Linear algebra operations are at the core of many Machine Learning (ML) programs. At the same time, a considerable amount of the effort for solving data analytics problems is spent in data preparation. As a result, end-to- end ML pipelines often consist of (i) relational operators used for joining the input data, (ii) user defined functions used for feature extraction and vectorization, and (iii) linear algebra operators used for model training and cross- validation. Often, these pipelines need to scale out to large datasets. In this case, these pipelines are usually implemented on top of dataflow engines like Hadoop, Spark, or Flink. These dataflow engines implement relational operators on row-partitioned datasets. However, efficient linear algebra operators use block-partitioned matrices. As a result, pipelines combining both kinds of operators require rather expensive changes to the physical representation, in particular re partitioning steps. In this paper, we investigate the potential of reducing shuffling costs by fusing relational and linear algebra operations into specialized physical operators. We present BlockJoin, a distributed join algorithm which directly produces block-partitioned results. To minimize shuffling costs, BlockJoin applies database techniques known from columnar processing, such as index-joins and late materialization, in the context of parallel dataflow engines. Our experimental evaluation shows speedups up to 6× and the skew resistance of BlockJoin compared to state- of-the-art pipelines implemented in Spark.@en

Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we presented an optimistic recovery mechanism that in certain cases eliminates the need to checkpoint the intermediate state of an iterative algorithm. In case of failure, our mechanism uses a compensation function to transit the algorithm to a consistent state, from which the execution can continue and successfully converge. Since this recovery mechanism does not checkpoint any state, it achieves optimal failure-free performance while guaranteeing fault tolerance. In this paper, we demonstrate our recovery mechanism with the Apache Flink data processing engine. During our demonstration, attendees will be able to run graph algorithms and trigger failures to observe the algorithms recovering with compensation functions instead of checkpoints.@en

Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we presented an optimistic recovery mechanism that in certain cases eliminates the need to checkpoint the intermediate state of an iterative algorithm. In case of failure, our mechanism uses a compensation function to transit the algorithm to a consistent state, from which the execution can continue and successfully converge. Since this recovery mechanism does not checkpoint any state, it achieves optimal failure-free performance while guaranteeing fault tolerance. In this paper, we demonstrate our recovery mechanism with the Apache Flink data processing engine. During our demonstration, attendees will be able to run graph algorithms and trigger failures to observe the algorithms recovering with compensation functions instead of checkpoints.@en

Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we presented an optimistic recovery mechanism that in certain cases eliminates the need to checkpoint the intermediate state of an iterative algorithm. In case of failure, our mechanism uses a compensation function to transit the algorithm to a consistent state, from which the execution can continue and successfully converge. Since this recovery mechanism does not checkpoint any state, it achieves optimal failure-free performance while guaranteeing fault tolerance. In this paper, we demonstrate our recovery mechanism with the Apache Flink data processing engine. During our demonstration, attendees will be able to run graph algorithms and trigger failures to observe the algorithms recovering with compensation functions instead of checkpoints.@en

Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we presented an optimistic recovery mechanism that in certain cases eliminates the need to checkpoint the intermediate state of an iterative algorithm. In case of failure, our mechanism uses a compensation function to transit the algorithm to a consistent state, from which the execution can continue and successfully converge. Since this recovery mechanism does not checkpoint any state, it achieves optimal failure-free performance while guaranteeing fault tolerance. In this paper, we demonstrate our recovery mechanism with the Apache Flink data processing engine. During our demonstration, attendees will be able to run graph algorithms and trigger failures to observe the algorithms recovering with compensation functions instead of checkpoints.@en

Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domainspecific language for collections and matrices. Lara's intermediate representation (IR) re ects on the complete program, i.e., UDFs, control ow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domainspecific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of our proposed optimizations on dense and sparse data, which achieve speedups of up to an order of magnitude.@en