Muhammad Ali Gulzar

Titian: data provenance support in Spark

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders-of-magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

BigDebug: debugging primitives for interactive big data processing in spark

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today’s data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires re-thinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.First, BigDebug’s simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.

BigDebug: interactive debugger for big data analytics in Apache Spark

To process massive quantities of data, developers leverage data-intensive scalable computing (DISC) systems in the cloud, such as Googles MapReduce, Apache Hadoop, and Apache Spark. In terms of debugging, DISC systems support post-mortem log analysis but do not provide interactive debugging features in realtime. This tool demonstration paper showcases a set of concrete usecases on how BigDebug can help debug Big Data Applications by providing interactive, realtime debug primitives. To emulate interactive step-wise debugging without reducing throughput, BigDebug provides simulated breakpoints to enable a user to inspect a program without actually pausing the entire computation. To minimize unnecessary communication and data transfer, BigDebug provides on-demand watchpoints that enable a user to retrieve intermediate data using a guard and transfer the selected data on demand. To support systematic and efficient trial-and-error debugging, BigDebug also enables users to change program logic in response to an error at runtime and replay the execution from that step. BigDebug is available for download at http://web.cs.ucla.edu/~miryung/software.html

Automated debugging in data-intensive scalable computing

Developing Big Data Analytics workloads often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (e.g., program crash, outlier results, etc.) arise, developers are often interested in identifying a subset of the input data that is able to reproduce the problem. BigSift is a new faulty data localization approach that combines insights from automated fault isolation in software engineering and data provenance in database systems to find a minimum set of failure-inducing inputs. BigSift redefines data provenance for the purpose of debugging using a test oracle function and implements several unique optimizations, specifically geared towards the iterative nature of automated debugging workloads. BigSift improves the accuracy of fault localizability by several orders-of-magnitude (∼103 to 107×) compared to Titian data provenance, and improves performance by up to 66× compared to Delta Debugging, an automated fault-isolation technique. For each faulty output, BigSift is able to localize fault-inducing data within 62% of the original job running time.

Debugging Big Data Analytics in Spark with BigDebug

To process massive quantities of data, developers leverage Data-Intensive Scalable Computing (DISC) systems such as Apache Spark. In terms of debugging, DISC systems support only post-mortem log analysis and do not provide any debugging functionality. This demonstration paper showcases BigDebug: a tool enhancing Apache Spark with a set of interactive debugging features that can help users in debug their Big Data Applications.

Adding data provenance support to Apache Spark

Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g., from log files) and performing trial-and-error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders of magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

Interactive and automated debugging for big data analytics

An abundance of data in many disciplines of science, engineering, national security, health care, and business has led to the emerging field of Big Data Analytics that run in a cloud computing environment. To process massive quantities of data in the cloud, developers leverage Data-Intensive Scalable Computing (DISC) systems such as Googles MapReduce, Hadoop, and Spark. Currently, developers do not have easy means to debug DISC applications. The use of cloud computing makes application development feel more like batch jobs and the nature of debugging is therefore post-mortem. Developers of big data applications write code that implements a data processing pipeline and test it on their local workstation with a small sample data, downloaded from a TB-scale data warehouse. They cross fingers and hope that the program works in the expensive production cloud. When a job fails or they get a suspicious result, data scientists spend hours guessing at the source of the error, digging through post-mortem logs. In such cases, the data scientists may want to pinpoint the root cause of errors by investigating a subset of corresponding input records. The vision of my work is to provide interactive, real-time and automated debugging services for big data processing programs in modern DISC systems with minimum performance impact. My work investigates the following research questions in the context of big data analytics: (1) What are the necessary debugging primitives for interactive big data processing? (2) What scalable fault localization algorithms are needed to help the user to localize and characterize the root causes of errors? (3) How can we improve testing efficiency during iterative development of DISC applications by reasoning the semantics of dataflow operators and user-defined functions used inside dataflow operators in tandem? To answer these questions, we synthesize and innovate ideas from software engineering, big data systems, and program analysis, and coordinate innovations across the software stack from the user-facing API all the way down to the systems infrastructure.