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support for processing Healthcare HL7 V2.x messages. The labs will give you a view into how you can leverage Snowflake’s data programmability features to parse HL7 V2.x messages using Java UDTF, Java UDF, and Python UDF.\u003C/p\u003E\n","\u003Cp\u003ESign up for a free 30-day trial of Snowflake and follow along with this lab exercise. After completing the labs, you’ll be ready to start processing and managing your own HL7 V2.x messages in Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/Architecture.png\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, database concepts, and objects\u003C/li\u003E\u003Cli\u003EBasic Python and Java knowledge\u003C/li\u003E\u003Cli\u003ERecommend to get familiarized with the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting_started_with_snowflake/getting_started_with_snowflake.md\"\u003ESnowflake User Interface\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ERecommended to also complete \u003Ca href=\"/en/developers/guides/getting-started-with-snowsql/\"\u003EGetting Started with SnowSQL\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to ingest and process HL7 V2.x messages\u003C/li\u003E\u003Cli\u003EHow to process HL7 V2.x messages using Java UDF's and UDTF's, Python UDF's.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA snowflake stage (internal) for storing and accessing HL7 V2.x in Snowflake.\u003C/li\u003E\u003Cli\u003EA user-defined function (UDF) and user-defined table functions (UDTF) using Snowflake's engine to process files.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ENotice and Terms of Use\u003C/h2\u003E\n","\u003Cp\u003EThe data provided for this lab is an extract from the Simhospital repository on github (\u003Ca href=\"https://github.com/google/simhospital/blob/master/docs/artifacts/messages.out\"\u003ESimhospital dataset\u003C/a\u003E).\u003C/p\u003E\n","\u003Cp\u003EUse of the data provided is limited to this quickstart in connection with the Snowflake service and is subject to any additional terms and conditions on the Simhospital github repository.\u003C/p\u003E\n","\u003Cp\u003EBy accessing this data, you acknowledge and agree to the limits and terms related to the use of the HL7 V2.x dataset.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003E\u003Cem\u003EThe implementations provided here are at best MVP/Prototype versions, they are not of production quality. You are free to extend the functionality and improve the code as it fits your functionality.\u003C/em\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare your lab environment\u003C/h2\u003E\n","\u003Cp\u003EIf you haven't already, register for a \u003Ca href=\"https://trial.snowflake.com/\"\u003ESnowflake free 30-day trial\u003C/a\u003E. The Snowflake edition (Standard, Enterprise, Business Critical, e.g.), cloud provider (AWS, Azure, e.g.), and Region (US East, EU, e.g.) do not matter for this lab. We suggest you select the region which is physically closest to you and the Enterprise Edition, our most popular offering. After registering, you will receive an email with an activation link and your Snowflake account URL.\u003C/p\u003E\n","\u003Ch3\u003ENavigating to Snowflake User Interface\u003C/h3\u003E\n","\u003Cp\u003EFor this lab, you will use the latest Snowflake web interface.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen a browser window and enter the URL of your Snowflake 30-day trial environment that was sent with your registration email.Log into your Snowflake trial account\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab in the left-hand navigation bar. The ​Worksheets​ tab provides an interface for submitting SQL queries, performing DDL and DML operations, and viewing results as your queries or operations complete.\u003C/li\u003E\u003Cli\u003EA new worksheet is created by clicking \u003Cstrong\u003E+ Worksheet\u003C/strong\u003E on the top right.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EDownload Data and Scripts\u003C/h3\u003E\n","\u003Cp\u003EAll source code for this guide can be found on \u003Ca href=\"https://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/\"\u003ESnowflake Labs Github\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to first download the following files to the local workstation by clicking on the hyperlinks below. The subsequent steps in the next section require \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003ESnowSQL CLI\u003C/a\u003E installed on the local workstation where the lab is ran.\u003C/p\u003E\n","\u003Ch4\u003ESample HL7 V2.x messages data file.\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/src/test/data/hl7/hl7_2-3_samples.txt\"\u003Ehl7_2-3_samples.txt\u003C/a\u003E    - Sample HL7 V2.x messages data file.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EJava UDF/UDTF related files and scripts\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/target/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar\"\u003Esf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar\u003C/a\u003E  - This implementation uses the \u003Ca href=\"https://hapifhir.github.io/hapi-hl7v2/\"\u003EHL7 HAPI parser\u003C/a\u003E to parse the HL7 V2.x messages. Jar file includes all the dependencies.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EPython UDF related files and scripts\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://files.pythonhosted.org/packages/28/85/09438fc4b5d9598192010d04347de98dc9f4676661ccbba2695707c217f0/hl7apy-1.3.4.tar.gz\"\u003Ehl7apy-1.3.4.tar.gz\u003C/a\u003E  - This implementation uses the HL7APY python library, to parse the message into JSON format. The HL7APY v1.3.4 was used for this implementation.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/src/main/python/hl7pyparserUDF.py\"\u003Ehl7pyparserUDF.py\u003C/a\u003E - Snowflake HL7 Python UDF data processing code\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDownload all the files locally to your workstation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELocal Files\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/prep_localfiles.png\" alt=\"Local Files\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStore & Access HL V2.x Messages\u003C/h2\u003E\n","\u003Cp\u003ELet’s start by preparing to load the HL7 V2.x messages into Snowflake. Snowflake supports two types of stages for storing data files used for loading and unloading:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-overview.html#internal-stages\"\u003EInternal stages\u003C/a\u003E store the files internally within Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-overview.html#external-stages\"\u003EExternal stages\u003C/a\u003E store the files in an external location (i.e. S3 bucket) that is referenced by the stage. An external stage specifies location and credential information, if required, for the bucket.