-to-day DE job, the challenge with Pandas is that it does not scale with your data volume linearly due to single-machine processing. For example, Pandas fails with out-of-memory error if it attempts to read a dataset that is larger than the memory available in a single machine.\u003C/p\u003E\n","\u003Cp\u003ENow we will see how Pandas works with Snowpark and how Snowflake has tried to parallelize the processing of Pandas API. Lets use a simple example to remove duplicates from a 6M sample table.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/snowparkbp_lab3_dedup.png\" alt=\"ndedup\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELab 1 (Snowpark Dataframes) - Execution\u003C/h2\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EYour snowflake user needs to be granted to a role have write access to database SNOWFLAKE_SAMPLE_DATA. For instructions to import SNOWFLAKE_SAMPLE_DATA, please click \u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data-using\"\u003Ehere\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EBy now, you should have finished setting up credentials.json configuration file in the overview section, please ensure this configuration file is in the same folder of the Jupyter Notebook you will run.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Do\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a Snowpark Session and connect with Snowpark using the connection parameters specified in credentials.json file.\u003C/li\u003E\u003Cli\u003ECreate a Stored Procedure to remove duplicates using Snowpark Dataframe and analyze how SQL is run in your Snowflake account.\u003C/li\u003E\u003Cli\u003ECreate a Stored Procedure to remove duplicates using Pandas Dataframe.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERun the Lab\u003C/h3\u003E\n","\u003Cp\u003EOpen up the jupyter notebook titled \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab1_avoid_pandas_df.ipynb\"\u003Elab1_avoid_pandas_df\u003C/a\u003E and run each of the cells.\u003C/p\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EIf you are interested, you can go to Snowflake UI to to check query history of the runs.\nI have summarized the performance differences between Snowpark Dataframe and Pandas Dataframe, both using the same small warehouse. As you can see the performance using Snowpark Dataframe is 8X faster than using Pandas Dataframe and it's always a best practice to leverage Snowpark Dataframe as much as you can!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/snowparkbp_lab3_compare.png\" alt=\"compare\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ELab 2 (Vectorised UDFs) - Overview\u003C/h2\u003E\n","\u003Ch3\u003ELab Summary\u003C/h3\u003E\n","\u003Cp\u003EThe Snowpark API provides methods that help to create a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/udf-overview\"\u003EUser Defined Function\u003C/a\u003E. This can be done either using a Lambda or a typical Function in Python. When you create these UDFs, the Snowpark library uploads this code to an internal stage. When you call these UDFs, the Snowpark library executes your function on the server, where the data is. As a result, the data doesn’t need to be transferred to the client in order for the function to process the data.\u003C/p\u003E\n","\u003Cp\u003ENaturally, you're wondering then, what is a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs#label-snowpark-python-udf-vectorized\"\u003EVectorised UDF\u003C/a\u003E?\u003C/p\u003E\n","\u003Cp\u003EThe Python UDF batch API enables defining Python functions that receive batches of input rows (aka chunked rows) as Pandas DataFrames and return batches of results as Pandas arrays or Series. The column in the Snowpark dataframe will be vectorized as a Pandas Series inside the UDF.\u003C/p\u003E\n","\u003Cp\u003EThe goal of this lab is to compare the performance of a UDF Operation with and without using the Python UDF Batch API (or Vectorised UDFs).\u003C/p\u003E\n","\u003Cp\u003EIn this notebook, we will do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWe will work with 4 datasets, namely Customers and Orders from the TPCH Datasets of different sizes (15M to 1.5B records) freely and instantly available in Snowflake through an inbound share.\u003C/li\u003E\u003Cli\u003EWe will use 3 different Virtual Warehouse sizes, i.e., Small, Medium, and Large.\u003C/li\u003E\u003Cli\u003EWe will execute 4 different use-cases, namely, Numerical Computation, String Manipulation, Regex Masking, and Timestamp Manipulation. Please note, these are hypothetical use-cases but are commonly found when working with data in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ELab 2 (Vectorised UDFs) - Execution\u003C/h2\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EYour snowflake user needs to be granted to a role have write access to database SNOWFLAKE_SAMPLE_DATA. For instructions to import SNOWFLAKE_SAMPLE_DATA, please click \u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data-using\"\u003Ehere\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EBy now, you should have finished setting up credentials.json configuration file in the overview section, please ensure this configuration file is in the same folder of the Jupyter Notebook you will run.