数据是当今分析世界的宝贵资产。 在向最终用户提供数据时，跟踪数据在一段时间内的变化非常重要。 渐变维度 (SCD) 是随时间推移存储和管理当前和历史数据的维度。 在 SCD 的类型中，我们将特别关注类型 2（SCD 2），它保留了值的完整历史。 每条记录都包含有效时间和到期时间，以标识记录处于活动状态的时间段。 这可以通过少数审计列来实现。 例如：生效开始日期、生效结束日期和活动记录指示器。 让我们了解如何使用 Apache Hudi 来实现这种 SCD-2 表设计。
Apache Hudi 是下一代流数据湖平台。 Apache Hudi 将核心仓库和数据库功能直接引入数据湖。 Hudi 提供表、事务、高效的 upserts/deletes、高级索引、流式摄取服务、数据Clustering/压缩优化和并发性，同时将数据保持为开源文件格式。
Apache Hudi 默认显示表中的快照数据，即最近提交的最新数据。 如果我们想跟踪历史变化，我们需要利用 Hudi 的时间点查询（https://hudi.apache.org/docs/quick-start-guide#point-in-time-query）
Hudi 允许通过时间点查询旧版本数据或最新数据和时间旅行，通过时间点查询遍历历史数据变化是不高效的，需要对给定数据进行多次时间间隔分析。 让我们看看如何通过使用经典方法的解决方法来克服这个问题。 让我们考虑一个包含产品详细信息和卖家折扣的表。
+---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+ |seller_id|prod_category |product_name |product_package|discount_percentage|eff_start_ts |eff_end_ts |actv_ind| +---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+ |3412 |Healthcare |Dolo 650 |10 |10 |2022-04-01 16:30:45|9999-12-31 23:59:59|1 | |1234 |Detergent |Tide 2L |6 |15 |2021-12-15 15:20:30|9999-12-31 23:59:59|1 | |1234 |Home Essential|Hand Towel |12 |20 |2021-10-20 06:55:22|9999-12-31 23:59:59|1 | |4565 |Gourmet |Dairy Milk Silk|6 |30 |2021-06-12 20:30:40|9999-12-31 23:59:59|1 | +---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+
步骤
让我们使用 Spark 将这些数据写入 Hudi 表中
spark-shell \ --packages org.apache.hudi:hudi-spark-bundle_2.12:0.11.1,org.apache.spark:spark-avro_2.12:2.4.7,org.apache.avro:avro:1.8.2 \ --conf "spark.serializer=org.apache.spark.serializer.KryoSerializer" \ --conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension' \ --conf "spark.sql.hive.convertMetastoreParquet=false"
启动 spark shell 后，我们可以导入库，并创建 Hudi 表，如下所示。
Welcome to
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/___/ .__/\_,_/_/ /_/\_\ version 2.4.8
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Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_312)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.sql("""create table hudi_product_catalog (
| seller_id int,
| prod_category string,
| product_name string,
| product_package string,
| discount_percentage string,
| eff_start_ts timestamp,
| eff_end_ts timestamp,
| actv_ind int
| ) using hudi
| tblproperties (
| type = 'cow',
| primaryKey = 'seller_id,prod_category,eff_end_ts',
| preCombineField = 'eff_start_ts'
| )
| partitioned by (actv_ind)
| location 'gs://target_bucket/hudi_product_catalog/'""")将数据写入到存储桶后，如下是 Hudi 目标表的数据格式。
+-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+ |_hoodie_commit_time|_hoodie_commit_seqno |_hoodie_record_key |_hoodie_partition_path|_hoodie_file_name |seller_id|prod_category |product_name |product_package|discount_percentage|eff_start_ts |eff_end_ts |actv_ind| +-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+ |20220722113258101 |20220722113258101_0_0|seller_id:3412,prod_category:Healthcare,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-29-1219_20220722113258101.parquet|3412 |Healthcare |Dolo 650 |10 |10 |2022-04-01 16:30:45|9999-12-31 23:59:59|1 | |20220722113258101 |20220722113258101_0_1|seller_id:1234,prod_category:Home Essential,eff_end_ts:253402300799000000|actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-29-1219_20220722113258101.parquet|1234 |Home Essential|Hand Towel |12 |20 |2021-10-20 06:55:22|9999-12-31 23:59:59|1 | |20220722113258101 |20220722113258101_0_2|seller_id:4565,prod_category:Gourmet,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-29-1219_20220722113258101.parquet|4565 |Gourmet |Dairy Milk Silk|6 |30 |2021-06-12 20:30:40|9999-12-31 23:59:59|1 | |20220722113258101 |20220722113258101_0_3|seller_id:1234,prod_category:Detergent,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-29-1219_20220722113258101.parquet|1234 |Detergent |Tide 2L |6 |15 |2021-12-15 15:20:30|9999-12-31 23:59:59|1 | +-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+---------------+---------------+-------------------+-------------------+-------------------+--------+