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate a Database, Schema, and Warehouse\u003C/h3\u003E\n","\u003Cp\u003EBefore creating any stages, let’s create a database and a schema that will be used for loading the unstructured data. We will use the UI within the Worksheets tab to run the DDL that creates the database and schema. Copy the commands below into your trial environment, and execute each individually.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\n\ncreate or replace database hl7db comment = 'HL7 Database';\ncreate or replace schema hl7db.hl7v2demo;\ncreate or replace warehouse quickstart warehouse_size = 'SMALL' initially_suspended = TRUE auto_suspend=60;\n\n\nuse database hl7db;\nuse schema hl7v2demo;\nuse warehouse quickstart;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003ECreate Snowflake Stages to Ingest and Store HL7 V2.x Messages\u003C/h3\u003E\n","\u003Cp\u003EStages in Snowflake are locations used to store data. If the data that needs to be loaded into Snowflake is stored in other cloud regions like AWS S3 or Azure or GCP then these are called External stages whereas if the data is stored inside Snowflake then these are called Internal stages.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E For the purpose of this lab we are using Internal stage. You can do the same with External Stage.\u003C/p\u003E\n","\u003Ch3\u003EStore HL7 V2.x messages in an Internal Stage\u003C/h3\u003E\n","\u003Cp\u003EYou can store data and files directly in Snowflake with internal stages. Now, we want to create an internal stage and upload the files using SnowSQL CLI.\u003C/p\u003E\n","\u003Ch4\u003ECreate an Internal Stage\u003C/h4\u003E\n","\u003Cp\u003ERun this command to create an internal stage called \u003Ccode\u003Ehl7_stage_internal\u003C/code\u003E as follows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema hl7db.hl7v2demo;\n-- Directory tables store a catalog of staged files in cloud storage.\ncreate or replace stage hl7_stage_internal\n   directory = ( enable = TRUE )\n   comment = 'used for staging data & libraries';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EUpload Files using SnowSQL to Internal Stage\u003C/h4\u003E\n","\u003Cp\u003ELet's now upload the files downloaded to your workstation to Snowflake internal stage \u003Ccode\u003Ehl7_stage_internal\u003C/code\u003E. We will use \u003Cstrong\u003ESnowSQL CLI\u003C/strong\u003E and \u003Cstrong\u003EPUT\u003C/strong\u003E command to load all files. Before opening terminal, find out your account identifier which for the trial account will be.\u003C/p\u003E\n","\u003Cp\u003EFor example, if the URL to access the trial account is \u003Ccode\u003Ehttps://xx74264.ca-central-1.aws.snowflakecomputing.com/\u003C/code\u003E. These are the values for the account identifier:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccount identifier: \u003Ccode\u003Exx74264\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThere may be additional segments if you are using your own account part of an organization. You can find those from the URL of your Snowflake account. Please check the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers\"\u003ESnowflake Documentation\u003C/a\u003E for additional details on this topic.\u003C/p\u003E\n","\u003Cp\u003EFor your convenience, use the below query to retrieve account identifier.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- retrieve your Snowflake account identifier \nselect replace(t.value:host::varchar,'.snowflakecomputing.com') as account_identifier \nfrom table(flatten(input => parse_json(system$allowlist()))) as t\nwhere t.value:type::varchar = 'SNOWFLAKE_DEPLOYMENT_REGIONLESS';\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's first prepare the snowflake internal stage structure. stage name is \u003Ccode\u003Ehl7_stage_internal\u003C/code\u003E and we have three sub directories in it \u003Ccode\u003Edataset\u003C/code\u003E, \u003Ccode\u003Ejava_stage\u003C/code\u003E and \u003Ccode\u003Epython_stage\u003C/code\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Edataset\u003C/code\u003E   - contains sample HL7 data files.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ejava_stage\u003C/code\u003E  - contains Java UDF/UDTF related code/dependencies jar file.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Epython_stage\u003C/code\u003E  - contains external HL7apy lib tar file and UDF code.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInternal Stage Files\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EUsing Snowsql upload the files.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Using a terminal window invoke snowsql.\n\nsnowsql -a <account-identifier> -u <username>\n\n-- set the database/schema/warehouse context\nuse database hl7db;\nuse schema hl7v2demo;\nuse warehouse quickstart;\n\n-- load sample hl7 data\nput file:////<downloaded files folder path>/hl7_2-3_samples.txt @hl7_stage_internal/dataset/ auto_compress=false;\n\n-- load java related code/dependencies jar\nput file:////<downloaded files folder path>/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar @hl7_stage_internal/java_stage/ auto_compress=false;\n\n-- load external dependency python lib\nput file:////<downloaded files folder path>/hl7apy-1.3.4.tar.gz @hl7_stage_internal/python_stage/ auto_compress=false;\n\n\n-- load python UDF code\nput file:////<downloaded files folder path>/hl7pyparserUDF.py @hl7_stage_internal/python_stage/ auto_compress=false;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_snowsql_put_files.png\" alt=\"SnowSQL Put File\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERefresh Stage\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- ensure to refresh the stage\nalter stage hl7_stage_internal refresh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_refresh_stage.png\" alt=\"Refresh stage metadata\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EList Files\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- run this to verify the files shows up in the list\nselect *\nfrom directory(@hl7_stage_internal);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_list_directory_table.png\" alt=\"Put Files\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProcessing HL7 messages using Java UDF & UDTF\u003C/h2\u003E\n","\u003Cp\u003EJava UDFs and UDTFs allow workloads expressed in Java to execute in Snowflake and therefore benefit from the virtually unlimited performance and scalability of the Data Cloud.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate Java UDTF function\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EA UDTF is a user-defined function (UDF) that returns tabular results. A Java UDTF specifies the Java data types of the output columns by defining an output row class. Each row returned from the UDTF is returned as an instance of the output row class. Each instance of the output row class contains one public field for each output column. Snowflake reads the values of the public fields from each instance of the output row class, converts the Java values to SQL values, and constructs a SQL output row containing those values.\u003C/p\u003E\n","\u003Ch4\u003ECreating the Java UDTF\u003C/h4\u003E\n","\u003Cp\u003ENow lets create the Java UDTF, this function will refer a HAPI library for parsing raw HL7 V2.x messages. The Java UDTF can be defined in Snowflake as below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace function hl7_hapi_parser(hl7_fl_url varchar ,validate_message boolean)\n   returns table ( \n        parsed_status boolean, \n        raw_msg varchar, \n        hl7_xml varchar,  \n        hl7_json variant,  \n        message_type varchar, \n        message_version varchar, \n        message_sequence_num integer, \n        error_msg varchar \n    )\n  language JAVA\n  imports = ('@hl7_stage_internal/java_stage/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar')\n  handler = 'com.snowflake.labs.hl7.HL7UDTF'\n  comment = 'Java based UDTF for parsing HL7v2 files.';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ENote: The above 'Create Java UDTF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name <hl7_stage_internal>\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Ch4\u003EParsing the HL7 data\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Parse the file and store in a local table\n\ncreate or replace table hl7v2parsed as \n  select * from (\n  with base as (\n      select \n          relative_path as data_filepath,\n          concat(build_scoped_file_url('@hl7_stage_internal/', data_filepath)) as full_path\n      from directory( @hl7_stage_internal )\n      where relative_path like 'dataset/%'\n  )\n  select \n      full_path, p.* \n  from base as b\n      ,table(hl7_hapi_parser(b.full_path, false) ) as p\n);\n\n-- verify the data inside the table\nselect * from hl7v2parsed;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_table_output.png\" alt=\"Java UDTF Result\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELets run additional queries to analyze the data parsed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Flatten the JSON Field to pull OBSERVATION (OBR) segment fields for one of the ORU messages\n\nwith base as (\n    select hl7_json as msg\n    from hl7v2parsed\n  where message_sequence_num=1 and message_type='ORU_R01'\n), oru_response_sgmt as (\n    select \n        msg:"ORU_R01.RESPONSE" as oru_response\n    from base\n)\n select\n    oru_response,\n    oru_response:"ORU_R01.ORDER_OBSERVATION":"OBR":"OBR.4" as ob4,\n    ob4:"CE.1" as ce_1,\n    ob4:"CE.2"::string as ce_2,\n    ob4:"CE.3"::string as ce_3\n from oru_response_sgmt as b;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_oru_obs.png\" alt=\"Java UDTF Flatten Result1\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Flatten the JSON to pull OBSERVATION - Patient Report Text fields\nwith base as (\n    select hl7_json as msg\n    from hl7v2parsed\n  where full_path like '%hl7_2-3_samples.txt' and message_type='ORU_R01'\n)\n select\n    msg,\n    msg:"ORU_R01.RESPONSE":"ORU_R01.ORDER_OBSERVATION":"ORU_R01.OBSERVATION" as OBSERVATIONS,\n    obx.value:"OBX":"OBX.5" as PATIENT_REPORT_TEXT\n from base,table(flatten(msg:"ORU_R01.RESPONSE":"ORU_R01.ORDER_OBSERVATION":"ORU_R01.OBSERVATION")) obx ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_patient_report_text.png\" alt=\"Java UDTF Flatten Result2\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- List out patient and physician interactions\n\nwith base as (\n    select hl7_json \n    from hl7v2parsed\n    where message_type = 'ORU_R01'\n)\n,patient_physician as (\n    select \n        concat(\n            f.value:"ORU_R01.PATIENT":"ORU_R01.VISIT":"PV1":"PV1.7":"XCN.2"::string, ' '\n            ,f.value:"ORU_R01.PATIENT":"ORU_R01.VISIT":"PV1":"PV1.7":"XCN.3"::string) as physician\n        ,concat(\n            f.value:"ORU_R01.PATIENT":"PID":"PID.5":"XPN.1"::string, ' '\n            ,f.value:"ORU_R01.PATIENT":"PID":"PID.5":"XPN.2"::string, ' '\n            ,f.value:"ORU_R01.PATIENT":"PID":"PID.5":"XPN.3"::string, ' '\n        ) as patient\n    from base as b\n        ,lateral flatten(input => hl7_json) as f\n    where key like 'ORU_R01.RESPONSE'\n    and patient is not null\n)\nselect\n    patient, physician\n    ,count(*) as number_of_visits\nfrom patient_physician\ngroup by patient, physician\norder by number_of_visits desc;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_patient_physicians.png\" alt=\"Java UDTF Flatten Result3\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate Java UDF function\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EA UDF (user-defined function) is a user-written function that can be called from Snowflake in the same way that a built-in function can be called.When a user calls a UDF, the user passes the name of the UDF and the parameters of the UDF to Snowflake. If the UDF is a Java UDF, Snowflake calls the appropriate Java code (called a handler method) in a JAR file. The handler method then returns the output to Snowflake, which passes it back to the client.\u003C/p\u003E\n","\u003Ch4\u003ECreating the Java UDF\u003C/h4\u003E\n","\u003Cp\u003EIdeally you would be using a Kafka connector, Snowpipe or other mechanisms to load the HL7 V2.x messages to Snowflake and then execute Java UDF for each raw HL7 V2.x message. For this lab we will simulate this by leveraging the HL7 V2.x raw messages loaded into the raw_msg column of hl7v2parsed table created in the previous section.