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Do\u003C/h3\u003E\n","\u003Cp\u003EThis lab consists of the following major steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EImporting libraries and connecting to Snowflake\u003C/li\u003E\u003Cli\u003ECreating the dataframes of test data from the TPCDS dataset\u003C/li\u003E\u003Cli\u003ESetting up the virtual warehouses prior to testing\u003C/li\u003E\u003Cli\u003ERunning four test suites on typical UDF operations\n\u003Cul\u003E\u003Cli\u003ENumeric Computation (~3 minutes)\u003C/li\u003E\u003Cli\u003EString Manipulation (~5 minutes)\u003C/li\u003E\u003Cli\u003EBatch Sizing (~5 minutes)\u003C/li\u003E\u003Cli\u003ETimestamp Manipulation (~60 minutes)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EAnalyzing the results, forming conclusions, and cleanup\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERun the lab\u003C/h3\u003E\n","\u003Cp\u003EOpen up the jupyter notebook titled \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab2_vectorized_udfs.ipynb\"\u003Elab2_vectorized_udfs\u003C/a\u003E and run each of the cells.\u003C/p\u003E\n","\u003Ch3\u003EAnalysis\u003C/h3\u003E\n","\u003Cp\u003EBelow is analysis of the tests ran in the notebook.  Please compare these findings with the results in your notebook.\u003C/p\u003E\n","\u003Ch4\u003ERecap\u003C/h4\u003E\n","\u003Cp\u003EBefore we look into the results, let's recap what we have done so far:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWe took 4 different TPCH datasets of varying sizes...\u003C/li\u003E\u003Cli\u003E...Used 3 different Virtual Warehouse sizes, i.e., Small, Medium, and Large...\u003C/li\u003E\u003Cli\u003E...On 4 different hypothetical use-cases...\u003C/li\u003E\u003Cli\u003E...and generated results that will allow us to compare the performance of a UDF Operation with and without using Vectorised UDFs .\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ENumeric Computation Use Cases\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/numeric_results_table.png\" alt=\"Numeric Results Table\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor Numerical Computation, keeping the same dataset and warehouse size, Vectorised UDFs outperform Normal UDFs.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThis is also seen when we change the size of the warehouse (to Large) to accommodate the change in dataset size (to Customer 1000)\u003C/li\u003E\u003Cli\u003EThis is because Numerical data types (including Boolean [0, 1]) expose their contents as \u003Cem\u003Ememoryviews()\u003C/em\u003E. Memoryviews allow direct read & write access to the data without needing to copy it. But if you do require to copy it, they don't need to be read sequentially first. This significantly improves performance.\u003C/li\u003E\u003Cli\u003EWhat is peculiar is the behavior when working with a Medium warehouse and Customer 100 dataset. Where it performs slightly less efficient than a Small Warehouse. This could have several reasons, but what I noticed in the Query Profile was:\n\u003Cul\u003E\u003Cli\u003EIt takes the Medium warehouse upto 35% of the time to initialise as compared to ~10% of time for a Smaller warehouse.\u003C/li\u003E\u003Cli\u003EAnother is the time it took to create the python sandbox environment, i.e. ~900ms on a Medium warehouse compared to <400ms for Smaller warehouse.  The last one especially could compound across the UDFs that get created and to cause the slight difference in performance.\u003C/li\u003E\u003Cli\u003EOf course, this previous point above doesn't carry across the other dataset and warehouse size.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cem\u003EOptional Work\u003C/em\u003E: Leverage \u003Ccode\u003EGET_QUERY_OPERATOR_STATS()\u003C/code\u003E table function for a more detailed and thorough analysis.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ENon-Numeric Computation Use Cases\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/nonnumeric_results_table.png\" alt=\"NonNumeric Results Table\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs expected, it makes sense not to use Vectorised UDFs for Non-numeric operations\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBut apart from the obvious, let's check out what the Query Profile looks like for the String Manipulation for Customer 100 Dataset when using a Small warehouse:\n\u003Cul\u003E\u003Cli\u003EThe Total (and by extention Average) UDF Handler Execution Time was about half for a Normal UDF when compared to the Vectorised UDF\u003C/li\u003E\u003Cli\u003EThe Average UDF encoding/decoding time was less than 1/100th of a millisecond for a Normal UDF when compared to 5 ms for a Vectorised UDF\u003C/li\u003E\u003Cli\u003EI think we have to appreciate the difference of orders of magnitude here and especially when compounded, really makes the difference in performance between a Normal and Vectorised UDF\u003C/li\u003E\u003Cli\u003EThe Python Sandbox environment creation time was 1/3rd for Normal UDFs when compared to Vectorised UDFs\u003C/li\u003E\u003Cli\u003EIt is also important to note that the Bytes scanned and written were almost the same, and the partitions were the same as no partitioning had been done to this dataset.