2.假设我们的增量数据存储在下表中（非Hudi格式，可以是Hive）。
+---------+-------------+-----------------+---------------+-------------------+-------------------+ |seller_id|prod_category|product_name |product_package|discount_percentage|eff_start_ts | +---------+-------------+-----------------+---------------+-------------------+-------------------+ |1234 |Detergent |Tide 5L |6 |25 |2022-01-31 10:00:30| |4565 |Gourmet |Dairy Milk Almond|12 |45 |2022-06-12 20:30:40| |3345 |Stationary |Sticky Notes |4 |12 |2022-07-09 21:30:45| +---------+-------------+-----------------+---------------+-------------------+-------------------+
现在让我们通过对目标表进行Left Anti Join过滤掉增量表中的所有 Insert only 记录。
val updFileDf = spark.read.option("header",true).csv("gs://target_bucket/hudi_product_catalog/hudi_product_update.csv")
val tgtHudiDf = spark.sql("select * from hudi_product_catalog")
hudiTableData.createOrReplaceTempView("hudiTable")
//Cast as needed
val stgDf = updFileDf.withColumn("eff_start_ts",to_timestamp(col("eff_start_ts")))
.withColumn("seller_id",col("seller_id").cast("int"))
//Prepare an insert DF from incremental temp DF
val instmpDf = stgDf.as("stg")
.join(tgtHudiDf.as("tgt"),
col("stg.seller_id") === col("tgt.seller_id") &&
col("stg.prod_category") === col("tgt.prod_category"),"left_anti")
.select("stg.*")
val insDf = instmpDf.withColumn("eff_end_ts",to_timestamp(lit("9999-12-31 23:59:59")))
.withColumn("actv_ind",lit(1))
insDf.show(false)
+---------+-------------+------------+---------------+-------------------+-------------------+-------------------+--------+
|seller_id|prod_category|product_name|product_package|discount_percentage| eff_start_ts| eff_end_ts|actv_ind|
+---------+-------------+------------+---------------+-------------------+-------------------+-------------------+--------+
| 3345| Stationary|Sticky Notes| 4| 12|2022-07-09 21:30:45|9999-12-31 23:59:59| 1|
+---------+-------------+------------+---------------+-------------------+-------------------+-------------------+--------+我们有一个只插入记录的DataFrame。 接下来让我们创建一个DataFrame，其中将包含来自 delta 表和目标表的属性，并在目标上使用内连接，它将获取需要更新的记录。
//Prepare an update DF from incremental temp DF, select columns from both the tables
val updDf = stgDf.as("stg")
.join(tgtHudiDf.as("tgt"),
col("stg.seller_id") === col("tgt.seller_id") &&
col("stg.prod_category") === col("tgt.prod_category"),"inner")
.where(col("stg.eff_start_ts") > col("tgt.eff_start_ts"))
.select((stgDf.columns.map(c => stgDf(c).as(s"stg_$c"))++ tgtHudiDf.columns.map(c => tgtHudiDf(c).as(s"tgt_$c"))):_*)
updDf.show(false)
+-------------+-----------------+-----------------+-------------------+-----------------------+-------------------+-----------------------+------------------------+----------------------+--------------------------+---------------------+-------------+-----------------+----------------+-------------------+-----------------------+-------------------+-------------------+------------+
|stg_seller_id|stg_prod_category| stg_product_name|stg_product_package|stg_discount_percentage| stg_eff_start_ts|tgt__hoodie_commit_time|tgt__hoodie_commit_seqno|tgt__hoodie_record_key|tgt__hoodie_partition_path|tgt__hoodie_file_name|tgt_seller_id|tgt_prod_category|tgt_product_name|tgt_product_package|tgt_discount_percentage| tgt_eff_start_ts| tgt_eff_end_ts|tgt_actv_ind|
+-------------+-----------------+-----------------+-------------------+-----------------------+-------------------+-----------------------+------------------------+----------------------+--------------------------+---------------------+-------------+-----------------+----------------+-------------------+-----------------------+-------------------+-------------------+------------+
