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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.


Tracing time for grep and word count
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Figure 16: Tracing time for grep and word count

Mentions: The time to trace backward one record for grep is depicted in Figure 16(a). In the Newt case, the query to compute the trace backward is composed of a simple join. Not surprisingly, when relations are indexed the time to compute a full trace is small. When the relations are not indexed, the time to execute the query increases to 10 minutes. Tracing queries over Titian-D scale linearly from 0.07 seconds (at 500MB) to 1.5 seconds (at 500GB). Figure 16(b) compares the three versions of Titian. As expected, Titian-P and -C are slightly faster than Titian-D since the data lineage is more localized and not large.


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)

Tracing time for grep and word count
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 16: Tracing time for grep and word count
Mentions: The time to trace backward one record for grep is depicted in Figure 16(a). In the Newt case, the query to compute the trace backward is composed of a simple join. Not surprisingly, when relations are indexed the time to compute a full trace is small. When the relations are not indexed, the time to execute the query increases to 10 minutes. Tracing queries over Titian-D scale linearly from 0.07 seconds (at 500MB) to 1.5 seconds (at 500GB). Figure 16(b) compares the three versions of Titian. As expected, Titian-P and -C are slightly faster than Titian-D since the data lineage is more localized and not large.

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.