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cem\u003EOptional Work\u003C/em\u003E: Leverage \u003Ccode\u003EGET_QUERY_OPERATOR_STATS()\u003C/code\u003E table function for a more detailed and thorough analysis.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EBatch Sizes\u003C/h4\u003E\n","\u003Cp\u003EAdditionally, when using Vectorised UDFs, you may want to play around with Batch Sizes.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThis is done with the input argument to the decorator @udf like so:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E@udf(max_batch_size=1000)\ndef vect_udf_bc_100(inp: PandasSeries[float]) -> PandasSeries[float]:\n    return (inp - df_customer_100_mean + df_customer_100_stddev) * 10000.0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt is important to note:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESetting a batch size doesn't guarantee which records will be present in a particular batch\u003C/li\u003E\u003Cli\u003EIt also doesn't guarantee that a batch size will be of the same size. For instance, if the argument \u003Ccode\u003Emax_batch_size = 50000\u003C/code\u003E, does not mean 50k records are sent per batch\u003C/li\u003E\u003Cli\u003EAn increase in batch size doesn't guarantee improvement in performance\u003C/li\u003E\u003Cli\u003EA very very low batch size (e.g. ~50-200) does slow performance depending on the size of the dataset. While a moderately sized batch (e.g. ~2000) does improve performance. This is what I have seen in my tests\u003C/li\u003E\u003Cli\u003ETypically, Snowflake automatically picks a batch size for you which is performance efficient and should suffice in most cases\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EYour Python code must operate efficiently on batch of rows\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor Numerical Computations, you can expect between 30% and 40% improvement in performance using Vectorised UDFs\u003C/li\u003E\u003Cli\u003EFor Non-Numeric Computaions/Manipulations, you're likely to see performance degredation using Vectorised UDFs; therefore we would recommend to stick with normal non-vectorised UDFs\u003C/li\u003E\u003Cli\u003EIf you're working with Numerical Computations, xperiment with different Warehouse and Batch Sizes to get more performance as a result. But remember, higher sizes does not always mean or always guarantee higher performance\u003C/li\u003E\u003Cli\u003ELastly, make sure to dive deep into Query Profiling either through the Snowsight UI or the newly launched \u003Ccode\u003EGET_QUERY_OPERATOR_STATS()\u003C/code\u003E table function to further optimize\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECleanup\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nIf you did not complete all test cases in the notebook, please ensure you cleaned up the environment by executing the final code block so that all other labs will function properly\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003ELab 3 (Cachetools) - Overview\u003C/h2\u003E\n","\u003Ch3\u003ELab Summary\u003C/h3\u003E\n","\u003Cp\u003EWhen using Snowpark, it is common for data engineers and data scientists to create pickle files, upload them to internal/external stage, and use them with Snowflake UDFs and Stored Procedures (SPs).  This lab will show how using the Python library Cachetools can be used to speed up UDF or SP performance by ensuring the logic is cached in memory in cases of repeated reads. For simplicity we will demonstrate this scenario using a pickle file which has the dictionary object serialized.\u003C/p\u003E\n","\u003Ch3\u003EBackground Information - Cachetools and pickle files\u003C/h3\u003E\n","\u003Ch4\u003ECachetools\u003C/h4\u003E\n","\u003Cp\u003E\u003Ca href=\"https://pypi.org/project/cachetools/\"\u003ECachetools\u003C/a\u003E is a Python library that provides a collection of caching algorithms. It offers simple and efficient caching methods, such as LRU (Least Recently Used) and FIFO (First In First Out), to store a limited number of items for a specified duration. The library is useful in applications where temporarily storing data in memory improves performance. It's easy to use and can be integrated into an application with just a few lines of code.