{"id":47193,"date":"2022-11-21T18:20:16","date_gmt":"2024-03-08T17:19:03","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8delta-live-tables%e7%9a%84%e5%8f%98%e6%9b%b4%e6%95%b0%e6%8d%ae%e6%8d%95%e8%8e%b7%ef%bc%88cdc%ef%bc%89%e3%80%82\/"},"modified":"2024-04-29T01:49:20","modified_gmt":"2024-04-28T17:49:20","slug":"%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8delta-live-tables%e7%9a%84%e5%8f%98%e6%9b%b4%e6%95%b0%e6%8d%ae%e6%8d%95%e8%8e%b7%ef%bc%88cdc%ef%bc%89%e3%80%82","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e4%bd%bf%e7%94%a8delta-live-tables%e7%9a%84%e5%8f%98%e6%9b%b4%e6%95%b0%e6%8d%ae%e6%8d%95%e8%8e%b7%ef%bc%88cdc%ef%bc%89%e3%80%82\/","title":{"rendered":"\u5c1d\u8bd5\u4f7f\u7528Delta Live Tables\u7684\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08CDC\uff09"},"content":{"rendered":"

\u6211\u4f1a\u6d4f\u89c8\u8fd9\u91cc\u53d1\u5e03\u7684\u7b14\u8bb0\u672c\u3002\u751f\u6210\u53d1\u751f\u53d8\u5316\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528Delta Live Tables\uff08DLT\uff09\u7684\u66f4\u6539\u6570\u636e\u6355\u83b7\uff08CDC\uff09\u5c06\u4ec5\u53d8\u66f4\u53cd\u6620\u5230\u540e\u7eed\u8868\u683c\u4e2d\u3002<\/p>\n

 <\/p>\n

\u4ee5\u4e0b\u662f\u7ffb\u8bd1\u7248\u672c\u3002\u6211\u4eec\u53ea\u7ffb\u8bd1\u4e86Python\u7248\u672c\u7684\u7ba1\u9053\u7b14\u8bb0\u672c\u3002<\/p>\n

 <\/p>\n

\u6570\u636e\u51c6\u5907<\/h1>\n

\u4f7f\u7528Faker\u751f\u6210CDC\u7684\u57fa\u7840\u6570\u636e\u3002<\/p>\n

 <\/p>\n

Faker\u7684\u5b89\u88c5<\/h2>\n
%pip install Faker\r\n<\/code><\/pre>\n
\u751f\u6210\u865a\u62df\u6570\u636e<\/h2>\n
from<\/span> pyspark.sql<\/span> import<\/span> functions<\/span> as<\/span> F<\/span>\r\nfrom<\/span> faker<\/span> import<\/span> Faker<\/span>\r\nfrom<\/span> collections<\/span> import<\/span> OrderedDict<\/span> \r\nimport<\/span> uuid<\/span>\r\n\r\n# \u30c7\u30fc\u30bf\u306e\u683c\u7d0d\u30d1\u30b9: \u9069\u5b9c\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044\r\n<\/span>folder<\/span> =<\/span> \"<\/span>\/tmp\/takaaki.yayoi@databricks.com\/demo\/cdc_raw<\/span>\"<\/span>\r\n#dbutils.fs.rm(folder, True)\r\n<\/span>try<\/span>:<\/span>\r\n  dbutils<\/span>.<\/span>fs<\/span>.<\/span>ls<\/span>(<\/span>folder<\/span>)<\/span>\r\nexcept<\/span>:<\/span>\r\n  print<\/span>(<\/span>\"<\/span>\u30d5\u30a9\u30eb\u30c0\u30fc\u304c\u5b58\u5728\u3057\u307e\u305b\u3093\u3001\u30c7\u30fc\u30bf\u3092\u751f\u6210\u4e2d<\/span>...<\/span>\"<\/span>)  \r\n  fake = Faker()\r\n  fake_firstname = F.udf(fake.first_name)\r\n  fake_lastname = F.udf(fake.last_name)\r\n  fake_email = F.udf(fake.ascii_company_email)\r\n  fake_date = F.udf(lambda:fake.date_time_this_month().strftime(<\/span>\"<\/span>%<\/span>m<\/span>-%<\/span>d<\/span>-%<\/span>Y<\/span> %<\/span>H<\/span>:<\/span>%<\/span>M<\/span>:<\/span>%<\/span>S<\/span>\"<\/span>))\r\n  fake_address = F.udf(fake.address)\r\n  operations = OrderedDict([(<\/span>\"<\/span>APPEND<\/span>\"<\/span>, 0.5),(<\/span>\"<\/span>DELETE<\/span>\"<\/span>, 0.1),(<\/span>\"<\/span>UPDATE<\/span>\"<\/span>, 0.3),(None, 0.01)])\r\n  fake_operation = F.udf(lambda:fake.random_elements(elements=operations, length=1)[0])\r\n  fake_id = F.udf(lambda: str(uuid.uuid4()))\r\n\r\n  df = spark.range(0, 100000)\r\n  df = df.withColumn(<\/span>\"<\/span>id<\/span>\"<\/span>, fake_id())\r\n  df = df.withColumn(<\/span>\"<\/span>firstname<\/span>\"<\/span>, fake_firstname())\r\n  df = df.withColumn(<\/span>\"<\/span>lastname<\/span>\"<\/span>, fake_lastname())\r\n  df = df.withColumn(<\/span>\"<\/span>email<\/span>\"<\/span>, fake_email())\r\n  df = df.withColumn(<\/span>\"<\/span>address<\/span>\"<\/span>, fake_address())\r\n  df = df.withColumn(<\/span>\"<\/span>operation<\/span>\"<\/span>, fake_operation())\r\n  df = df.withColumn(<\/span>\"<\/span>operation_date<\/span>\"<\/span>, fake_date())\r\n\r\n  df.repartition(100).write.format(<\/span>\"<\/span>json<\/span>\"<\/span>).mode(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).save(folder+<\/span>\"<\/span>\/<\/span>customers<\/span>\"<\/span>)\r\n  \r\n  df = spark.range(0, 10000)\r\n  df = df.withColumn(<\/span>\"<\/span>id<\/span>\"<\/span>, fake_id())\r\n  df = df.withColumn(<\/span>\"<\/span>transaction_date<\/span>\"<\/span>, fake_date())\r\n  df = df.withColumn(<\/span>\"<\/span>amount<\/span>\"<\/span>, F.round(F.rand()*1000))\r\n  df = df.withColumn(<\/span>\"<\/span>item_count<\/span>\"<\/span>, F.round(F.rand()*10))\r\n  df = df.withColumn(<\/span>\"<\/span>operation<\/span>\"<\/span>, fake_operation())\r\n  df = df.withColumn(<\/span>\"<\/span>operation_date<\/span>\"<\/span>, fake_date())\r\n\r\n  # \u540c\u3058ID\u304c\u751f\u6210\u3055\u308c\u305f\u9867\u5ba2\u3068Join\r\n  df = df.withColumn(<\/span>\"<\/span>t_id<\/span>\"<\/span>, F.monotonically_increasing_id()).join(spark.read.json(folder+<\/span>\"<\/span>\/<\/span>customers<\/span>\"<\/span>).select(<\/span>\"<\/span>id<\/span>\"<\/span>).withColumnRenamed(<\/span>\"<\/span>id<\/span>\"<\/span>, <\/span>\"<\/span>customer_id<\/span>\"<\/span>).withColumn(<\/span>\"<\/span>t_id<\/span>\"<\/span>, F.monotonically_increasing_id()), <\/span>\"<\/span>t_id<\/span>\"<\/span>).drop(<\/span>\"<\/span>t_id<\/span>\"<\/span>)\r\n  df.repartition(10).write.format(<\/span>\"<\/span>json<\/span>\"<\/span>).mode(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).save(folder+<\/span>\"<\/span>\/<\/span>transactions<\/span>\"<\/span>)\r\n<\/span><\/code><\/pre>\n
\u786e\u8ba4\u6570\u636e<\/h2>\n
spark<\/span>.<\/span>read<\/span>.<\/span>json<\/span>(<\/span>folder<\/span>+<\/span>\"<\/span>\/customers<\/span>\"<\/span>).<\/span>display<\/span>()<\/span>\r\n<\/code><\/pre>\n
\"Screen<\/div>\n
\u7ba1\u9053\u7684\u521d\u59cb\u914d\u7f6e\u8bbe\u7f6e<\/h2>\n
\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u521d\u59cb\u5316\u7528\u4e8eDelta Live Tables\u7ba1\u9053\u7684\u6570\u636e\u5e93\u548c\u6587\u4ef6\u8def\u5f84\u3002<\/p>\n
-- \u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4f5c\u6210: \u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u30bf\u30fc\u30b2\u30c3\u30c8\u306b\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044<\/span>\r\nDROP<\/span> DATABASE<\/span> IF<\/span> EXISTS<\/span> cdc_data_taka<\/span> CASCADE<\/span>;<\/span>\r\nCREATE<\/span> DATABASE<\/span> cdc_data_taka<\/span>;<\/span>\r\n<\/code><\/pre>\n
# DLT\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u30b9\u30c8\u30ec\u30fc\u30b8\u306e\u521d\u671f\u5316: \u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u30b9\u30c8\u30ec\u30fc\u30b8\u306b\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\r\n<\/span>dbutils<\/span>.<\/span>fs<\/span>.<\/span>rm<\/span>(<\/span>\"<\/span>\/tmp\/takaaki.yayoi@databricks.com\/demo\/dlt_cdc<\/span>\"<\/span>,<\/span> True<\/span>)<\/span>\r\n<\/code><\/pre>\n
CDC\u7ba1\u7ebf\u7684\u5b9e\u65bd\u548c\u6267\u884c\u3002<\/h1>\n
 <\/p>\n
\u6574\u4e2a\u6d41\u7a0b\u5982\u4e0b\u6240\u793a\u3002<\/p>\n
\"\"<\/div>\n
CDC\u7684\u91cd\u8981\u6027<\/h2>\n
Change Data Capture\uff08CDC\uff09\u662f\u6355\u83b7\u6570\u636e\u5e93\u6216\u6570\u636e\u4ed3\u5e93\u4e2d\u8bb0\u5f55\u66f4\u6539\u7684\u8fc7\u7a0b\u3002\u8fd9\u4e9b\u66f4\u6539\u901a\u5e38\u88ab\u89c6\u4e3a\u5220\u9664\u3001\u6dfb\u52a0\u548c\u66f4\u65b0\u7b49\u64cd\u4f5c\u3002<\/p>\n
\u4ece\u6570\u636e\u5e93\u4e2d\u5bfc\u51fa\u6570\u636e\u5e93\u8f6c\u50a8\uff0c\u7136\u540e\u5c06\u5176\u5bfc\u5165\u5230Lakehouse\/\u6570\u636e\u4ed3\u5e93\/\u6570\u636e\u6e56\u4e2d\u662f\u4e00\u79cd\u7b80\u5355\u7684\u6570\u636e\u590d\u5236\u65b9\u6cd5\uff0c\u4f46\u8fd9\u5e76\u4e0d\u662f\u53ef\u6269\u5c55\u7684\u65b9\u6cd5\u3002<\/p>\n
\u53ea\u6355\u6349\u6570\u636e\u5e93\u4e2d\u7684\u66f4\u6539\u5e76\u5c06\u8fd9\u4e9b\u66f4\u6539\u5e94\u7528\u4e8e\u76ee\u6807\u6570\u636e\u5e93\u624d\u662f\u53d8\u66f4\u6570\u636e\u6355\u6349\uff08CDC\uff09\u3002CDC\u53ef\u4ee5\u51cf\u5c11\u5f00\u9500\u5e76\u652f\u6301\u5b9e\u65f6\u5206\u6790\u3002\u5b83\u53ef\u4ee5\u5b9e\u73b0\u589e\u91cf\u52a0\u8f7d\u800c\u65e0\u9700\u8fdb\u884c\u6279\u91cf\u52a0\u8f7d\u3002<\/p>\n
\u75be\u63a7\u4e2d\u5fc3\u7684\u65b9\u6cd5<\/h2>\n
1 – \u5f00\u53d1\u5185\u90e8\u5236\u9020CDC\u6d41\u7a0b\u3002<\/p>\n
\u590d\u6742\u7684\u4efb\u52a1\uff1aCDC\u6570\u636e\u590d\u5236\u5e76\u4e0d\u662f\u4e00\u6b21\u6027\u7b80\u5355\u7684\u89e3\u51b3\u65b9\u6848\u3002\u7531\u4e8e\u6570\u636e\u5e93\u63d0\u4f9b\u5546\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u8bb0\u5f55\u683c\u5f0f\u4e0d\u540c\uff0c\u4e14\u8bbf\u95ee\u65e5\u5fd7\u8bb0\u5f55\u4e0d\u4fbf\uff0c\u56e0\u6b64CDC\u53d8\u5f97\u56f0\u96be\u3002<\/p>\n
\u5b9a\u671f\u7ef4\u62a4\uff1a\u7f16\u5199CDC\u8fc7\u7a0b\u811a\u672c\u662f\u7b2c\u4e00\u6b65\u3002\u5fc5\u987b\u5bf9\u4e0a\u8ff0\u53d8\u5316\u8fdb\u884c\u5b9a\u671f\u6620\u5c04\u7684\u5b9a\u5236\u89e3\u51b3\u65b9\u6848\u9700\u8981\u8fdb\u884c\u7ef4\u62a4\uff0c\u8fd9\u9700\u8981\u5927\u91cf\u65f6\u95f4\u548c\u8d44\u6e90\u3002<\/p>\n
\u8fc7\u5ea6\u8d1f\u62c5\uff1a\u4f01\u4e1a\u7684\u5f00\u53d1\u4eba\u5458\u5df2\u7ecf\u9762\u4e34\u7740\u5bf9\u5b98\u65b9\u67e5\u8be2\u7684\u8fc7\u5206\u4f9d\u8d56\u3002\u6784\u5efa\u81ea\u5b9a\u4e49CDC\u89e3\u51b3\u65b9\u6848\u9700\u8981\u989d\u5916\u7684\u5de5\u4f5c\u91cf\uff0c\u8fd9\u53ef\u80fd\u4f1a\u5bf9\u6b63\u5728\u4ea7\u751f\u5229\u6da6\u7684\u9879\u76ee\u4ea7\u751f\u5f71\u54cd\u3002<\/p>\n
2 – CDC\u5de5\u5177\u7684\u8fd0\u7528\uff1aDebezium\u3001Hevo Data\u3001IBM Infosphere\u3001Qlik Replicate\u3001Talend\u3001Oracle GoldenGate\u3001StreamSets\u7b49\u3002<\/p>\n
\u5728\u8fd9\u4e2a\u6f14\u793a\u4ee3\u7801\u5e93\u4e2d\uff0c\u5c06\u5229\u7528CDC\u5de5\u5177\u4f20\u9001\u8fc7\u6765\u7684CDC\u6570\u636e\u3002CDC\u5de5\u5177\u80fd\u591f\u8bfb\u53d6\u6570\u636e\u5e93\u65e5\u5fd7\uff0c\u56e0\u6b64\u5728\u66f4\u65b0\u7279\u5b9a\u5217\u65f6\u4e0d\u9700\u8981\u5f00\u53d1\u8005\u7684\u5e2e\u52a9\u3002<\/p>\n
\u50cfDebezium\u8fd9\u6837\u7684CDC\u5de5\u5177\u53ef\u4ee5\u6355\u83b7\u6240\u6709\u5df2\u66f4\u6539\u7684\u884c\u3002\u5728Kafka\u65e5\u5fd7\u4e2d\uff0c\u5e94\u7528\u7a0b\u5e8f\u4f1a\u8bb0\u5f55\u81ea\u5f00\u59cb\u4f7f\u7528\u4ee5\u6765\u7684\u6570\u636e\u66f4\u6539\u5386\u53f2\u3002<\/p>\n
\u5982\u4f55\u5c06\u60a8\u7684SQL\u6570\u636e\u5e93\u4e0eLakehouse\u540c\u6b65\uff1f<\/h2>\n
\u4f7f\u7528CDC\u5de5\u5177\u3001\u81ea\u52a8\u52a0\u8f7d\u7a0b\u5e8f\u3001DLT\u7ba1\u9053\u6765\u5b9e\u65bdCDC\u6d41\u7a0b\u3002<\/p>\n
    \n
  • \n
      CDC\u30c4\u30fc\u30eb\u304c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30ed\u30b0\u3092\u8aad\u307f\u8fbc\u307f\u3001\u5909\u66f4\u3092\u542b\u3080JSON\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u751f\u6210\u3057\u3001Kafka\u306b\u5bfe\u3057\u3066\u5909\u66f4\u8aac\u660e\u3092\u4f34\u3046\u30ec\u30b3\u30fc\u30c9\u3092\u30b9\u30c8\u30ea\u30fc\u30df\u30f3\u30b0<\/ul>\n<\/li>\n<\/ul>\n
       <\/p>\n
        \n
      • \n
          Kafka\u304cINSERT, UPDATE, DELETE\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u542b\u3080\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u30b9\u30c8\u30ea\u30fc\u30df\u30f3\u30b0\u3057\u3001\u30af\u30e9\u30a6\u30c9\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u30b9\u30c8\u30ec\u30fc\u30b8(S3\u3001ADLS\u306a\u3069)\u306b\u683c\u7d0d<\/ul>\n<\/li>\n<\/ul>\n
           <\/p>\n
            \n
          • \n
              Auto Loader\u3092\u7528\u3044\u3066\u30af\u30e9\u30a6\u30c9\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u30b9\u30c8\u30ec\u30fc\u30b8\u304b\u3089\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u30a4\u30f3\u30af\u30ea\u30e1\u30f3\u30bf\u30eb\u306b\u30ed\u30fc\u30c9\u3057\u3001\u751f\u306e\u30e1\u30c3\u30bb\u30fc\u30b8\u3068\u3057\u3066\u4fdd\u5b58\u3059\u308b\u305f\u3081\u306b\u30d6\u30ed\u30f3\u30ba\u30c6\u30fc\u30d6\u30eb\u306b\u683c\u7d0d<\/ul>\n<\/li>\n<\/ul>\n
               <\/p>\n
                \u6b21\u306b\u3001\u30af\u30ec\u30f3\u30b8\u30f3\u30b0\u3055\u308c\u305f\u30d6\u30ed\u30f3\u30ba\u30ec\u30a4\u30e4\u30fc\u30c6\u30fc\u30d6\u30eb\u306b APPLY CHANGES INTO \u3092\u5b9f\u884c\u3057\u3001\u5f8c\u6bb5\u306e\u30b7\u30eb\u30d0\u30fc\u30c6\u30fc\u30d6\u30eb\u306b\u6700\u65b0\u306e\u66f4\u65b0\u30c7\u30fc\u30bf\u3092\u4f1d\u642c<\/ul>\n
                \u4ee5\u4e0b\u662f\u5904\u7406\u6765\u81ea\u5916\u90e8\u6570\u636e\u5e93\u7684CDC\u6570\u636e\u7684\u5b9e\u73b0\u3002\u8bf7\u6ce8\u610f\u8f93\u5165\u53ef\u4ee5\u662f\u4efb\u4f55\u683c\u5f0f\uff0c\u5305\u62ec\u50cfKafka\u8fd9\u6837\u7684\u6d88\u606f\u961f\u5217\u3002<\/p>\n
                \"Make<\/div>\n
                \u50cfDebezium\u8fd9\u6837\u7684CDC\u5de5\u5177\u7684\u8f93\u51fa\u662f\u600e\u6837\u7684\uff1f<\/h2>\n
                \u8868\u8fbe\u53d8\u66f4\u6570\u636e\u7684JSON\u6d88\u606f\u5177\u6709\u4e0e\u4ee5\u4e0b\u5217\u8868\u76f8\u4f3c\u7684\u6709\u8da3\u5b57\u6bb5\uff1a<\/p>\n
                  \n
                • \n
                    operation: \u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u30b3\u30fc\u30c9(DELETE, APPEND, UPDATE, CREATE)<\/ul>\n<\/li>\n<\/ul>\n
                     <\/p>\n
                      operation_date: \u305d\u308c\u305e\u308c\u306e\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u30a2\u30af\u30b7\u30e7\u30f3\u304c\u3042\u3063\u305f\u65e5\u4ed8\u3001\u30bf\u30a4\u30e0\u30b9\u30bf\u30f3\u30d7<\/ul>\n
                      Debezium\u7684\u8f93\u51fa\u5305\u542b\u4ee5\u4e0b\u5b57\u6bb5\uff08\u6b64\u6f14\u793a\u4e2d\u4e0d\u5305\u542b\uff09\uff1a<\/p>\n
                        \n
                      • \n
                          before: \u5909\u66f4\u524d\u306e\u884c<\/ul>\n<\/li>\n<\/ul>\n
                           <\/p>\n
                            after: \u5909\u66f4\u5f8c\u306e\u884c<\/ul>\n
                            \u8bf7\u67e5\u770b\u8fd9\u4e2a\u53c2\u8003\u8d44\u6599\u4ee5\u4e86\u89e3\u53ef\u80fd\u7684\u9886\u57df\u3002<\/p>\n
                            \u4f7f\u7528Auto Loader\uff08\u4e91\u6587\u4ef6\uff09\u6765\u5b9e\u73b0\u589e\u91cf\u6570\u636e\u52a0\u8f7d\u3002<\/h2>\n