| 1234| Detergent| Tide 5L| 6| 25|2022-01-31 10:00:30| 20220710113622931| 20220710113622931...| seller_id:1234,pr...| actv_ind=1| 2dd6109f-2173-429...| 1234| Detergent| Tide 2L| 6| 15|2021-12-15 15:20:30|9999-12-31 23:59:59| 1|
| 4565| Gourmet|Dairy Milk Almond| 12| 45|2022-06-12 20:30:40| 20220710113622931| 20220710113622931...| seller_id:4565,pr...| actv_ind=1| 2dd6109f-2173-429...| 4565| Gourmet| Dairy Milk Silk| 6| 30|2021-06-12 20:30:40|9999-12-31 23:59:59| 1|
+-------------+-----------------+-----------------+-------------------+-----------------------+-------------------+-----------------------+------------------------+----------------------+--------------------------+---------------------+-------------+-----------------+----------------+-------------------+-----------------------+-------------------+-------------------+------------+现在我们有一个DataFrame，它在一条记录中包含新旧数据，让我们在各自单独的DataFrame中拉取更新记录的活动和非活动实例。
在进行上述练习时，我们将通过更改活动（新）记录的 eff_end_tsto eff_start_ts -1 并更新 actv_ind = 0 来废弃非活动记录
//Prepare Active updates
val updActiveDf = updDf.select(col("stg_seller_id").as("seller_id"),
col("stg_prod_category").as("prod_category"),
col("stg_product_name").as("product_name"),
col("stg_product_package").as("product_package"),
col("stg_discount_percentage").as("discount_percentage"),
col("stg_eff_start_ts").as("eff_start_ts"),
to_timestamp(lit("9999-12-31 23:59:59")) as ("eff_end_ts"),
lit(1) as ("actv_ind"))
updActiveDf.show(false)
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
|seller_id|prod_category|product_name |product_package|discount_percentage|eff_start_ts |eff_end_ts |actv_ind|
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
|1234 |Detergent |Tide 5L |6 |25 |2022-01-31 10:00:30|9999-12-31 23:59:59|1 |
|4565 |Gourmet |Dairy Milk Almond|12 |45 |2022-06-12 20:30:40|9999-12-31 23:59:59|1 |
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
//Prepare inactive updates, which will become obsolete records
val updInactiveDf = updDf.select(col("tgt_seller_id").as("seller_id"),
col("tgt_prod_category").as("prod_category"),
col("tgt_product_name").as("product_name"),
col("tgt_product_package").as("product_package"),
col("tgt_discount_percentage").as("discount_percentage"),
col("tgt_eff_start_ts").as("eff_start_ts"),
(col("stg_eff_start_ts") - expr("interval 1 seconds")).as("eff_end_ts"),
lit(0) as ("actv_ind"))
scala> updInactiveDf.show
+---------+-------------+---------------+---------------+-------------------+-------------------+-------------------+--------+
|seller_id|prod_category| product_name|product_package|discount_percentage| eff_start_ts| eff_end_ts|actv_ind|
+---------+-------------+---------------+---------------+-------------------+-------------------+-------------------+--------+
| 1234| Detergent| Tide 2L| 6| 15|2021-12-15 15:20:30|2022-01-31 10:00:29| 0|
| 4565| Gourmet|Dairy Milk Silk| 6| 30|2021-06-12 20:30:40|2022-06-12 20:30:39| 0|
+---------+-------------+---------------+---------------+-------------------+-------------------+-------------------+--------+现在我们将使用union运算符将插入、活动更新和非活动更新拉入单个DataFrame。 将此DataFrame作为最终 Hudi 写入逻辑的增量源。
scala> val upsertDf = insDf.union(updActiveDf).union(updInactiveDf)
scala> upsertDf.show
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
|seller_id|prod_category| product_name|product_package|discount_percentage| eff_start_ts| eff_end_ts|actv_ind|
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
| 3345| Stationary| Sticky Notes| 4| 12|2022-07-09 21:30:45|9999-12-31 23:59:59| 1|
| 4565| Gourmet|Dairy Milk Almond| 12| 45|2022-06-12 20:30:40|9999-12-31 23:59:59| 1|
| 1234| Detergent| Tide 5L| 6| 25|2022-01-31 10:00:30|9999-12-31 23:59:59| 1|
| 4565| Gourmet| Dairy Milk Silk| 6| 30|2021-06-12 20:30:40|2022-06-12 20:30:39| 0|
| 1234| Detergent| Tide 2L| 6| 15|2021-12-15 15:20:30|2022-01-31 10:00:29| 0|
+---------+-------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