\u003C/p\u003E\n","\u003Cp\u003EThe Cachetools library with Snowpark UDF can be used for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELoading pretrained ML models from the stage for model inference\u003C/li\u003E\u003Cli\u003EReading data which is stored in pickle file\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EPickle File\u003C/h4\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://docs.python.org/3/library/pickle.html\"\u003Epickle\u003C/a\u003E file is a file format used to store Python objects as binary data. It is used in data engineering to store and retrieve data objects that can be used across different applications or scripts.For example, you might use pickle to store data objects such as pandas dataframes, numpy arrays, or even custom Python classes and functions. By using pickle, you can save these objects to disk and load them later without having to recreate the data from scratch.\u003C/p\u003E\n","\u003Ch3\u003EScenario\u003C/h3\u003E\n","\u003Cp\u003EThere is a ficticious system that generates transactional data along with the day of the week information for each row. To access the names of the weekdays, we are required to use a Python pickle file that is shared by the upstream data teams. This file contains the names of the weekdays, which may change in different scenarios. To ensure compatibility and security, the upstream data teams only provide access to this information through the pickle file.\u003C/p\u003E\n","\u003Cp\u003ETo analyze this data in Snowflake Snowpark Python, we have created a Python User Defined Function (UDF) to efficiently retrieve the corresponding day name based on the day number which comes as an input. This UDF provides a convenient and reliable way to access the day names from the pickle file, allowing us to perform the necessary DataFrame transformations for our analysis.\u003C/p\u003E\n","\u003Cp\u003ELately as the number of transactions are growing we are seeing performance degradation of our select queries which is using the UDF to fetch the required information from the pickle file. We need to look for ways to improve the performance of the Python UDF that we have created.\u003C/p\u003E\n","\u003Ch2\u003ELab 3 (Cachetools) - Execution\u003C/h2\u003E\n","\u003Ch3\u003EWhat You'll Do\u003C/h3\u003E\n","\u003Cp\u003EThis lab consists of the following major steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EImporting libraries and connecting to Snowflake\u003C/li\u003E\u003Cli\u003ECreating a pickle file\u003C/li\u003E\u003Cli\u003ERunning a UDF without Cachetools\u003C/li\u003E\u003Cli\u003ERunning a UDF with Cachetools\u003C/li\u003E\u003Cli\u003EConclusion and Cleanup\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERun the lab\u003C/h3\u003E\n","\u003Cp\u003EOpen up the jupyter notebook titled \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab3_cachetools_library.ipynb\"\u003Elab3_cachetools_library\u003C/a\u003E and run each of the cells.\u003C/p\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EBased on the our testing, we have identified that the total duration for the query using Cachetools decorator for this use case yields a significant performance increase for the Python UDF execution.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully performed all the labs illustrating the best practices for getting optimal performance when using Snowpark Python.  You're now ready to implement these concepts in your own projects.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe importance of Snowpark dataframes versus Pandas dataframes\u003C/li\u003E\u003Cli\u003EThe performance benefits of Vectorised UDFs, and what type of operations benefit the most from them\u003C/li\u003E\u003Cli\u003EThe performance benefits of the cachetools library\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/\"\u003EGetting Started with Data Engineering and ML using Snowpark Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAdvanced: Snowpark for Python Data Engineering Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAdvanced: Snowpark for Python Machine Learning Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md\"\u003ESnowpark for Python Demos\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html\"\u003ESnowpark for Python Developer Guide\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nThis Quickstart guide contains key tips for optimal performance when using Snowpark Python.  The guide is broken up into multiple labs, each covering a key concept that can improve performance and/or efficiency while running workloads in Snowpark.  \r\n\r\n### Prerequisites\r\n- Understanding of [Snowpark Python Dataframes](https://docs.snowflake.com/developer-guide/snowpark/reference/python/dataframe.html)\r\n- Understanding of [Python UDFs](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs)\r\n- Familarity with using Notebook IDEs\r\n\r\n### What You’ll Learn\r\n\r\nIn this quickstart, you will learn how to make optimized decisions when using Snowpark Python.  These choices will be compared with others to show performance improvements.  