                            \"\"<\/div>\n
                            \u901a\u8fc7\u6a21\u5f0f\u7684\u66f4\u65b0\uff0c\u4e0e\u5916\u90e8\u7cfb\u7edf\u7684\u534f\u8c03\u53ef\u80fd\u53d8\u5f97\u56f0\u96be\u3002\u5916\u90e8\u6570\u636e\u5e93\u4f1a\u8fdb\u884c\u6a21\u5f0f\u7684\u66f4\u65b0\u548c\u5217\u7684\u6dfb\u52a0\u3001\u66f4\u65b0\uff0c\u6211\u4eec\u7684\u7cfb\u7edf\u9700\u8981\u5bf9\u8fd9\u4e9b\u53d8\u5316\u5177\u6709\u5f3a\u5927\u7684\u9002\u5e94\u6027\u3002Databricks\u7684\u81ea\u52a8\u52a0\u8f7d\u5668\uff08cloudFiles\uff09\u53ef\u4ee5\u7acb\u5373\u5904\u7406\u6a21\u5f0f\u7684\u4f30\u7b97\u548c\u6f14\u5316\u3002<\/p>\n
                            \u901a\u8fc7\u4f7f\u7528Auto Loader\uff0c\u53ef\u4ee5\u4ece\u4e91\u5b58\u50a8\u4e2d\u5bfc\u5165\u6570\u767e\u4e07\u4e2a\u6587\u4ef6\uff0c\u5e76\u652f\u6301\u5927\u89c4\u6a21\u7684\u6a21\u5f0f\u4f30\u8ba1\u548c\u6f14\u5316\u3002\u5728\u8fd9\u4e2a\u7b14\u8bb0\u672c\u4e2d\uff0c\u6211\u4eec\u5c06\u5229\u7528Auto Loader\u6765\u5904\u7406\u6d41\u6570\u636e\uff08\u548c\u6279\u91cf\u6570\u636e\uff09\u3002<\/p>\n
                            \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Auto Loader\u6765\u521b\u5efa\u4e00\u4e2a\u6d41\u6c34\u7ebf\uff0c\u4ee5\u4fbf\u5bfc\u5165\u7531\u5916\u90e8\u63d0\u4f9b\u5546\u63d0\u4f9b\u7684\u539f\u59cbJSON\u6570\u636e\u3002<\/p>\n
                            \u5206\u5e03\u5f0f\u5206\u7c7b\u8d26\u6280\u672f\uff08DLT\uff09\u662f\u4f7f\u7528Python\u7f16\u5199\u7684\u4ee3\u7801\u7ed3\u6784\u3002<\/h2>\n
                            \u4e3a\u4e86\u4f7f\u7528\u76f8\u5173\u65b9\u6cd5\uff0c\u60a8\u9700\u8981\u5bfc\u5165dlt Python\u6a21\u5757\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4e5f\u5bfc\u5165\u4e86pyspark.sql.functions\u3002<\/p>\n
                            DLT\u7684\u8868\u683c\u3001\u89c6\u56fe\u548c\u5173\u8054\u8bbe\u7f6e\u662f\u4f7f\u7528\u88c5\u9970\u5668\u8fdb\u884c\u8bbe\u7f6e\u7684\u3002<\/p>\n
                            \u5982\u679c\u4f60\u6ca1\u6709\u63a5\u89e6\u8fc7Python\u7684\u88c5\u9970\u5668\uff0c\u90a3\u4e48\u4f60\u53ef\u4ee5\u5c06\u5176\u89c6\u4e3a\u5728Python\u811a\u672c\u4e2d\u4e0e\u4e0b\u4e00\u4e2a\u88ab\u8868\u793a\u4e3a\u51fd\u6570\u4e92\u52a8\u7684\u4ee5@\u5f00\u59cb\u7684\u51fd\u6570\u6216\u7c7b\u3002<\/p>\n
                            @dlt.table\u88c5\u9970\u5668\u662f\u5c06Python\u51fd\u6570\u8f6c\u6362\u4e3aDelta Live\u8868\u7684\u57fa\u672c\u65b9\u6cd5\u3002<\/p>\n
                            \u4ee5\u4e0b\u6211\u4eec\u5c06\u63a2\u7d22\u5230\u8fbe\u6570\u636e\u3002<\/p>\n
                            \u9752\u94dc\u684c-\u81ea\u52a8\u88c5\u586b\u5668\u548cDLT<\/h3>\n
                            ## \u30b9\u30c8\u30ec\u30fc\u30b8\u30d1\u30b9\u304b\u3089\u53d6\u5f97\u3059\u308b\u751f\u306eJSON\u30c7\u30fc\u30bf\u3092\u542b\u3080\u30d6\u30ed\u30f3\u30ba\u30c6\u30fc\u30d6\u30eb\u306e\u4f5c\u6210\r\n<\/span>import<\/span> dlt<\/span>\r\nfrom<\/span> pyspark.sql.functions<\/span> import<\/span> *<\/span>\r\nfrom<\/span> pyspark.sql.types<\/span> import<\/span> *<\/span>\r\n\r\nsource<\/span> =<\/span> spark<\/span>.<\/span>conf<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>source<\/span>\"<\/span>)<\/span>\r\n\r\n@dlt.table<\/span>(<\/span>name<\/span>=<\/span>\"<\/span>customer_bronze<\/span>\"<\/span>,<\/span>\r\n                  comment<\/span> =<\/span> \"<\/span>\u30af\u30e9\u30a6\u30c9\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u30b9\u30c8\u30ec\u30fc\u30b8\u306e\u30e9\u30f3\u30c7\u30a3\u30f3\u30b0\u30be\u30fc\u30f3\u304b\u3089\u30a4\u30f3\u30af\u30ea\u30e1\u30f3\u30bf\u30eb\u306b\u53d6\u308a\u8fbc\u307e\u308c\u308b\u65b0\u898f\u9867\u5ba2<\/span>\"<\/span>,<\/span>\r\n  table_properties<\/span>=<\/span>{<\/span>\r\n    \"<\/span>quality<\/span>\"<\/span>:<\/span> \"<\/span>bronze<\/span>\"<\/span>\r\n  }<\/span>\r\n)<\/span>\r\n\r\ndef<\/span> customer_bronze<\/span>():<\/span>\r\n  return <\/span>(<\/span>\r\n    spark<\/span>.<\/span>readStream<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>cloudFiles<\/span>\"<\/span>)<\/span> \\\r\n      .<\/span>option<\/span>(<\/span>\"<\/span>cloudFiles.format<\/span>\"<\/span>,<\/span> \"<\/span>json<\/span>\"<\/span>)<\/span> \\\r\n      .<\/span>option<\/span>(<\/span>\"<\/span>cloudFiles.inferColumnTypes<\/span>\"<\/span>,<\/span> \"<\/span>true<\/span>\"<\/span>)<\/span> \\\r\n      .<\/span>load<\/span>(<\/span>f<\/span>\"<\/span>{<\/span>source<\/span>}<\/span>\/customers<\/span>\"<\/span>)<\/span>\r\n  )<\/span>\r\n<\/code><\/pre>\n
                            \u94f6\u5c42 – \u7ecf\u8fc7\u6e05\u6d01\u7684\u684c\u5b50\uff08\u53d7\u5230\u9650\u5236\u7684\u5e94\u7528\uff09<\/h3>\n