\u003C/p\u003E\n","\u003Cp\u003ENow lets create the Java UDF, this function will refer a HAPI library for parsing raw HL7 V2.x messages. The Java UDF can be defined in Snowflake as below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace function hl7_hapi_udf_parser(hl7_msg varchar ,validate_message boolean)\nreturns variant\nlanguage java\nimports = ('@hl7_stage_internal/java_stage/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar')\nhandler = 'com.snowflake.labs.hl7.HL7UDF.process'\ncomment = 'Java based UDF for parsing HL7v2 messages.';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ENote: The above 'Create Java UDF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name <hl7_stage_internal>\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Ch4\u003EParsing the HL7 data\u003C/h4\u003E\n","\u003Cp\u003EThe previously loaded raw_hl7_table has each record as a single hl7v2 message in the raw_msg column.\nThe following query invokes the function, to parse the HL7 message file :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith base as (\n    select\n        raw_msg, -- column holding the HL7v2.x message in pipe delimited format\n        hl7_hapi_udf_parser(raw_msg, false) as parsed\n    from hl7v2parsed\n    limit 10\n)\nselect\n    parse_json(parsed:"hl7_json") as hl7_json\nfrom base;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe above query examples clearly demonstrates the beauty and simplicity of Snowflake's capabilities to parse and analyze the HL7 V2.x messages.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProcessing HL7 messages using Python UDF\u003C/h2\u003E\n","\u003Cp\u003EUser-defined functions (UDFs) let you extend the system to perform operations that are not available through the built-in, system-defined functions provided by Snowflake. Python UDFs (user-defined functions) allow you to write Python code and call it as though it were a SQL function\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate Python UDF function\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's create a Python UDF function, this function will refer a hl7apy library for parsing raw messages. Using \u003Ccode\u003Eimports\u003C/code\u003E we can pass external library and python code. After executing \u003Ccode\u003Ehl7pyparser\u003C/code\u003E it will return a JSON messages and here in this example snowflake \u003Ccode\u003Evariant\u003C/code\u003E data type has been used to store JSON message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace function hl7pyparser(hl7_raw string)\nreturns variant\nlanguage python\nruntime_version = 3.8\nimports=('@hl7_stage_internal/python_stage/hl7apy-1.3.4.tar.gz', '@hl7_stage_internal/python_stage/hl7pyparserUDF.py')\nhandler = 'hl7pyparserUDF.udf'\ncomment = 'python based hl7v2 message parser';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ENote: The above 'Create Python UDF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name <hl7_stage_internal>\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile Format\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will create a Snowflake FILE FORMAT to read the RAW data directly from the stage, more details about snowflake file format can be found here - \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-file-format.html#create-file-format\"\u003EFILE FORMAT\u003C/a\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE FILE FORMAT "HL7DB"."HL7V2DEMO"."RAW_HL7_FILE_FORMAT"\n   TYPE = 'CSV'\n   COMPRESSION = 'NONE'\n   FIELD_DELIMITER = 'NONE'\n   RECORD_DELIMITER = 'n'\n   SKIP_HEADER = 0\n   FIELD_OPTIONALLY_ENCLOSED_BY = 'NONE'\n   TRIM_SPACE = FALSE\n   ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE\n   ESCAPE = 'NONE'\n   ESCAPE_UNENCLOSED_FIELD = '134'\n   DATE_FORMAT = 'AUTO'\n   TIMESTAMP_FORMAT = 'AUTO' NULL_IF = ('\\N');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's verify that our FILE FORMAT is correct and we are able to read files from stage. For now we will read raw message into single Column \u003Ccode\u003ERAW_MSG\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ERemember our data is stored inside dataset sub directory and we will aslo add pattern for reading only text files.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReading from stage\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect t.$1 as RAW_MSG from @hl7_stage_internal/dataset\n   (file_format => 'RAW_HL7_FILE_FORMAT', pattern=>'.*.*[.]txt') t\n   where RAW_MSG is not NULL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's create a separate temporary table to store this raw data \u003Ccode\u003Eraw\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TEMPORARY TABLE raw\nAS SELECT t.$1 AS RAW_MSG FROM @hl7_stage_internal/dataset\n   (file_format => 'RAW_HL7_FILE_FORMAT', pattern=>'.*.*[.]txt') t\n   WHERE RAW_MSG IS NOT NULL;\n\n\n-- verify data inside raw table\nselect * from raw;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/tmp_raw.png\" alt=\"Tmp Raw\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUse UDF function\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFinally, let's use our UDF function \u003Ccode\u003Ehl7pyparser\u003C/code\u003E that we created  earlier.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n   raw_msg,\n   parse_json(hl7pyparser(raw_msg)) as JSON_MSG,\n   parse_json(hl7pyparser(raw_msg)):msh:message_type:message_type:id as SRC\nfrom raw\nwhere src = 'ORU';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Eraw_msg\u003C/code\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EColumn contains RAW HL7 messages which we read earlier from the stage and from \u003Ccode\u003Eraw\u003C/code\u003E table\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003Eparse_json(hl7pyparser(raw_msg)) as JSON_MSG\u003C/code\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Eparse_json\u003C/strong\u003E function will read the RAW HL7 messages from RAW_MSG column and it will convert it to JSON\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003Eparse_json(hl7pyparser(raw_msg)):msh:message_type:message_type:id as SRC\u003C/code\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETo further flatten or parse the json column, we will use colon (\u003Ccode\u003E:\u003C/code\u003E) between keys.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor example - \u003Ccode\u003Emsh\u003C/code\u003E is a root element in json string and \u003Ccode\u003Emessage_type\u003C/code\u003E and \u003Ccode\u003Eid\u003C/code\u003E are nested keys (Inner elements in json)\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/udf_output_table.png\" alt=\"UDF Output\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You used Snowflake to trasform your HL7 V2.x messages using Snowflake Java UDFs/UDTFs and Python UDFs.\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EStoring HL7 V2.x messages with an \u003Cstrong\u003EInternal Stage\u003C/strong\u003E and \u003Cstrong\u003ESnowSQL\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECatalog HL7 V2.x messages data with \u003Cstrong\u003EDirectory Tables\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EProcessing HL7 V2.x messages with a \u003Cstrong\u003EJava UDF\u003C/strong\u003E and \u003Cstrong\u003EJava UDTF\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EProcessing HL7 V2.x messages with a \u003Cstrong\u003EPython UDF\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EProcessing JSON HL7 data with \u003Cstrong\u003Eparse_json\u003C/strong\u003E function\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/unstructured.html\"\u003EUnstructured Data Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/java/udf-java.html\"\u003EJava UDF Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/java/udf-java-tabular-functions.html#tabular-java-udfs-udtfs\"\u003EJava UDTF Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-introduction.html#introduction-to-python-udfs\"\u003EPython UDF Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-tabular-functions.html#implementing-user-defined-table-functions-udtfs-in-python\"\u003EPython UDTF Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index.html\"\u003ESnowpark Docs\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nThis Quickstart is designed to help you understand the capabilities included in Snowflake’s support for processing Healthcare HL7 V2.x messages. The labs will give you a view into how you can leverage Snowflake’s data programmability features to parse HL7 V2.x messages using Java UDTF, Java UDF, and Python UDF.\r\n\r\nSign up for a free 30-day trial of Snowflake and follow along with this lab exercise. After completing the labs, you’ll be ready to start processing and managing your own HL7 V2.x messages in Snowflake.\r\n\r\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/Architecture.png)\r\n\r\n\r\n### Prerequisites\r\n- Snowflake account\r\n- Basic knowledge of SQL, database concepts, and objects\r\n- Basic Python and Java knowledge\r\n- Recommend to get familiarized with the [Snowflake User Interface](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting_started_with_snowflake/getting_started_with_snowflake.md)\r\n- Recommended to also complete [Getting Started with SnowSQL](/en/developers/guides/getting-started-with-snowsql/)\r\n\r\n\r\n### What You’ll Learn\r\n- How to ingest and process HL7 V2.x messages\r\n- How to process HL7 V2.x messages using Java UDF's and UDTF's, Python UDF's. \r\n\r\n\r\n### What You'll Build\r\n- A snowflake stage (internal) for storing and accessing HL7 V2.x in Snowflake.\r\n- A user-defined function (UDF) and user-defined table functions (UDTF) using Snowflake's engine to process files.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Notice and Terms of Use\r\n\r\nThe data provided for this lab is an extract from the Simhospital repository on github ([Simhospital dataset](https://github.com/google/simhospital/blob/master/docs/artifacts/messages.out)).\r\n\r\n\r\nUse of the data provided is limited to this quickstart in connection with the Snowflake service and is subject to any additional terms and conditions on the Simhospital github repository.\r\n\r\n\r\nBy accessing this data, you acknowledge and agree to the limits and terms related to the use of the HL7 V2.x dataset.\r\n\r\n### **Note:**\r\n*The implementations provided here are at best MVP/Prototype versions, they are not of production quality. You are free to extend the functionality and improve the code as it fits your functionality.*\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Prepare your lab environment\r\n\r\nIf you haven't already, register for a [Snowflake free 30-day trial](https://trial.snowflake.com/). The Snowflake edition (Standard, Enterprise, Business Critical, e.g.), cloud provider (AWS, Azure, e.g.), and Region (US East, EU, e.g.) do not matter for this lab. We suggest you select the region which is physically closest to you and the Enterprise Edition, our most popular offering. After registering, you will receive an email with an activation link and your Snowflake account URL.\r\n\r\n\r\n### Navigating to Snowflake User Interface\r\nFor this lab, you will use the latest Snowflake web interface.\r\n1. Open a browser window and enter the URL of your Snowflake 30-day trial environment that was sent with your registration email.Log into your Snowflake trial account\r\n2. Click on __Worksheets__ tab in the left-hand navigation bar. The ​Worksheets​ tab provides an interface for submitting SQL queries, performing DDL and DML operations, and viewing results as your queries or operations complete. \r\n3. A new worksheet is created by clicking __+ Worksheet__ on the top right.\r\n\r\n\r\n### Download Data and Scripts\r\nAll source code for this guide can be found on [Snowflake Labs Github](\u003Chttps://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/\u003E) \r\n\r\nWe need to first download the following files to the local workstation by clicking on the hyperlinks below. The subsequent steps in the next section require [SnowSQL CLI](https://docs.snowflake.com/en/user-guide/snowsql.html) installed on the local workstation where the lab is ran.\r\n\r\n#### Sample HL7 V2.x messages data file.\r\n- [hl7_2-3_samples.txt](\u003Chttps://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/src/test/data/hl7/hl7_2-3_samples.txt\u003E)    - Sample HL7 V2.x messages data file.\r\n\r\n\r\n#### Java UDF/UDTF related files and scripts\r\n- [sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar](https://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/target/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar)  - This implementation uses the [HL7 HAPI parser](https://hapifhir.github.io/hapi-hl7v2/) to parse the HL7 V2.x messages. Jar file includes all the dependencies.\r\n\r\n\r\n#### Python UDF related files and scripts\r\n- [hl7apy-1.3.4.tar.gz](\u003Chttps://files.pythonhosted.org/packages/28/85/09438fc4b5d9598192010d04347de98dc9f4676661ccbba2695707c217f0/hl7apy-1.3.4.tar.gz\u003E)  - This implementation uses the HL7APY python library, to parse the message into JSON format. The HL7APY v1.3.4 was used for this implementation.