Each concept is broken up into a lab, listed below:\r\n\r\n- [Lab 1](/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/): Using Snowpark dataframes perform ~8X faster compared to Pandas dataframes\r\n- [Lab 2](/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/): Using Vectorized UDFs can improve numerical computations by 30-40%\r\n- [Lab 3](/en/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/): Using Cachetools library to improve performance up to 20x (~20 mins)\r\n\r\n### What You’ll Need \r\n\r\n- A Snowflake account with [Anaconda Packages enabled by ORGADMIN](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda). If you do not have a Snowflake account, you can register for a [free trial account](https://signup.snowflake.com/?utm_cta=quickstarts_).\r\n- A Snowflake account login with ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to 1) Register for a free trial, 2) Use a different role that has the ability to create database, schema, tables, stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you are able to create the mentioned objects.\r\n\r\n\r\n## Setup\r\n\r\n### Python Environment Setup \r\n\r\nLet's set up the Python environment necessary to run this quickstart:\r\n\r\nFirst, clone the source code for this repo to your local environment:\r\n```bash\r\ngit clone https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance\r\ncd sfguide-snowpark-python-top-three-tips-for-optimal-performance\r\n```\r\n\r\n#### Snowpark Python via Anaconda\r\n\r\n\u003E aside positive\r\n\u003E IMPORTANT:\r\n\u003E - If you are using a machine wth Apple M1 chip, follow [these instructions](https://docs.snowflake.com/en/developer-guide/snowpark/python/setup) to create the virtual environment and install Snowpark Python instead of what's described below.\r\n\r\nIf you are using [Anaconda](https://www.anaconda.com/products/distribution) on your local machine, create a conda env for this quickstart:\r\n```bash\r\nconda env create -f conda_env.yml\r\nconda activate pysnowpark\r\n```\r\n\r\nConda will automatically install `snowflake-snowpark-python` and all other dependencies for you.\r\n\r\nNow, launch Jupyter Notebook on your local machine:\r\n```bash\r\njupyter notebook\r\n```\r\n\r\n### Troubleshooting `pyarrow` related issues\r\n\r\n- If you have `pyarrow` library already installed, uninstall it before installing Snowpark.\r\n- If you do not have `pyarrow` installed, you do not need to install it yourself; installing Snowpark automatically installs the appropriate version.\r\n- Do not reinstall a different version of `pyarrow` after installing Snowpark.\r\n\r\n### Snowflake Environment Setup\r\n\r\nCreate a fresh database in your Snowflake account for the following labs.  Login to [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight-quick-tour) and issue the following command:\r\n\r\n```\r\nCREATE DATABASE SNOWPARK_BEST_PRACTICES_LABS;\r\n```\r\n\r\nWe'll also standardize the [warehouse](https://docs.snowflake.com/en/user-guide/warehouses) we will use for the following labs.  Run the following command:\r\n\r\n```\r\nCREATE OR REPLACE WAREHOUSE compute_wh WAREHOUSE_SIZE=SMALL INITIALLY_SUSPENDED=TRUE;\r\n```\r\n\r\n### Edit the credentials.json file\r\n\r\nThroughout all the following labs, we will need to connect your notebook IDE to Snowflake.  This is done via a credentials file.  Edit the `credentials.json` file found in the cloned repo with your Snowflake account information.  \r\n - Provide your account information, username, and password.\r\n - Ensure the role you are specifying upon login will have access to the database you just created.\r\n - Specify default warehouse as `COMPUTE_WH` as created above.\r\n - Specify the default database as `SNOWPARK_BEST_PRACTICES_LABS` and default schema as `PUBLIC`.\r\n\r\n## Lab 1 (Snowpark Dataframes) - Overview\r\n\r\n\r\nSnowpark is essentially a wrapper that converts Spark or Pandas APIs into SQL when executing and data engineers don't need to spend excessive time to find out how it works behind scenes. This notebook guides you through why we should use Snowpark Dataframe and avoid using Pandas Data Frame.\r\n\r\nThough Pandas has tons of really good APIs in day-to-day DE job, the challenge with Pandas is that it does not scale with your data volume linearly due to single-machine processing. For example, Pandas fails with out-of-memory error if it attempts to read a dataset that is larger than the memory available in a single machine.\r\n\r\n\r\nNow we will see how Pandas works with Snowpark and how Snowflake has tried to parallelize the processing of Pandas API. Lets use a simple example to remove duplicates from a 6M sample table.\r\n![ndedup](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/snowparkbp_lab3_dedup.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Lab 1 (Snowpark Dataframes) - Execution\r\n\r\n\r\n### Prerequisites\r\n- Your snowflake user needs to be granted to a role have write access to database SNOWFLAKE_SAMPLE_DATA. For instructions to import SNOWFLAKE_SAMPLE_DATA, please click [here](https://docs.snowflake.com/en/user-guide/sample-data-using).\r\n- By now, you should have finished setting up credentials.json configuration file in the overview section, please ensure this configuration file is in the same folder of the Jupyter Notebook you will run.