                            @dlt.table<\/span>(<\/span>name<\/span>=<\/span>\"<\/span>customer_bronze_clean<\/span>\"<\/span>,<\/span>\r\n  comment<\/span>=<\/span>\"<\/span>\u30af\u30ec\u30f3\u30b8\u30f3\u30b0\u3055\u308c\u305f\u30d6\u30ed\u30f3\u30ba\u9867\u5ba2\u30d3\u30e5\u30fc(\u30b7\u30eb\u30d0\u30fc\u306b\u306a\u308b\u30c6\u30fc\u30d6\u30eb\u3067\u3059)<\/span>\"<\/span>)<\/span>\r\n\r\n@dlt.expect_or_drop<\/span>(<\/span>\"<\/span>valid_id<\/span>\"<\/span>,<\/span> \"<\/span>id IS NOT NULL<\/span>\"<\/span>)<\/span>\r\n@dlt.expect<\/span>(<\/span>\"<\/span>valid_address<\/span>\"<\/span>,<\/span> \"<\/span>address IS NOT NULL<\/span>\"<\/span>)<\/span>\r\n@dlt.expect_or_drop<\/span>(<\/span>\"<\/span>valid_operation<\/span>\"<\/span>,<\/span> \"<\/span>operation IS NOT NULL<\/span>\"<\/span>)<\/span>\r\n\r\ndef<\/span> customer_bronze_clean<\/span>():<\/span>\r\n  return<\/span> dlt<\/span>.<\/span>read_stream<\/span>(<\/span>\"<\/span>customer_bronze<\/span>\"<\/span>)<\/span> \\\r\n            .<\/span>select<\/span>(<\/span>\"<\/span>address<\/span>\"<\/span>,<\/span> \"<\/span>email<\/span>\"<\/span>,<\/span> \"<\/span>id<\/span>\"<\/span>,<\/span> \"<\/span>firstname<\/span>\"<\/span>,<\/span> \"<\/span>lastname<\/span>\"<\/span>,<\/span> \"<\/span>operation<\/span>\"<\/span>,<\/span> \"<\/span>operation_date<\/span>\"<\/span>,<\/span> \"<\/span>_rescued_data<\/span>\"<\/span>)<\/span>\r\n<\/code><\/pre>\n
                            \u94f6\u8272\u684c\u5b50\u7684\u5177\u8c61\u5316<\/h2>\n
                            \"Make<\/div>\n
                            \u5728\u540d\u4e3acustomer_silver\u7684\u94f6\u8272\u8868\u4e2d\uff0c\u5305\u542b\u4e86\u6700\u65b0\u7684\u89c6\u56fe\u3002\u5b83\u662f\u539f\u59cb\u8868\u7684\u526f\u672c\u3002<\/p>\n
                            \u5728DLT\u7ba1\u9053\u8bbe\u7f6e\u4e2d\u6dfb\u52a0\u5e76\u542f\u7528applyChanges\u914d\u7f6e\uff0c\u4ee5\u660e\u786e\u542f\u7528\u6b64\u529f\u80fd\uff0c\u4ece\u800c\u5c06Apply Changes Into\u64cd\u4f5c\u4f20\u64ad\u5230\u540e\u7eed\u7684\u94f6\u5c42\u3002<\/p>\n
                            \u5220\u9664\u4e0d\u5fc5\u8981\u7684\u5ba2\u6237\u8bb0\u5f55 – \u94f6\u8272\u8868 – \u4f7f\u7528 DLT Python<\/h3>\n
                            dlt<\/span>.<\/span>create_target_table<\/span>(<\/span>name<\/span>=<\/span>\"<\/span>customer_silver<\/span>\"<\/span>,<\/span>\r\n  comment<\/span>=<\/span>\"<\/span>\u30af\u30ec\u30f3\u30b8\u30f3\u30b0\u3001\u30de\u30fc\u30b8\u3055\u308c\u305f\u9867\u5ba2<\/span>\"<\/span>,<\/span>\r\n  table_properties<\/span>=<\/span>{<\/span>\r\n    \"<\/span>quality<\/span>\"<\/span>:<\/span> \"<\/span>silver<\/span>\"<\/span>\r\n  }<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
                            dlt<\/span>.<\/span>apply_changes<\/span>(<\/span>\r\n  target<\/span> =<\/span> \"<\/span>customer_silver<\/span>\"<\/span>,<\/span> # \u30de\u30c6\u30ea\u30a2\u30e9\u30a4\u30ba\u3055\u308c\u308b\u9867\u5ba2\u30c6\u30fc\u30d6\u30eb\r\n<\/span>  source<\/span> =<\/span> \"<\/span>customer_bronze_clean<\/span>\"<\/span>,<\/span> # \u5165\u529b\u306eCDC\r\n<\/span>  keys<\/span> =<\/span> [<\/span>\"<\/span>id<\/span>\"<\/span>],<\/span> # upsert\/delete\u3059\u308b\u305f\u3081\u306b\u884c\u3092\u30de\u30c3\u30c1\u3059\u308b\u969b\u306e\u4e3b\u30ad\u30fc\r\n<\/span>  sequence_by<\/span> =<\/span> col<\/span>(<\/span>\"<\/span>operation_date<\/span>\"<\/span>),<\/span> # \u6700\u65b0\u306e\u5024\u3092\u53d6\u5f97\u3059\u308b\u305f\u3081\u306b\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u65e5\u306b\u3088\u308b\u91cd\u8907\u6392\u9664\r\n<\/span>  apply_as_deletes<\/span> =<\/span> expr<\/span>(<\/span>\"<\/span>operation = <\/span>'<\/span>DELETE<\/span>'\"<\/span>),<\/span> # DELETE\u306e\u6761\u4ef6\r\n<\/span>  except_column_list<\/span> =<\/span> [<\/span>\"<\/span>operation<\/span>\"<\/span>,<\/span> \"<\/span>operation_date<\/span>\"<\/span>,<\/span> \"<\/span>_rescued_data<\/span>\"<\/span>]<\/span> # \u30e1\u30bf\u30c7\u30fc\u30bf\u30ab\u30e9\u30e0\u306e\u524a\u9664\r\n<\/span>)<\/span>\r\n<\/code><\/pre>\n
                            \u521b\u5efa\u7ba1\u9053<\/h2>\n
                            \"Screen<\/div>\n
                            \u4ee5\u4e0b\u662f\u672c\u6b21\u8bbe\u7f6e\u7684JSON\u793a\u4f8b\u3002<\/p>\n