\r\n\r\n\r\n- [hl7pyparserUDF.py](\u003Chttps://github.com/Snowflake-Labs/Snowflake-HL7V2-Parsing/blob/main/src/main/python/hl7pyparserUDF.py\u003E) - Snowflake HL7 Python UDF data processing code\r\n\r\n\r\nDownload all the files locally to your workstation.\r\n\r\n**Local Files**\r\n\r\n![Local Files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/prep_localfiles.png)\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Store & Access HL V2.x Messages\r\n\r\nLet’s start by preparing to load the HL7 V2.x messages into Snowflake. Snowflake supports two types of stages for storing data files used for loading and unloading:\r\n- [Internal stages](https://docs.snowflake.com/en/user-guide/data-load-overview.html#internal-stages) store the files internally within Snowflake.\r\n- [External stages](https://docs.snowflake.com/en/user-guide/data-load-overview.html#external-stages) store the files in an external location (i.e. S3 bucket) that is referenced by the stage. An external stage specifies location and credential information, if required, for the bucket.\r\n\r\n\r\n### Create a Database, Schema, and Warehouse\r\nBefore creating any stages, let’s create a database and a schema that will be used for loading the unstructured data. We will use the UI within the Worksheets tab to run the DDL that creates the database and schema. Copy the commands below into your trial environment, and execute each individually.\r\n\r\n\r\n```sql\r\nuse role sysadmin;\r\n\r\ncreate or replace database hl7db comment = 'HL7 Database';\r\ncreate or replace schema hl7db.hl7v2demo;\r\ncreate or replace warehouse quickstart warehouse_size = 'SMALL' initially_suspended = TRUE auto_suspend=60;\r\n\r\n\r\nuse database hl7db;\r\nuse schema hl7v2demo;\r\nuse warehouse quickstart;\r\n```\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n### Create Snowflake Stages to Ingest and Store HL7 V2.x Messages\r\nStages in Snowflake are locations used to store data. If the data that needs to be loaded into Snowflake is stored in other cloud regions like AWS S3 or Azure or GCP then these are called External stages whereas if the data is stored inside Snowflake then these are called Internal stages.\r\n\r\n**Note:** For the purpose of this lab we are using Internal stage. You can do the same with External Stage.\r\n\r\n\r\n### Store HL7 V2.x messages in an Internal Stage\r\nYou can store data and files directly in Snowflake with internal stages. Now, we want to create an internal stage and upload the files using SnowSQL CLI.\r\n\r\n#### Create an Internal Stage\r\nRun this command to create an internal stage called `hl7_stage_internal` as follows.\r\n\r\n\r\n```sql\r\nuse schema hl7db.hl7v2demo;\r\n-- Directory tables store a catalog of staged files in cloud storage.\r\ncreate or replace stage hl7_stage_internal\r\n   directory = ( enable = TRUE )\r\n   comment = 'used for staging data & libraries';\r\n```\r\n\r\n#### Upload Files using SnowSQL to Internal Stage\r\n\r\nLet's now upload the files downloaded to your workstation to Snowflake internal stage `hl7_stage_internal`. We will use **SnowSQL CLI** and **PUT** command to load all files. Before opening terminal, find out your account identifier which for the trial account will be.\r\n\r\n\r\nFor example, if the URL to access the trial account is `https://xx74264.ca-central-1.aws.snowflakecomputing.com/`. These are the values for the account identifier:\r\n\r\n\r\n- Account identifier: `xx74264`\r\n\r\nThere may be additional segments if you are using your own account part of an organization. You can find those from the URL of your Snowflake account. Please check the [Snowflake Documentation](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers) for additional details on this topic.\r\n\r\nFor your convenience, use the below query to retrieve account identifier.\r\n\r\n```sql\r\n-- retrieve your Snowflake account identifier \r\nselect replace(t.value:host::varchar,'.snowflakecomputing.com') as account_identifier \r\nfrom table(flatten(input =\u003E parse_json(system$allowlist()))) as t\r\nwhere t.value:type::varchar = 'SNOWFLAKE_DEPLOYMENT_REGIONLESS';\r\n\r\n```\r\n\r\nLet's first prepare the snowflake internal stage structure. stage name is `hl7_stage_internal` and we have three sub directories in it `dataset`, `java_stage` and `python_stage`\r\n\r\n\r\n- `dataset`   - contains sample HL7 data files.\r\n- `java_stage`  - contains Java UDF/UDTF related code/dependencies jar file.\r\n- `python_stage`  - contains external HL7apy lib tar file and UDF code.\r\n\r\n\r\n**Internal Stage Files**\r\n\r\nUsing Snowsql upload the files.\r\n\r\n```sql\r\n-- Using a terminal window invoke snowsql.\r\n\r\nsnowsql -a \u003Caccount-identifier\u003E -u \u003Cusername\u003E\r\n\r\n-- set the database/schema/warehouse context\r\nuse database hl7db;\r\nuse schema hl7v2demo;\r\nuse warehouse quickstart;\r\n\r\n-- load sample hl7 data\r\nput file:////\u003Cdownloaded files folder path\u003E/hl7_2-3_samples.txt @hl7_stage_internal/dataset/ auto_compress=false;\r\n\r\n-- load java related code/dependencies jar\r\nput file:////\u003Cdownloaded files folder path\u003E/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar @hl7_stage_internal/java_stage/ auto_compress=false;\r\n\r\n-- load external dependency python lib\r\nput file:////\u003Cdownloaded files folder path\u003E/hl7apy-1.3.4.tar.gz @hl7_stage_internal/python_stage/ auto_compress=false;\r\n\r\n\r\n-- load python UDF code\r\nput file:////\u003Cdownloaded files folder path\u003E/hl7pyparserUDF.py @hl7_stage_internal/python_stage/ auto_compress=false;\r\n\r\n```\r\n\r\n![SnowSQL Put File](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_snowsql_put_files.png)\r\n\r\n\r\n**Refresh Stage**\r\n\r\n```sql\r\n-- ensure to refresh the stage\r\nalter stage hl7_stage_internal refresh;\r\n```\r\n\r\n\r\n![Refresh stage metadata](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_refresh_stage.png)\r\n\r\n\r\n**List Files**\r\n\r\n\r\n```sql\r\n-- run this to verify the files shows up in the list\r\nselect *\r\nfrom directory(@hl7_stage_internal);\r\n```\r\n\r\n\r\n![Put Files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/store_list_directory_table.png)\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Processing HL7 messages using Java UDF & UDTF\r\n\r\nJava UDFs and UDTFs allow workloads expressed in Java to execute in Snowflake and therefore benefit from the virtually unlimited performance and scalability of the Data Cloud.   \r\n\r\n\r\n**Create Java UDTF function**\r\n\r\nA UDTF is a user-defined function (UDF) that returns tabular results. A Java UDTF specifies the Java data types of the output columns by defining an output row class. Each row returned from the UDTF is returned as an instance of the output row class. Each instance of the output row class contains one public field for each output column. Snowflake reads the values of the public fields from each instance of the output row class, converts the Java values to SQL values, and constructs a SQL output row containing those values.\r\n\r\n\r\n#### Creating the Java UDTF\r\nNow lets create the Java UDTF, this function will refer a HAPI library for parsing raw HL7 V2.x messages. The Java UDTF can be defined in Snowflake as below:\r\n\r\n```sql\r\ncreate or replace function hl7_hapi_parser(hl7_fl_url varchar ,validate_message boolean)\r\n   returns table ( \r\n        parsed_status boolean, \r\n        raw_msg varchar, \r\n        hl7_xml varchar,  \r\n        hl7_json variant,  \r\n        message_type varchar, \r\n        message_version varchar, \r\n        message_sequence_num integer, \r\n        error_msg varchar \r\n    )\r\n  language JAVA\r\n  imports = ('@hl7_stage_internal/java_stage/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar')\r\n  handler = 'com.snowflake.labs.hl7.HL7UDTF'\r\n  comment = 'Java based UDTF for parsing HL7v2 files.';\r\n```\r\n***Note: The above 'Create Java UDTF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name \u003Chl7_stage_internal\u003E***\r\n\r\n#### Parsing the HL7 data\r\n\r\n```sql\r\n-- Parse the file and store in a local table\r\n\r\ncreate or replace table hl7v2parsed as \r\n  select * from (\r\n  with base as (\r\n      select \r\n          relative_path as data_filepath,\r\n          concat(build_scoped_file_url('@hl7_stage_internal/', data_filepath)) as full_path\r\n      from directory( @hl7_stage_internal )\r\n      where relative_path like 'dataset/%'\r\n  )\r\n  select \r\n      full_path, p.* \r\n  from base as b\r\n      ,table(hl7_hapi_parser(b.full_path, false) ) as p\r\n);\r\n\r\n-- verify the data inside the table\r\nselect * from hl7v2parsed;\r\n```\r\n\r\n![Java UDTF Result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_table_output.png)\r\n\r\nLets run additional queries to analyze the data parsed.\r\n\r\n```sql\r\n-- Flatten the JSON Field to pull OBSERVATION (OBR) segment fields for one of the ORU messages\r\n\r\nwith base as (\r\n    select hl7_json as msg\r\n    from hl7v2parsed\r\n  where message_sequence_num=1 and message_type='ORU_R01'\r\n), oru_response_sgmt as (\r\n    select \r\n        msg:\"ORU_R01.RESPONSE\" as oru_response\r\n    from base\r\n)\r\n select\r\n    oru_response,\r\n    oru_response:\"ORU_R01.ORDER_OBSERVATION\":\"OBR\":\"OBR.4\" as ob4,\r\n    ob4:\"CE.1\" as ce_1,\r\n    ob4:\"CE.2\"::string as ce_2,\r\n    ob4:\"CE.3\"::string as ce_3\r\n from oru_response_sgmt as b;\r\n```\r\n\r\n![Java UDTF Flatten Result1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_oru_obs.png)\r\n\r\n```sql\r\n-- Flatten the JSON to pull OBSERVATION - Patient Report Text fields\r\nwith base as (\r\n    select hl7_json as msg\r\n    from hl7v2parsed\r\n  where full_path like '%hl7_2-3_samples.txt' and message_type='ORU_R01'\r\n)\r\n select\r\n    msg,\r\n    msg:\"ORU_R01.RESPONSE\":\"ORU_R01.ORDER_OBSERVATION\":\"ORU_R01.OBSERVATION\" as OBSERVATIONS,\r\n    obx.value:\"OBX\":\"OBX.5\" as PATIENT_REPORT_TEXT\r\n from base,table(flatten(msg:\"ORU_R01.RESPONSE\":\"ORU_R01.ORDER_OBSERVATION\":\"ORU_R01.OBSERVATION\")) obx ;\r\n```\r\n![Java UDTF Flatten Result2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_patient_report_text.png)\r\n\r\n```sql\r\n-- List out patient and physician interactions\r\n\r\nwith base as (\r\n    select hl7_json \r\n    from hl7v2parsed\r\n    where message_type = 'ORU_R01'\r\n)\r\n,patient_physician as (\r\n    select \r\n        concat(\r\n            f.value:\"ORU_R01.PATIENT\":\"ORU_R01.VISIT\":\"PV1\":\"PV1.7\":\"XCN.2\"::string, ' '\r\n            ,f.value:\"ORU_R01.PATIENT\":\"ORU_R01.VISIT\":\"PV1\":\"PV1.7\":\"XCN.3\"::string) as physician\r\n        ,concat(\r\n            f.value:\"ORU_R01.PATIENT\":\"PID\":\"PID.5\":\"XPN.1\"::string, ' '\r\n            ,f.value:\"ORU_R01.PATIENT\":\"PID\":\"PID.5\":\"XPN.2\"::string, ' '\r\n            ,f.value:\"ORU_R01.PATIENT\":\"PID\":\"PID.5\":\"XPN.3\"::string, ' '\r\n        ) as patient\r\n    from base as b\r\n        ,lateral flatten(input =\u003E hl7_json) as f\r\n    where key like 'ORU_R01.RESPONSE'\r\n    and patient is not null\r\n)\r\nselect\r\n    patient, physician\r\n    ,count(*) as number_of_visits\r\nfrom patient_physician\r\ngroup by patient, physician\r\norder by number_of_visits desc;\r\n```\r\n\r\n![Java UDTF Flatten Result3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/java_udtf_flatten_patient_physicians.png)\r\n\r\n**Create Java UDF function**\r\n\r\nA UDF (user-defined function) is a user-written function that can be called from Snowflake in the same way that a built-in function can be called.When a user calls a UDF, the user passes the name of the UDF and the parameters of the UDF to Snowflake. If the UDF is a Java UDF, Snowflake calls the appropriate Java code (called a handler method) in a JAR file. The handler method then returns the output to Snowflake, which passes it back to the client.\r\n\r\n#### Creating the Java UDF\r\nIdeally you would be using a Kafka connector, Snowpipe or other mechanisms to load the HL7 V2.x messages to Snowflake and then execute Java UDF for each raw HL7 V2.x message. For this lab we will simulate this by leveraging the HL7 V2.x raw messages loaded into the raw_msg column of hl7v2parsed table created in the previous section.\r\n\r\nNow lets create the Java UDF, this function will refer a HAPI library for parsing raw HL7 V2.x messages. The Java UDF can be defined in Snowflake as below:\r\n\r\n```sql\r\ncreate or replace function hl7_hapi_udf_parser(hl7_msg varchar ,validate_message boolean)\r\nreturns variant\r\nlanguage java\r\nimports = ('@hl7_stage_internal/java_stage/sf-hl7v2-parser-1.0-SNAPSHOT-jar-with-dependencies.jar')\r\nhandler = 'com.snowflake.labs.hl7.HL7UDF.process'\r\ncomment = 'Java based UDF for parsing HL7v2 messages.';\r\n``` \r\n\r\n***Note: The above 'Create Java UDF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name \u003Chl7_stage_internal\u003E***\r\n\r\n#### Parsing the HL7 data\r\n\r\nThe previously loaded raw_hl7_table has each record as a single hl7v2 message in the raw_msg column.