\r\n\r\n\r\n\r\n### What You'll Do\r\n- Create a Snowpark Session and connect with Snowpark using the connection parameters specified in credentials.json file.\r\n- Create a Stored Procedure to remove duplicates using Snowpark Dataframe and analyze how SQL is run in your Snowflake account.\r\n- Create a Stored Procedure to remove duplicates using Pandas Dataframe.\r\n\r\n### Run the Lab\r\n\r\nOpen up the jupyter notebook titled [lab1_avoid_pandas_df](https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab1_avoid_pandas_df.ipynb) and run each of the cells.\r\n\r\n\r\n### Conclusion \r\n\r\nIf you are interested, you can go to Snowflake UI to to check query history of the runs.\r\nI have summarized the performance differences between Snowpark Dataframe and Pandas Dataframe, both using the same small warehouse. As you can see the performance using Snowpark Dataframe is 8X faster than using Pandas Dataframe and it's always a best practice to leverage Snowpark Dataframe as much as you can!\r\n\r\n![compare](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/snowparkbp_lab3_compare.png)\r\n\r\n## Lab 2 (Vectorised UDFs) - Overview\r\n\r\n### Lab Summary\r\n\r\nThe Snowpark API provides methods that help to create a [User Defined Function](https://docs.snowflake.com/en/sql-reference/udf-overview). This can be done either using a Lambda or a typical Function in Python. When you create these UDFs, the Snowpark library uploads this code to an internal stage. When you call these UDFs, the Snowpark library executes your function on the server, where the data is. As a result, the data doesn’t need to be transferred to the client in order for the function to process the data.\r\n\r\nNaturally, you're wondering then, what is a [Vectorised UDF](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs#label-snowpark-python-udf-vectorized)?\r\n\r\nThe Python UDF batch API enables defining Python functions that receive batches of input rows (aka chunked rows) as Pandas DataFrames and return batches of results as Pandas arrays or Series. The column in the Snowpark dataframe will be vectorized as a Pandas Series inside the UDF.\r\n\r\nThe goal of this lab is to compare the performance of a UDF Operation with and without using the Python UDF Batch API (or Vectorised UDFs).\r\n\r\nIn this notebook, we will do the following:\r\n - We will work with 4 datasets, namely Customers and Orders from the TPCH Datasets of different sizes (15M to 1.5B records) freely and instantly available in Snowflake through an inbound share.\r\n - We will use 3 different Virtual Warehouse sizes, i.e., Small, Medium, and Large.\r\n - We will execute 4 different use-cases, namely, Numerical Computation, String Manipulation, Regex Masking, and Timestamp Manipulation. Please note, these are hypothetical use-cases but are commonly found when working with data in Snowflake.\r\n\r\n## Lab 2 (Vectorised UDFs) - Execution\r\n\r\n### Prerequisites\r\n- Your snowflake user needs to be granted to a role have write access to database SNOWFLAKE_SAMPLE_DATA. For instructions to import SNOWFLAKE_SAMPLE_DATA, please click [here](https://docs.snowflake.com/en/user-guide/sample-data-using).\r\n- By now, you should have finished setting up credentials.json configuration file in the overview section, please ensure this configuration file is in the same folder of the Jupyter Notebook you will run.\r\n\r\n\r\n### What You'll Do\r\n\r\nThis lab consists of the following major steps:\r\n\r\n1. Importing libraries and connecting to Snowflake\r\n2. Creating the dataframes of test data from the TPCDS dataset\r\n3. Setting up the virtual warehouses prior to testing\r\n4. Running four test suites on typical UDF operations \r\n    - Numeric Computation (~3 minutes)\r\n    - String Manipulation (~5 minutes)\r\n    - Batch Sizing (~5 minutes)\r\n    - Timestamp Manipulation (~60 minutes)\r\n5. Analyzing the results, forming conclusions, and cleanup\r\n\r\n### Run the lab\r\n\r\nOpen up the jupyter notebook titled [lab2_vectorized_udfs](https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab2_vectorized_udfs.ipynb) and run each of the cells.\r\n\r\n### Analysis\r\n\r\nBelow is analysis of the tests ran in the notebook.  Please compare these findings with the results in your notebook.\r\n\r\n#### Recap\r\n\r\nBefore we look into the results, let's recap what we have done so far:\r\n- We took 4 different TPCH datasets of varying sizes...\r\n- ...Used 3 different Virtual Warehouse sizes, i.e., Small, Medium, and Large...\r\n- ...On 4 different hypothetical use-cases...\r\n- ...and generated results that will allow us to compare the performance of a UDF Operation with and without using Vectorised UDFs .\r\n\r\n#### Numeric Computation Use Cases\r\n\r\n![Numeric Results Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/numeric_results_table.png)\r\n\r\nFor Numerical Computation, keeping the same dataset and warehouse size, Vectorised UDFs outperform Normal UDFs.