                            {<\/span>\r\n    \"id\"<\/span>:<\/span> \"7c2607ec-47a2-48d9-8572-9e498656b573\"<\/span>,<\/span>\r\n    \"clusters\"<\/span>:<\/span> [<\/span>\r\n        {<\/span>\r\n            \"label\"<\/span>:<\/span> \"default\"<\/span>,<\/span>\r\n            \"autoscale\"<\/span>:<\/span> {<\/span>\r\n                \"min_workers\"<\/span>:<\/span> 1<\/span>,<\/span>\r\n                \"max_workers\"<\/span>:<\/span> 5<\/span>,<\/span>\r\n                \"mode\"<\/span>:<\/span> \"ENHANCED\"<\/span>\r\n            }<\/span>\r\n        }<\/span>\r\n    ],<\/span>\r\n    \"development\"<\/span>:<\/span> true<\/span>,<\/span>\r\n    \"continuous\"<\/span>:<\/span> false<\/span>,<\/span>\r\n    \"channel\"<\/span>:<\/span> \"CURRENT\"<\/span>,<\/span>\r\n    \"edition\"<\/span>:<\/span> \"ADVANCED\"<\/span>,<\/span>\r\n    \"photon\"<\/span>:<\/span> false<\/span>,<\/span>\r\n    \"libraries\"<\/span>:<\/span> [<\/span>\r\n        {<\/span>\r\n            \"notebook\"<\/span>:<\/span> {<\/span>\r\n                \"path\"<\/span>:<\/span> \"\/Users\/takaaki.yayoi@databricks.com\/20221212_dlt_cdc\/2-Retail_DLT_CDC_Python\"<\/span>\r\n            }<\/span>\r\n        }<\/span>\r\n    ],<\/span>\r\n    \"name\"<\/span>:<\/span> \"dlt_cdc_retail\"<\/span>,<\/span>\r\n    \"storage\"<\/span>:<\/span> \"\/tmp\/takaaki.yayoi@databricks.com\/demo\/dlt_cdc\"<\/span>,<\/span>\r\n    \"configuration\"<\/span>:<\/span> {<\/span>\r\n        \"source\"<\/span>:<\/span> \"\/tmp\/takaaki.yayoi@databricks.com\/demo\/cdc_raw\"<\/span>\r\n    },<\/span>\r\n    \"target\"<\/span>:<\/span> \"cdc_data_taka\"<\/span>\r\n}<\/span>\r\n<\/code><\/pre>\n
                            \u6267\u884c\u6d41\u6c34\u7ebf<\/h2>\n
                            \"Screen<\/div>\n
                            \"Screen<\/div>\n
                            \u6267\u884c\u6d41\u6c34\u7ebf\u540e\uff0c\u5c06\u5206\u6790\u4e8b\u4ef6\u65e5\u5fd7\u4ee5\u76d1\u63a7\u4e8b\u4ef6\u65e5\u5fd7\u548c\u8840\u7edf\u6570\u636e\u3002<\/p>\n
                            \u4e8b\u4ef6\u65e5\u5fd7\u7684\u5206\u6790<\/h1>\n
                             <\/p>\n
                            \u6bcf\u4e2a DLT \u7ba1\u9053\u90fd\u4e0e\u5176\u5728\u7ba1\u9053\u4e2d\u5b9a\u4e49\u7684\u5b58\u50a8\u4f4d\u7f6e\u62e5\u6709\u81ea\u5df1\u7684\u4e8b\u4ef6\u8868\u3002\u901a\u8fc7\u8fd9\u4e2a\u8868\uff0c\u53ef\u4ee5\u786e\u8ba4\u6b63\u5728\u53d1\u751f\u7684\u60c5\u51b5\uff0c\u5e76\u68c0\u67e5\u901a\u8fc7\u7ba1\u9053\u7684\u6570\u636e\u8d28\u91cf\u3002<\/p>\n
                            \"undefined\"<\/div>\n
                            \u5b89\u88c5<\/h2>\n
                            %<\/span>sql<\/span> \r\n-- \u9069\u5b9c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044<\/span>\r\nCREATE<\/span> TABLE<\/span> IF<\/span> NOT<\/span> EXISTS<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span> using<\/span> delta<\/span> LOCATION<\/span> '$storage_path\/system\/events'<\/span>;<\/span>\r\nselect<\/span> *<\/span> from<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span>;<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            Delta Live Tables\u7684\u671f\u671b\u5206\u6790<\/h2>\n
                            \u901a\u8fc7Delta Live Tables\uff0c\u53ef\u4ee5\u901a\u8fc7\u671f\u671b\u8ddf\u8e2a\u6570\u636e\u8d28\u91cf\u3002\u8fd9\u4e9b\u671f\u671b\u4f1a\u4e0eDLT\u7684\u65e5\u5fd7\u4e8b\u4ef6\u4e00\u8d77\u5b58\u50a8\u4e3a\u6280\u672f\u8868\u3002\u4e3a\u4e86\u5206\u6790\u8fd9\u4e9b\u4fe1\u606f\uff0c\u53ef\u4ee5\u7b80\u5355\u5730\u521b\u5efa\u4e00\u4e2a\u89c6\u56fe\u3002<\/p>\n
                            1 – \u4e8b\u4ef6\u65e5\u5fd7\u7684\u5206\u6790<\/h2>\n
                            \u5728\u8be6\u60c5\u680f\u4e2d\u5305\u542b\u4e86\u53d1\u9001\u5230\u4e8b\u4ef6\u65e5\u5fd7\u7684\u6bcf\u4e2a\u4e8b\u4ef6\u7684\u5143\u6570\u636e\u3002\u6839\u636e\u4e8b\u4ef6\u7c7b\u578b\uff0c\u5b57\u6bb5\u4f1a\u6709\u6240\u4e0d\u540c\u3002\u4ee5\u4e0b\u662f\u884c\u7a0b\u75b2\u52b3\u7684\u793a\u4f8b\u3002<\/p>\n
                            \n
                            \u30a4\u30d9\u30f3\u30c8\u306e\u30bf\u30a4\u30d7\u6319\u52d5user_action<\/code>\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u4f5c\u6210\u306e\u3088\u3046\u306a\u30a2\u30af\u30b7\u30e7\u30f3\u304c\u884c\u308f\u308c\u305f\u969b\u306b\u751f\u3058\u308b\u30a4\u30d9\u30f3\u30c8flow_definition<\/code>\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8\u3084\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u304c\u884c\u308f\u308c\u305f\u969b\u306b\u751f\u3058\u308b\u30a4\u30d9\u30f3\u30c8\u3067\u3042\u308a\u3001\u30ea\u30cd\u30fc\u30b8\u30e5\u3001\u30b9\u30ad\u30fc\u30de\u3001\u5b9f\u884c\u8a08\u753b\u60c5\u5831\u3092\u6301\u3061\u307e\u3059output_dataset<\/code> \u3068 