val path = "gs://target_bucket/hudi_product_catalog"
upsertDf.write.format("org.apache.hudi")
.option(TABLE_TYPE_OPT_KEY, "COPY_ON_WRITE")
.option("hoodie.datasource.write.keygenerator.class","org.apache.hudi.keygen.ComplexKeyGenerator")
.option(RECORDKEY_FIELD_OPT_KEY, "seller_id,prod_category,eff_end_ts")
.option(PRECOMBINE_FIELD_OPT_KEY, "eff_start_ts")
.option("hoodie.table.name","hudi_product_catalog")
.option(DataSourceWriteOptions.HIVE_DATABASE_OPT_KEY, "target_schema")
.option(DataSourceWriteOptions.HIVE_TABLE_OPT_KEY, "hudi_product_catalog")
.option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL)
.option(DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY, "true")
.option(PARTITIONPATH_FIELD_OPT_KEY, "actv_ind")
.mode(Append)
.save(s"$path")
scala> spark.sql("refresh table stg_wmt_ww_fin_rtn_mb_dl_secure.hudi_product_catalog")
scala> spark.sql("select * from stg_wmt_ww_fin_rtn_mb_dl_secure.hudi_product_catalog").show(false)
+-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
|_hoodie_commit_time|_hoodie_commit_seqno |_hoodie_record_key |_hoodie_partition_path|_hoodie_file_name |seller_id|prod_category |product_name |product_package|discount_percentage|eff_start_ts |eff_end_ts |actv_ind|
+-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+
|20220722113258101 |20220722113258101_0_0|seller_id:3412,prod_category:Healthcare,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-72-2451_20220722114049500.parquet|3412 |Healthcare |Dolo 650 |10 |10 |2022-04-01 16:30:45|9999-12-31 23:59:59|1 |
|20220722113258101 |20220722113258101_0_1|seller_id:1234,prod_category:Home Essential,eff_end_ts:253402300799000000|actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-72-2451_20220722114049500.parquet|1234 |Home Essential|Hand Towel |12 |20 |2021-10-20 06:55:22|9999-12-31 23:59:59|1 |
|20220722114049500 |20220722114049500_0_2|seller_id:4565,prod_category:Gourmet,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-72-2451_20220722114049500.parquet|4565 |Gourmet |Dairy Milk Almond|12 |45 |2022-06-12 20:30:40|9999-12-31 23:59:59|1 |
|20220722114049500 |20220722114049500_0_3|seller_id:1234,prod_category:Detergent,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-72-2451_20220722114049500.parquet|1234 |Detergent |Tide 5L |6 |25 |2022-01-31 10:00:30|9999-12-31 23:59:59|1 |
|20220722114049500 |20220722114049500_0_4|seller_id:3345,prod_category:Stationary,eff_end_ts:253402300799000000 |actv_ind=1 |a94c9c58-ac6b-4841-a734-8ef1580e2547-0_0-72-2451_20220722114049500.parquet|3345 |Stationary |Sticky Notes |4 |12 |2022-07-09 21:30:45|9999-12-31 23:59:59|1 |
|20220722114049500 |20220722114049500_1_0|seller_id:4565,prod_category:Gourmet,eff_end_ts:1655065839000000 |actv_ind=0 |789e0317-d499-4d74-a5d9-ad6e6517d6b8-0_1-72-2452_20220722114049500.parquet|4565 |Gourmet |Dairy Milk Silk |6 |30 |2021-06-12 20:30:40|2022-06-12 20:30:39|0 |
|20220722114049500 |20220722114049500_1_1|seller_id:1234,prod_category:Detergent,eff_end_ts:1643623229000000 |actv_ind=0 |789e0317-d499-4d74-a5d9-ad6e6517d6b8-0_1-72-2452_20220722114049500.parquet|1234 |Detergent |Tide 2L |6 |15 |2021-12-15 15:20:30|2022-01-31 10:00:29|0 |
+-------------------+---------------------+-------------------------------------------------------------------------+----------------------+--------------------------------------------------------------------------+---------+--------------+-----------------+---------------+-------------------+-------------------+-------------------+--------+实施过程中需要考虑的几点
对于现有记录的每次更新，parquet 文件将在存储中重新写入/移动，这可能会影响写入时的性能
在查询数据期间，根据代表主要过滤器的属性对目标表进行分区总是一个更好的主意。 例如：销售表中的销售日期，注册产品目录的卖家。 上述示例中选择了 actv_ind ，因为我们希望使其易于解释并将所有活动记录保存在一个分区中。
结论
随着我们持续使用 Apache Hudi 编写 Spark 应用程序，我们将继续改进加载数据的策略，上述尝试只是用 Hudi 实现 SCD-2 功能的一个开始。
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