{"id":15098,"date":"2026-09-17T10:15:58","date_gmt":"2026-09-17T14:15:58","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/minervalab\/?page_id=15098"},"modified":"2026-09-17T10:26:16","modified_gmt":"2026-09-17T14:26:16","slug":"air%c2%b7ms-benchmarking","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/minervalab\/air%c2%b7ms-benchmarking\/","title":{"rendered":"AIR\u00b7MS Benchmarking"},"content":{"rendered":"

[et_pb_section bb_built=”1″ inner_width=”auto” inner_max_width=”1080px”][et_pb_row][et_pb_column type=”4_4″ custom_padding__hover=”|||” custom_padding=”|||”][et_pb_text _builder_version=”4.27.4″ background_pattern_color=”rgba(0,0,0,0.2)” background_mask_color=”#ffffff” text_text_shadow_horizontal_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength=”text_text_shadow_style,%91object Object%93″ text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length=”link_text_shadow_style,%91object Object%93″ link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length=”link_text_shadow_style,%91object Object%93″ link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength=”link_text_shadow_style,%91object Object%93″ link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length=”header_text_shadow_style,%91object Object%93″ header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length=”header_text_shadow_style,%91object Object%93″ header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength=”header_text_shadow_style,%91object Object%93″ header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ z_index_tablet=”0″]<\/p>\n

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Benchmarking<\/strong><\/span><\/h1>\n
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We made performance comparisons for the SAP HANA system at AIR\u00b7MS comparing:<\/p>\n

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  • Execution time<\/li>\n
  • Search time<\/li>\n
  • Data compression ratio<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
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    Execution Time Performance Benchmark<\/strong><\/span><\/h2>\n