input_datasets<\/code>\u51fa\u529b\u306e\u30c6\u30fc\u30d6\u30eb\/\u30d3\u30e5\u30fc\u3001\u524d\u6bb5\u306e\u30c6\u30fc\u30d6\u30eb\/\u30d3\u30e5\u30fcflow_type<\/code>\u30b3\u30f3\u30d7\u30ea\u30fc\u30c8\u30d5\u30ed\u30fc\u304b\u8ffd\u52a0\u306e\u30d5\u30ed\u30fc\u304bexplain_text<\/code>Spark\u306e\u5b9f\u884c\u8a08\u753bflow_progress<\/code>\u30c7\u30fc\u30bf\u30d5\u30ed\u30fc\u304c\u30c7\u30fc\u30bf\u30d0\u30c3\u30c1\u306e\u51e6\u7406\u3092\u958b\u59cb\u3042\u308b\u3044\u306f\u5b8c\u4e86\u3057\u305f\u969b\u306b\u751f\u3058\u308b\u30a4\u30d9\u30f3\u30c8metrics<\/code>\u73fe\u5728\u306fnum_output_rows<\/code>\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059data_quality<\/code> (dropped_records<\/code>), (expectations<\/code>: name<\/code>, dataset<\/code>, passed_records<\/code>, failed_records<\/code>)\u3053\u306e\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3059\u308b\u30c7\u30fc\u30bf\u54c1\u8cea\u30eb\u30fc\u30eb\u306e\u7d50\u679c\u306e\u914d\u5217\u304c\u542b\u307e\u308c\u307e\u3059 * expectations<\/code><\/div>\n<\/div>\n
                            \u4e8b\u4ef6\u65e5\u5fd7 – \u6839\u636e\u65f6\u95f4\u6233\u6392\u5e8f\u7684\u539f\u59cb\u4e8b\u4ef6<\/h3>\n
                            -- \u9069\u5b9c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044<\/span>\r\nSELECT<\/span> \r\n       id<\/span>,<\/span>\r\n       timestamp<\/span>,<\/span>\r\n       sequence<\/span>,<\/span>\r\n       event_type<\/span>,<\/span>\r\n       message<\/span>,<\/span>\r\n       level<\/span>,<\/span> \r\n       details<\/span>\r\n  FROM<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span>\r\n ORDER<\/span> BY<\/span> timestamp<\/span> ASC<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            2- DLT\u7684\u884d\u751f\u7269<\/h2>\n
                            %<\/span>sql<\/span>\r\n-- \u30bf\u30a4\u30d7\u3068\u6700\u65b0\u306e\u5909\u66f4\u3054\u3068\u306b\u51fa\u529b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4e00\u89a7\u3057\u307e\u3059<\/span>\r\n-- \u9069\u5b9c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044<\/span>\r\ncreate<\/span> or<\/span> replace<\/span> temp<\/span> view<\/span> cdc_dlt_expectations<\/span> as<\/span> (<\/span>\r\n  SELECT<\/span> \r\n    id<\/span>,<\/span>\r\n    timestamp<\/span>,<\/span>\r\n    details<\/span>:<\/span>flow_progress<\/span>.<\/span>metrics<\/span>.<\/span>num_output_rows<\/span> as<\/span> output_records<\/span>,<\/span>\r\n    details<\/span>:<\/span>flow_progress<\/span>.<\/span>data_quality<\/span>.<\/span>dropped_records<\/span>,<\/span>\r\n    details<\/span>:<\/span>flow_progress<\/span>.<\/span>status<\/span> as<\/span> status_update<\/span>,<\/span>\r\n    explode<\/span>(<\/span>from_json<\/span>(<\/span>details<\/span>:<\/span>flow_progress<\/span>.<\/span>data_quality<\/span>.<\/span>expectations<\/span>\r\n             ,<\/span>'array<struct<dataset: string, failed_records: bigint, name: string, passed_records: bigint>>'<\/span>))<\/span> expectations<\/span>\r\n  FROM<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span>\r\n  where<\/span> details<\/span>:<\/span>flow_progress<\/span>.<\/span>data_quality<\/span>.<\/span>expectations<\/span> is<\/span> not<\/span> null<\/span>\r\n  ORDER<\/span> BY<\/span> timestamp<\/span>);<\/span>\r\n\r\nselect<\/span> *<\/span> from<\/span> cdc_dlt_expectations<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            %<\/span>sql<\/span>\r\n----------------------------------------------------------------------------------------<\/span>\r\n-- \u30ea\u30cd\u30fc\u30b8\u30e5<\/span>\r\n----------------------------------------------------------------------------------------<\/span>\r\nSELECT<\/span> max_timestamp<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>.<\/span>input_datasets<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>.<\/span>flow_type<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>.<\/span>schema<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>.<\/span>explain_text<\/span>,<\/span>\r\n       details<\/span>:<\/span>flow_definition<\/span>\r\n  FROM<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span> e<\/span>\r\n INNER<\/span> JOIN<\/span> (<\/span>\r\n              SELECT<\/span> details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span> output_dataset<\/span>,<\/span>\r\n                     MAX<\/span>(<\/span>timestamp<\/span>)<\/span> max_timestamp<\/span>\r\n                FROM<\/span> cdc_data_taka<\/span>.<\/span>demo_cdc_dlt_system_event_log_raw<\/span>\r\n               WHERE<\/span> details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span> IS<\/span> NOT<\/span> NULL<\/span>\r\n               GROUP<\/span> BY<\/span> details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span>\r\n            )<\/span> m<\/span>\r\n  WHERE<\/span> e<\/span>.<\/span>timestamp<\/span> =<\/span> m<\/span>.<\/span>max_timestamp<\/span>\r\n    AND<\/span> e<\/span>.<\/span>details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span> =<\/span> m<\/span>.<\/span>output_dataset<\/span>\r\n--    AND e.details:flow_definition IS NOT NULL<\/span>\r\n ORDER<\/span> BY<\/span> e<\/span>.<\/span>details<\/span>:<\/span>flow_definition<\/span>.<\/span>output_dataset<\/span>\r\n;<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            3 – \u8cea\u91cf\u6307\u6a19<\/h2>\n