\r\nThe following query invokes the function, to parse the HL7 message file :\r\n\r\n```sql\r\nwith base as (\r\n    select\r\n        raw_msg, -- column holding the HL7v2.x message in pipe delimited format\r\n        hl7_hapi_udf_parser(raw_msg, false) as parsed\r\n    from hl7v2parsed\r\n    limit 10\r\n)\r\nselect\r\n    parse_json(parsed:\"hl7_json\") as hl7_json\r\nfrom base;\r\n```\r\n\r\nThe above query examples clearly demonstrates the beauty and simplicity of Snowflake's capabilities to parse and analyze the HL7 V2.x messages.\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Processing HL7 messages using Python UDF \r\n\r\nUser-defined functions (UDFs) let you extend the system to perform operations that are not available through the built-in, system-defined functions provided by Snowflake. Python UDFs (user-defined functions) allow you to write Python code and call it as though it were a SQL function\r\n\r\n\r\n**Create Python UDF function**\r\n\r\nLet's create a Python UDF function, this function will refer a hl7apy library for parsing raw messages. Using `imports` we can pass external library and python code. After executing `hl7pyparser` it will return a JSON messages and here in this example snowflake `variant` data type has been used to store JSON message.\r\n\r\n\r\n```sql\r\ncreate or replace function hl7pyparser(hl7_raw string)\r\nreturns variant\r\nlanguage python\r\nruntime_version = 3.8\r\nimports=('@hl7_stage_internal/python_stage/hl7apy-1.3.4.tar.gz', '@hl7_stage_internal/python_stage/hl7pyparserUDF.py')\r\nhandler = 'hl7pyparserUDF.udf'\r\ncomment = 'python based hl7v2 message parser';\r\n```\r\n***Note: The above 'Create Python UDF' function is implemented using snowflake internal stage, the same definition can be used for external stage by replacing the stage name \u003Chl7_stage_internal\u003E***\r\n\r\n**File Format**\r\n\r\nWe will create a Snowflake FILE FORMAT to read the RAW data directly from the stage, more details about snowflake file format can be found here - [FILE FORMAT](https://docs.snowflake.com/en/sql-reference/sql/create-file-format.html#create-file-format)\r\n\r\n\r\n```sql\r\nCREATE FILE FORMAT \"HL7DB\".\"HL7V2DEMO\".\"RAW_HL7_FILE_FORMAT\"\r\n   TYPE = 'CSV'\r\n   COMPRESSION = 'NONE'\r\n   FIELD_DELIMITER = 'NONE'\r\n   RECORD_DELIMITER = 'n'\r\n   SKIP_HEADER = 0\r\n   FIELD_OPTIONALLY_ENCLOSED_BY = 'NONE'\r\n   TRIM_SPACE = FALSE\r\n   ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE\r\n   ESCAPE = 'NONE'\r\n   ESCAPE_UNENCLOSED_FIELD = '134'\r\n   DATE_FORMAT = 'AUTO'\r\n   TIMESTAMP_FORMAT = 'AUTO' NULL_IF = ('\\N');\r\n```\r\n\r\n\r\nLet's verify that our FILE FORMAT is correct and we are able to read files from stage. For now we will read raw message into single Column `RAW_MSG`.\r\n\r\n\r\nRemember our data is stored inside dataset sub directory and we will aslo add pattern for reading only text files.\r\n\r\n\r\n**Reading from stage**\r\n\r\n```sql\r\nselect t.$1 as RAW_MSG from @hl7_stage_internal/dataset\r\n   (file_format =\u003E 'RAW_HL7_FILE_FORMAT', pattern=\u003E'.*.*[.]txt') t\r\n   where RAW_MSG is not NULL;\r\n```\r\n\r\n\r\nLet's create a separate temporary table to store this raw data `raw`\r\n\r\n\r\n```sql\r\nCREATE TEMPORARY TABLE raw\r\nAS SELECT t.$1 AS RAW_MSG FROM @hl7_stage_internal/dataset\r\n   (file_format =\u003E 'RAW_HL7_FILE_FORMAT', pattern=\u003E'.*.*[.]txt') t\r\n   WHERE RAW_MSG IS NOT NULL;\r\n\r\n\r\n-- verify data inside raw table\r\nselect * from raw;\r\n```\r\n![Tmp Raw](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/tmp_raw.png)\r\n\r\n\r\n**Use UDF function**\r\n\r\n\r\nFinally, let's use our UDF function `hl7pyparser` that we created  earlier.\r\n\r\n\r\n```sql\r\nselect\r\n   raw_msg,\r\n   parse_json(hl7pyparser(raw_msg)) as JSON_MSG,\r\n   parse_json(hl7pyparser(raw_msg)):msh:message_type:message_type:id as SRC\r\nfrom raw\r\nwhere src = 'ORU';\r\n```\r\n\r\n\r\n`raw_msg`\r\n\r\n\r\n- Column contains RAW HL7 messages which we read earlier from the stage and from `raw` table\r\n\r\n\r\n`parse_json(hl7pyparser(raw_msg)) as JSON_MSG`\r\n\r\n\r\n- **parse_json** function will read the RAW HL7 messages from RAW_MSG column and it will convert it to JSON\r\n\r\n\r\n`parse_json(hl7pyparser(raw_msg)):msh:message_type:message_type:id as SRC`\r\n\r\n\r\n- To further flatten or parse the json column, we will use colon (`:`) between keys.\r\n\r\n\r\nFor example - `msh` is a root element in json string and `message_type` and `id` are nested keys (Inner elements in json)\r\n\r\n\r\n![UDF Output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/processing-hl7-v2-messages-with-snowflake/udf_output_table.png)\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion\r\n\r\n\r\nCongratulations! You used Snowflake to trasform your HL7 V2.x messages using Snowflake Java UDFs/UDTFs and Python UDFs.\r\n\r\n\r\n### What we've covered\r\n- Storing HL7 V2.x messages with an __Internal Stage__ and __SnowSQL__\r\n- Catalog HL7 V2.x messages data with __Directory Tables__\r\n- Processing HL7 V2.x messages with a __Java UDF__ and __Java UDTF__\r\n- Processing HL7 V2.x messages with a __Python UDF__ \r\n- Processing JSON HL7 data with __parse_json__ function\r\n\r\n\r\n### Related Resources\r\n- [Unstructured Data Docs](https://docs.snowflake.com/en/user-guide/unstructured.html)\r\n- [Java UDF Docs](https://docs.snowflake.com/en/developer-guide/udf/java/udf-java.html)\r\n- [Java UDTF Docs](https://docs.snowflake.com/en/developer-guide/udf/java/udf-java-tabular-functions.html#tabular-java-udfs-udtfs)\r\n- [Python UDF Docs](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-introduction.html#introduction-to-python-udfs)\r\n- [Python UDTF Docs](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-tabular-functions.html#implementing-user-defined-table-functions-udtfs-in-python)\r\n- [Snowpark Docs](https://docs.snowflake.com/en/developer-guide/snowpark/index.html)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f2425465bb","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":{"layout":"SIMPLE","id":"container-2d5fc3e83b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-677d12775f","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2023-09-18",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-3dd5a43cb4","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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