Scalable Time Series Forecasting and Monitoring using Apache Spark and ElasticSearch at Adye
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Adyen enables integrating companies to accept payments from their customers using any payment method over any sales channel. We have designed and implemented a time series forecasting algorithm that allows us to predict the volume for each integration with confidence and thus be able to flag anomalies such as traffic drop or abnormally low traffic. We are using Apache Spark as our computational engine both to make this data available to the training process as well as to train over years of data in a scalable way. The prediction performances are benchmarked and the models are served in production through custom real-time monitoring and alerting infrastructure that uses ElasticSearch as hot storage. With this state-of-the-art solution, Adyen knows whether a problem happened and can alert the operational teams accordingly in a record time. ‘This presentation will cover the journey we took with focus on the mathematical concepts, the present time constraints, the prediction performances, and the architecture needed to make this happen. We’ll go over lessons learned, pitfalls, and best practices discovered on modeling time series datasets with Apache Spark. Data Scientists would be able to gain insights on applying effective and real-life seasonality modeling techniques. We’ll share our approaches used for sub-millisecond model serving that would inspire Data Engineers who work on related problems.
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Scalable Time Series Forecasting and Monitoring using Apache Spark and ElasticSearch at Adye
- 1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
- 2. Andreu Mora, Adyen Time series forecasting and monitoring with Apache Spark and ElasticSearch #UnifiedDataAnalytics #SparkAISummit
- 3. Adyen Payments Processor Tech company International customers (aka merchants) Omnichannel
- 4. Back in the day… The legacy monitor was based on a SQL query that would compute an average for the hour of the week and compare to a threshold.
- 5. Doesn’t quite work: • Generates loads of False Positives • It was fairly trimmed down: top merchants.
- 8. Do that at scale
- 10. Connect to a live platform
- 11. OK, but What is an anomaly? No luxury of a labelled dataset, divergence of opinions. Connecting to a live platform without ML deployment hooks ready. We were working on MLflow but not there yet. No standard for timeseries forecasting at scale With spark, several choices.
- 12. Considerations when dealing with Big Data Big Technology Leverage on mature Tech to solve the problem (hello Spark). Big diversity Many different topologies for our merchants and yet one algorithm to track them all. Big consequences 1000 merchants * 10 min * 95% accuracy = 50400 emails/week
- 13. Big Data Platform Volumes Predictions
- 14. Big Data Platform Volumes Predictions
- 16. TimeSeries Ecosystem Flint Spark-ts FB Prophet Stats models Data size consideration 1 year @ 1 min @ double64 = 4.2 mb
- 17. Scoring in Java While working on a fully functional engine to deploy ML models based on MLflow. Launch fast and iterate! Transporting the model The model transported for tens of thousands of accounts needs to be lightweight. Harness the maths No using blackboxed models, equations need to be understood and replicated in Java. Needs to perform fast Score and decide whether our seen traffic form ElasticSearch is actually anomalous on the ms scale.
- 18. Big Data Platform Volumes Predictions
- 20. Fourier components Would not optimise the business cycles ARIMA Not perfect for picking up seasonality Isolation Forests Great for multidimensional data, not so much for time series. Autoencoders Good luck transporting the model for each merchant. XGBM Noice, but score that in Java. Research stage Understand a problem and build a solution, decide what’s best.
- 21. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 22. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 23. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 24. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 25. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 26. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 27. Ridge Regression Makes scoring in Java nice and kinda easy. Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 28. Ridge Regression Makes scoring in Java nice and kinda easy. easy Residuals Confidence intervals modelled through quantile regression of observed values. Events Recurrent or one-off events are shown to the model. Piece-wise linear trends Breaks down the signal into pieces and learn the last trends. Gaussian Basis Functions Allow us to teach the model to understand business cycles The model Discover anomalous behaviour based on a probability p. Pre-sampling Allow us to sample and bucketize the merchants to adequate intervals.
- 29. Trendspotting Estimating hinges and trends and offering it as subproduct to Account Managers for evaluating the low variations of volume.
- 30. Train set: 90 days Test set: 7 days Real volume Predicted volume 95% confidence How do the predictions look like?
- 31. Missed event
- 32. The implementation on Spark How did we get there, on the Spark side. Reusability Overloads of scikit-learns and pandas allow us to ensure reusability Cross-validation Ensure the best tuning through tuning of hyperparameters. Scalability Using Spark’s map-reduce paradigm we totally control the computational performances.
- 34. Input daily time series —> {t:[…], v:[…]} Collect to list —> [{t:[…], v:[…]}] Hinges and Hyperparameters Distribute UDF Making it happen at scale
- 35. Cross-validation F4-sampling score: favours higher sampling considering classical precision and recall. Custom cv folds split TimeSeriesWeekSplit get the sense of the business cycle
- 36. The output
- 38. Enabling canary roll-out based on scores
- 39. Overcoming unsupervised learning Alarm rate and synthetic recall allow us to know for each case how many alarms would have been captured and raised, even without having a labelled dataset.
- 40. Trade-off alarm rates and recall We provide a number of choices (95%, 97%, 99% probability and completely profile what to expect in terms of anomalies.
- 44. So we saw this on the data
- 48. ’You don’t call us, we call you’
- 50. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT