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Productionizing Big Data - stories from the tre...
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Roksolana
September 14, 2023
Technology
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Productionizing Big Data - stories from the trenches
Presented at ScalaDays 2023 (Madrid, Spain)
Roksolana
September 14, 2023
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Transcript
Productionizing big data - stories from the trenches
Roksolana Diachuk •Engineering manager at Captify •Women Who Code Kyiv
Data Engineering Lead •Speaker
AdTech methodologies deliver the right content at the right time
to the right consumer AdTech
None
You have your pipelines in production What’s next?
Types of issues • Low performance • Human errors •
Data source errors
Story #1. Unlucky query
Problem Drop 13 months of user profiles
Reporting
Problem 13 months hour=22042001
Loading mechanism loader.ImpalaLoaderConfig.periodToLoad: “P5D” loader.ImpalaLoaderConfig.periodToLoad: “P13M” val minTime = currentDay.minus(config.feedPeriod)
listFiles.filter(file => file.eventDateTime isAfter minTime)
Solution loader.ImpalaLoaderConfig.periodToLoad: “P5D” loader.ImpalaLoaderConfig.periodToLoad: “P1M” loader.ImpalaLoaderConfig.periodToLoad: “P13M” …
Story #2. Missing data
Data ingestion Data from Partner X Data costs attribution Extractor
Problem XX Advertiser ID, Language, XX Device Type, …, XX
Media Cost (USD) X Advertiser ID, Language, X Device Type, …, X Media Cost (USD)
Solution • Rename old columns • Reload data for the
week
Solution val colRegex: Regex = “””X (.+)“””.r val oldNewColumnsMapping =
df.schema.collect { case oldColdName@colRegex(pattern) => (oldColName.name, (“XX “ + pattern)) } oldNewColumnsMapping.foldLeft(df) { case (data, (oldName, newName)) => data.withColumnRenamed(oldName, newName) }
XX Advertiser ID, Language, XX Device Type, …, XX Media
Cost (USD) Solution
Story #3. Divide and conquer
Problem processing_time part-*.parquet filtering aggregations created part-*.parquet
• Slow processing • Large parquet files • Failing job
that consumes lots of resources Problem
• Write new partitioned state • Run downstream jobs with
smaller states • Generate seed partition column - xxhash64(fullUrl, domain) Solution
processing_time part-*.parquet created bucket=0 part-*.parquet part-*.parquet … bucket=9 part-*.parquet part-*.parquet
processing_time part-*.parquet Solution
Story #4. Catch the evolution train
Data organisation evolution
Problem • Missing columns from the source • Impala to
Databricks migration speed • Dependency with another team • Unhappy users
Log-level data Mapper Ingestor Transformer Data costs calculator Data costs
attribution
Data costs attribution Data costs attribution Data extractor Impala loader
Data costs attribution Data extractor Impala loader Data costs attribution
Solution XX Advertiser ID, Language, XX Device Type, …, XX
Partner Currency, XX CPM Fee (USD) XX Advertiser ID, Language, XX Device Type, …, XX Media Cost (USD) 26 columns 82 columns
Solution Data extractor New ingestion job
//final step is writing the data df.write .partitionBy(“event_date”, “event_hour”) .mode(SaveMode.Overwrite)
.parquet(dstPath) Solution
Why this solution doesn’t work data_feed clicks.csv.gz views.csv.gz activity.csv.gz event_date
clicks1.parquet clicks2.parquet
Impressions Clicks Conversions Attribution data source
Solution impressions clicks conversions clicks.csv.gz views.csv.gz activity.csv.gz
Story #5. Cleanup time
Corrupted data Data from Partner X Ingestor
Corrupted data Data from Partner X Ingestor IllegalArgumentException: Can't convert
value to BinaryType data type
Solution • Adjust pipeline • Reload data for 3 days
on S3 • Relaunch Databricks autoloader
Current solution impressions videoevents conversions impressions conversions Clicks clicks videoevents
Current solution impressions conversions clicks videoevents
Better solution impressions videoevents conversions impressions conversions clicks clicks videoevents
Conclusions
2. Observability is the key 4. Plan major changes carefully
1. Set up clear expectations with stakeholders Prevention mechanisms 3. Distribute data transformation load
2. Errors can be prevented 4. Data evolution is hard
1. Data setup is always changing Conclusions 3. There are multiple approaches with different tools
None
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