\r\n- This is also seen when we change the size of the warehouse (to Large) to accommodate the change in dataset size (to Customer 1000)\r\n- This is because Numerical data types (including Boolean [0, 1]) expose their contents as _memoryviews()_. Memoryviews allow direct read & write access to the data without needing to copy it. But if you do require to copy it, they don't need to be read sequentially first. This significantly improves performance.\r\n- What is peculiar is the behavior when working with a Medium warehouse and Customer 100 dataset. Where it performs slightly less efficient than a Small Warehouse. This could have several reasons, but what I noticed in the Query Profile was:\r\n  - It takes the Medium warehouse upto 35% of the time to initialise as compared to ~10% of time for a Smaller warehouse.\r\n  - Another is the time it took to create the python sandbox environment, i.e. ~900ms on a Medium warehouse compared to \u003C400ms for Smaller warehouse.  The last one especially could compound across the UDFs that get created and to cause the slight difference in performance.\r\n  - Of course, this previous point above doesn't carry across the other dataset and warehouse size.\r\n- *Optional Work*: Leverage `GET_QUERY_OPERATOR_STATS()` table function for a more detailed and thorough analysis.\r\n\r\n#### Non-Numeric Computation Use Cases\r\n\r\n![NonNumeric Results Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-python-top-three-tips-for-optimal-performance/nonnumeric_results_table.png)\r\n\r\nAs expected, it makes sense not to use Vectorised UDFs for Non-numeric operations\r\n- But apart from the obvious, let's check out what the Query Profile looks like for the String Manipulation for Customer 100 Dataset when using a Small warehouse:\r\n  - The Total (and by extention Average) UDF Handler Execution Time was about half for a Normal UDF when compared to the Vectorised UDF\r\n  - The Average UDF encoding/decoding time was less than 1/100th of a millisecond for a Normal UDF when compared to 5 ms for a Vectorised UDF\r\n  - I think we have to appreciate the difference of orders of magnitude here and especially when compounded, really makes the difference in performance between a Normal and Vectorised UDF\r\n  - The Python Sandbox environment creation time was 1/3rd for Normal UDFs when compared to Vectorised UDFs\r\n  - It is also important to note that the Bytes scanned and written were almost the same, and the partitions were the same as no partitioning had been done to this dataset.\r\n- *Optional Work*: Leverage `GET_QUERY_OPERATOR_STATS()` table function for a more detailed and thorough analysis.\r\n\r\n#### Batch Sizes\r\n\r\nAdditionally, when using Vectorised UDFs, you may want to play around with Batch Sizes.\r\n- This is done with the input argument to the decorator @udf like so:\r\n```\r\n@udf(max_batch_size=1000)\r\ndef vect_udf_bc_100(inp: PandasSeries[float]) -\u003E PandasSeries[float]:\r\n    return (inp - df_customer_100_mean + df_customer_100_stddev) * 10000.0\r\n```\r\nIt is important to note:\r\n- Setting a batch size doesn't guarantee which records will be present in a particular batch\r\n- It also doesn't guarantee that a batch size will be of the same size. For instance, if the argument `max_batch_size = 50000`, does not mean 50k records are sent per batch\r\n- An increase in batch size doesn't guarantee improvement in performance\r\n- A very very low batch size (e.g. ~50-200) does slow performance depending on the size of the dataset. While a moderately sized batch (e.g. ~2000) does improve performance. This is what I have seen in my tests\r\n- Typically, Snowflake automatically picks a batch size for you which is performance efficient and should suffice in most cases\r\n\r\n### Conclusion\r\n\r\nYour Python code must operate efficiently on batch of rows\r\n- For Numerical Computations, you can expect between 30% and 40% improvement in performance using Vectorised UDFs\r\n- For Non-Numeric Computaions/Manipulations, you're likely to see performance degredation using Vectorised UDFs; therefore we would recommend to stick with normal non-vectorised UDFs\r\n- If you're working with Numerical Computations, xperiment with different Warehouse and Batch Sizes to get more performance as a result. But remember, higher sizes does not always mean or always guarantee higher performance\r\n- Lastly, make sure to dive deep into Query Profiling either through the Snowsight UI or the newly launched `GET_QUERY_OPERATOR_STATS()` table function to further optimize\r\n\r\n### Cleanup\r\n\r\n\u003E aside positive\r\n\u003E If you did not complete all test cases in the notebook, please ensure you cleaned up the environment by executing the final code block so that all other labs will function properly\r\n\r\n\r\n\r\n\r\n## Lab 3 (Cachetools) - Overview\r\n\r\n### Lab Summary\r\n\r\nWhen using Snowpark, it is common for data engineers and data scientists to create pickle files, upload them to internal/external stage, and use them with Snowflake UDFs and Stored Procedures (SPs).  