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Titian: Data Provenance Support in Spark.

Interlandi M, Shah K, Tetali SD, Gulzar MA, Yoo S, Kim M, Millstein T, Condie T - Proceedings VLDB Endowment (2015)

Bottom Line: Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California, Los Angeles.

ABSTRACT

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.

No MeSH data available.


Input lines with the most frequent error
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Figure 5: Input lines with the most frequent error

Mentions: Titian is enabled by wrapping the native SparkContext (sc in line 1 of Figure 1) with a LineageContext. Figure 5 shows a code fragment that takes the result of our running example in Figure 1 and selects the most frequent error (via a native Spark sortBy and take operations), then traces back to the input lines containing such errors and prints them.


Titian: Data Provenance Support in Spark.

Interlandi M, Shah K, Tetali SD, Gulzar MA, Yoo S, Kim M, Millstein T, Condie T - Proceedings VLDB Endowment (2015)

Input lines with the most frequent error
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4697929&req=5

Figure 5: Input lines with the most frequent error
Mentions: Titian is enabled by wrapping the native SparkContext (sc in line 1 of Figure 1) with a LineageContext. Figure 5 shows a code fragment that takes the result of our running example in Figure 1 and selects the most frequent error (via a native Spark sortBy and take operations), then traces back to the input lines containing such errors and prints them.

Bottom Line: Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California, Los Angeles.

ABSTRACT

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.

No MeSH data available.