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Setup for Async clustering Job

Clustering can be scheduled and run asynchronously using WriteClient APIs

1. Schedule clustering API can be found here
2. Execute clustering API can be found here

a SparkJob. The utilities spark job can be found here

1. prepare the clusering config file: 

     2. Schedule clustering

      you can find the schedule clustering instant time in the spark logs. With the log prefix "The schedule instant time is" ,and the schedule clustering instant time is 20210122190240

Image Added

     3. use the schedule instant time "20210122190240" to run clustering

Some caveats

There is WIP to fix these limitations. But these issues are worth mentioning:

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1. Convert dataset to hoodie format

   Code Block
   language scala
   val df = spark.read.option("sep", "\t").option("header", "true").csv(amznReviewsRawDataPath) val tableName = "reviews" df.write.format("org.apache.hudi"). options(getQuickstartWriteConfigs). option(PRECOMBINE_FIELD_OPT_KEY, "customer_id"). option(RECORDKEY_FIELD_OPT_KEY, "review_id"). option(PARTITIONPATH_FIELD_OPT_KEY, "marketplace"). option(OPERATION_OPT_KEY, "insert"). option(TABLE_NAME, tableName). mode(Overwrite). save(amznReviewHudiPath); //creates ~500 data files in one partition

   
   

2. Evaluate query time (No Clustering)

   Code Block
   language sql title query takes ~10 seconds
   scala> spark.time(spark.sql("select sum(total_votes), product_category from amzn_reviews where review_date > '2007' and review_date < '2009' group by product_category").collect()) Time taken: 10018 ms

     

3. Perform clustering

   Code Block
   language scala
   // create writeClient with overriding following write config: //"hoodie.clustering.plan.strategy.sort.columns" -> "product_category,review_date" //"hoodie.clustering.plan.strategy.max.bytes.per.group" -> "107374182400" //"hoodie.clustering.plan.strategy.max.num.groups" -> "1" val clusteringInstant = writeClient.scheduleClustering(Option.empty()) val metadata = writeClient.cluster(clusteringInstant.get, true) //creates ~350 data files and replaces existing ~500 data files one partition

   
   

   Info
   title Verify replacecommit is created
   $ hadoop fs -ls $amznReviewHudiPath/.hoodie/ Found 10 items drwxr-xr-x   - satish           0 2021-01-20 18:38 $amznReviewHudiPath/.hoodie/.aux drwxr-xr-x   - satish           0 2021-01-21 00:49 $amznReviewHudiPath/.hoodie/.temp -rw-r--r--   3 satish      445621 2021-01-20 18:41 $amznReviewHudiPath/.hoodie/20210120183848.commit -rw-r--r--   3 satish           0 2021-01-20 18:39 $amznReviewHudiPath/.hoodie/20210120183848.commit.requested -rw-r--r--   3 satish         979 2021-01-20 18:40 $amznReviewHudiPath/.hoodie/20210120183848.inflight -rw-r--r--   3 satish      493983 2021-01-21 00:51 $amznReviewHudiPath/.hoodie/20210121004731.replacecommit -rw-r--r--   3 satish           0 2021-01-21 00:47 $amznReviewHudiPath/.hoodie/20210121004731.replacecommit.inflight -rw-r--r--   3 satish      131495 2021-01-21 00:47 $amznReviewHudiPath/.hoodie/20210121004731.replacecommit.requested drwxr-xr-x   - satish           0 2021-01-20 18:38 $amznReviewHudiPath/.hoodie/archived -rw-r--r--   3 satish         228 2021-01-20 18:38 $amznReviewHudiPath/.hoodie/hoodie.properties

   
   

4. Evaluate query time (with Clustering). Note that same query in step 2 that took 10 seconds now runs in 4 seconds 

   Code Block
   language sql title query takes ~4 seconds
   scala> spark.time(spark.sql("select sum(total_votes), product_category from amzn_reviews where review_date > '2007' and review_date < '2009' group by product_category").collect()) Time taken: 4099 ms

    

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In summary, rewriting the data using clustering can speed up query runtime by ~60%. ~60% 

Rollout/Adoption Plan

• No impact on the existing users because add new function. Note that if you are already using Hudi, It is important to move your readers first to 0.7 release version before upgrading writers. This is because clustering creates a new type of commit and its important query engines recognize this new commit type.

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