This lab will show how using the Python library Cachetools can be used to speed up UDF or SP performance by ensuring the logic is cached in memory in cases of repeated reads. For simplicity we will demonstrate this scenario using a pickle file which has the dictionary object serialized.\r\n\r\n\r\n### Background Information - Cachetools and pickle files\r\n\r\n#### Cachetools\r\n[Cachetools](https://pypi.org/project/cachetools/) is a Python library that provides a collection of caching algorithms. It offers simple and efficient caching methods, such as LRU (Least Recently Used) and FIFO (First In First Out), to store a limited number of items for a specified duration. The library is useful in applications where temporarily storing data in memory improves performance. It's easy to use and can be integrated into an application with just a few lines of code.\r\n\r\nThe Cachetools library with Snowpark UDF can be used for:\r\n\r\n- Loading pretrained ML models from the stage for model inference\r\n- Reading data which is stored in pickle file\r\n\r\n#### Pickle File\r\nA [pickle](https://docs.python.org/3/library/pickle.html) file is a file format used to store Python objects as binary data. It is used in data engineering to store and retrieve data objects that can be used across different applications or scripts.For example, you might use pickle to store data objects such as pandas dataframes, numpy arrays, or even custom Python classes and functions. By using pickle, you can save these objects to disk and load them later without having to recreate the data from scratch.\r\n\r\n### Scenario\r\n\r\nThere is a ficticious system that generates transactional data along with the day of the week information for each row. To access the names of the weekdays, we are required to use a Python pickle file that is shared by the upstream data teams. This file contains the names of the weekdays, which may change in different scenarios. To ensure compatibility and security, the upstream data teams only provide access to this information through the pickle file.\r\n\r\nTo analyze this data in Snowflake Snowpark Python, we have created a Python User Defined Function (UDF) to efficiently retrieve the corresponding day name based on the day number which comes as an input. This UDF provides a convenient and reliable way to access the day names from the pickle file, allowing us to perform the necessary DataFrame transformations for our analysis.\r\n\r\nLately as the number of transactions are growing we are seeing performance degradation of our select queries which is using the UDF to fetch the required information from the pickle file. We need to look for ways to improve the performance of the Python UDF that we have created.\r\n\r\n## Lab 3 (Cachetools) - Execution\r\n\r\n### What You'll Do\r\n\r\nThis lab consists of the following major steps:\r\n\r\n1. Importing libraries and connecting to Snowflake\r\n2. Creating a pickle file\r\n3. Running a UDF without Cachetools \r\n4. Running a UDF with Cachetools\r\n5. Conclusion and Cleanup\r\n\r\n### Run the lab\r\n\r\nOpen up the jupyter notebook titled [lab3_cachetools_library](https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance/blob/main/lab3_cachetools_library.ipynb) and run each of the cells.\r\n\r\n### Conclusion \r\n\r\nBased on the our testing, we have identified that the total duration for the query using Cachetools decorator for this use case yields a significant performance increase for the Python UDF execution.\r\n\r\n## Conclusion and Resources\r\n\r\nCongratulations! You've successfully performed all the labs illustrating the best practices for getting optimal performance when using Snowpark Python.  You're now ready to implement these concepts in your own projects.\r\n\r\n### What You Learned\r\n\r\n- The importance of Snowpark dataframes versus Pandas dataframes\r\n- The performance benefits of Vectorised UDFs, and what type of operations benefit the most from them\r\n- The performance benefits of the cachetools library \r\n\r\n### Related Resources\r\n\r\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-snowpark-python-top-three-tips-for-optimal-performance)\r\n- [Getting Started with Data Engineering and ML using Snowpark Python](/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/) \r\n- [Advanced: Snowpark for Python Data Engineering Guide](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\r\n- [Advanced: Snowpark for Python Machine Learning Guide](/en/developers/guides/getting-started-snowpark-machine-learning/)\r\n- [Snowpark for Python Demos](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\r\n- [Snowpark for Python Developer Guide](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html)\r\n\r\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-7bbc498573","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-e068f76a49","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-19d149b652","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-0d7ac42637","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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