    Here you can find a performance comparison between AIR\u00b7MS using SAP HANA and Microsoft SQL Server.
    The cohort record count for both was 985,092 records.<\/p>\n\n\n\n\n\n\n\n\n
    \u00a0<\/td>\nAIR\u00b7MS on SAP HANA<\/strong><\/td>\n\u00a0Microsoft SQL Server<\/strong><\/td>\n<\/tr>\n
    Database Systems<\/td>\nSAP HANA 2.0 SPS05 on Azure Standard M64s (64 <\/span>vCPUs<\/span> with 1024 GiB memory)<\/span><\/span>\u00a0<\/span><\/td>\nMicrosoft SQL Server Enterprise 2019 Azure Standard L32s v2 (32 <\/span>v<\/span>CPUs <\/span>with 256 GiB memory)<\/span><\/span>\u00a0<\/span><\/td>\n<\/tr>\n
    First Run<\/td>\n174 ms<\/span> 268 \u00b5s<\/span><\/td>\n5 minutes 29 seconds<\/span><\/span>\u00a0<\/span><\/td>\n<\/tr>\n
    Second Run<\/td>\n173 <\/span>ms<\/span> 384 \u00b5s<\/span><\/span>\u00a0<\/span><\/td>\n5 minutes <\/span>36<\/span> seconds<\/span><\/span>\u00a0<\/span><\/td>\n<\/tr>\n
    Third Run<\/td>\n172 <\/span>ms<\/span> 193 \u00b5s<\/span><\/span>\u00a0<\/span><\/td>\n5 minutes <\/span>36<\/span> seconds<\/span><\/span>\u00a0<\/span><\/td>\n<\/tr>\n
    Average <\/span>execution <\/span>time<\/span><\/span>\u00a0<\/span><\/strong><\/td>\n173 <\/span>ms<\/span> 281 \u00b5s<\/span><\/span>\u00a0<\/span><\/strong><\/td>\n5 minutes 33 seconds<\/span><\/span>\u00a0<\/span><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Example SQL code used to build patient cohort in AIR\u00b7MS<\/h4>\n
    select<\/span> count(<\/span>*) from (<\/span><\/span>\u00a0<\/span>
    <\/span>select<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 CDMPHI.CONDITION_OCCURRENCE.PERSON_ID,<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 <\/span>CDMPHI.PERSON.GENDER<\/span>_SOURCE_VALUE as gender,<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 <\/span>CDMPHI.PERSON.RACE<\/span>_SOURCE_VALUE as race,<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 <\/span>CDMPHI.CONCEPT.CONCEPT<\/span>_NAME as diagnosis,<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 <\/span>CDMPHI.CONCEPT.CONCEPT<\/span>_CODE as icd10,<\/span><\/span>\u00a0<\/span>
    <\/span>\u00a0\u00a0\u00a0 CDMPHI.CONDITION_OCCURRENCE.CONDITION_START_DATE as <\/span>dx_date<\/span><\/span>\u00a0<\/span>
    <\/span>from CDMPHI.CONDITION_OCCURRENCE<\/span><\/span>\u00a0<\/span>
    <\/span>join CDMPHI.CONCEPT_RELATIONSHIP on CDMPHI.CONCEPT_RELATIONSHIP.CONCEPT_ID_2 = CDMPHI.CONDITION_OCCURRENCE.CONDITION_CONCEPT_ID<\/span><\/span>\u00a0<\/span>
    <\/span>join CDMPHI.CONCEPT on <\/span>CDMPHI.CONCEPT.CONCEPT<\/span>_ID = CDMPHI.CONCEPT_RELATIONSHIP.CONCEPT_ID_1<\/span><\/span>\u00a0<\/span>
    <\/span>join CDMPHI.PERSON on <\/span>CDMPHI.PERSON.PERSON<\/span>_ID = CDMPHI.CONDITION_OCCURRENCE.PERSON_ID<\/span><\/span>\u00a0<\/span>
    <\/span>where CDMPHI.CONCEPT_RELATIONSHIP.RELATIONSHIP_ID = \u2018Maps to\u2019<\/span><\/span>\u00a0<\/span>
    <\/span>and <\/span>CDMPHI.CONCEPT.VOCABULARY<\/span>_ID like \u2018%ICD10%\u2019<\/span><\/span>\u00a0<\/span>
    <\/span>and <\/span>CDMPHI.CONCEPT.CONCEPT<\/span>_CODE like \u2018C34%\u2019) as count<\/span><\/span>\u00a0<\/span><\/pre>\n<\/div>\n<\/div>\n
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    In-memory Search Benchmark<\/strong><\/span><\/h2>\n
    The following examples show execution time benchmarks for our SAP HANA system.<\/p>\n<\/div>\n<\/div>\n
    AIR.MS in-memory full-text search performance examples<\/span><\/strong><\/h2>\n<\/div>\n
    Number of indexed notes: 86,893,767<\/pre>\n
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    Example 1:<\/strong>\u00a0Show\u00a0count\u00a0<\/strong>for notes containing an\u00a0EXACT\u00a0<\/strong>search string ‘acute kidney disease<\/strong>‘, broken down by\u00a0gender<\/strong><\/span><\/span><\/h3>\n<\/div>\n
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    \u00a0<\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    \u00a0<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    0.31\u00a0second runtime!<\/strong><\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    Example\u00a02:<\/strong>\u00a0Show\u00a0count\u00a0<\/strong>for\u00a0notes\u00a0containing\u00a0FUZZY\u00a0<\/strong>search\u00a0string\u00a0‘acute\u00a0kidney\u00a0disease<\/strong>‘, broken\u00a0down\u00a0by\u00a0gender<\/strong><\/span><\/span><\/h3>\n<\/div>\n
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    \"\"<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    \u00a0<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    0.39\u00a0second runtime!<\/strong><\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    Example 3:<\/strong>%<\/p>\n","protected":false},"excerpt":{"rendered":"
    \n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div> BenchmarkingWe made performance comparisons for the SAP HANA system at AIR\u00b7MS comparing:Execution timeSearch timeData compression ratioExecution Time Performance BenchmarkHere you can find a performance comparison between AIR\u00b7MS using SAP HANA and Microsoft SQL Server.The cohort record count for both was 985,092 records.\u00a0AIR\u00b7MS on SAP HANA\u00a0Microsoft SQL ServerDatabase SystemsSAP HANA 2.0 SPS05 on Azure Standard […]<\/p>\n","protected":false},"author":699,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-15098","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/15098","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/users\/699"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/comments?post=15098"}],"version-history":[{"count":7,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/15098\/revisions"}],"predecessor-version":[{"id":15106,"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/pages\/15098\/revisions\/15106"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-json\/wp\/v2\/media?parent=15098"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}