                            %<\/span>sql<\/span> \r\nselect<\/span> sum<\/span>(<\/span>expectations<\/span>.<\/span>failed_records<\/span>)<\/span> as<\/span> failed_records<\/span>,<\/span> \r\nsum<\/span>(<\/span>expectations<\/span>.<\/span>passed_records<\/span>)<\/span> as<\/span> passed_records<\/span>,<\/span> \r\nexpectations<\/span>.<\/span>name<\/span> \r\nfrom<\/span> cdc_dlt_expectations<\/span> \r\ngroup<\/span> by<\/span> expectations<\/span>.<\/span>name<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            4 – \u68c0\u67e5\u5546\u4e1a\u6c47\u603b\u4fe1\u606f<\/h2>\n
                            %<\/span>python<\/span> \r\nimport<\/span> plotly.express<\/span> as<\/span> px<\/span>\r\nexpectations_metrics<\/span> =<\/span> spark<\/span>.<\/span>sql<\/span>(<\/span>\"\"\"<\/span>select sum(expectations.failed_records) as failed_records, \r\n                                 sum(expectations.passed_records) as passed_records, \r\n                                 expectations.name \r\n                                 from cdc_dlt_expectations\r\n                                 group by expectations.name<\/span>\"\"\"<\/span>).<\/span>toPandas<\/span>()<\/span>\r\npx<\/span>.<\/span>bar<\/span>(<\/span>expectations_metrics<\/span>,<\/span> x<\/span>=<\/span>\"<\/span>name<\/span>\"<\/span>,<\/span> y<\/span>=<\/span>[<\/span>\"<\/span>passed_records<\/span>\"<\/span>,<\/span> \"<\/span>failed_records<\/span>\"<\/span>],<\/span> title<\/span>=<\/span>\"<\/span>DLT expectations metrics<\/span>\"<\/span>)<\/span>\r\n<\/code><\/pre>\n
                            \"Screen<\/div>\n
                            \u603b\u7ed3<\/h1>\n
                            \u901a\u8fc7\u4f7f\u7528Delta Live Tables\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5229\u7528\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08Change Data Capture\uff09\u5c06\u53ea\u4f20\u64ad\u66f4\u6539\u7684\u6570\u636e\u4f20\u9012\u5230\u4e0b\u4e00\u4e2a\u8868\u3002\u901a\u8fc7\u5229\u7528\u4e8b\u4ef6\u65e5\u5fd7\u6570\u636e\uff0c\u8fd8\u53ef\u4ee5\u76d1\u63a7\u7ba1\u9053\u5904\u7406\u7684\u72b6\u6001\uff0c\u4ece\u800c\u6784\u5efa\u80fd\u591f\u540c\u65f6\u4fdd\u6301\u6570\u636e\u7ba1\u9053\u8d28\u91cf\u5e76\u6267\u884c\u591a\u79cd\u5904\u7406\u7684\u7ba1\u9053\u3002\u8bf7\u52a1\u5fc5\u5c1d\u8bd5\u4f7f\u7528DLT\u8fdb\u884cCDC\uff01<\/p>\n
                            Databricks \u514d\u8d39\u8bd5\u7528<\/h3>\n
                            \u8fbe\u767e\u514b\u514d\u8d39\u8bd5\u7528<\/p>\n","protected":false},"excerpt":{"rendered":"
                            \u6211\u4f1a\u6d4f\u89c8\u8fd9\u91cc\u53d1\u5e03\u7684\u7b14\u8bb0\u672c\u3002\u751f\u6210\u53d1\u751f\u53d8\u5316\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528Delta Live Tables\uff08DLT\uff09\u7684\u66f4\u6539\u6570\u636e\u6355\u83b7 […]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-47193","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\n\u5c1d\u8bd5\u4f7f\u7528Delta Live Tables\u7684\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08CDC\uff09 - Blog - Silicon Cloud<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/zh\/blog\/\u5c1d\u8bd5\u4f7f\u7528delta-live-tables\u7684\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08cdc\uff09\u3002\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5c1d\u8bd5\u4f7f\u7528Delta Live Tables\u7684\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08CDC\uff09\" \/>\n<meta property=\"og:description\" content=\"\u6211\u4f1a\u6d4f\u89c8\u8fd9\u91cc\u53d1\u5e03\u7684\u7b14\u8bb0\u672c\u3002\u751f\u6210\u53d1\u751f\u53d8\u5316\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528Delta Live Tables\uff08DLT\uff09\u7684\u66f4\u6539\u6570\u636e\u6355\u83b7 […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/zh\/blog\/\u5c1d\u8bd5\u4f7f\u7528delta-live-tables\u7684\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08cdc\uff09\u3002\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - 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