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Why Impala Continues to Lead?

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2 June 2014


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New SQL Choices in the Apache Hadoop Ecosystem: Why Impala Continues to Lead?

Impala continues to demonstrate performance leadership compared to alternatives (by 950% or more), while providing greater query throughput and with a far smaller CPU footprint.

In our previous post from January 2014, we reported that Impala had achieved query performance over Apache Hadoop equivalent to that of an analytic DBMS over its own proprietary storage system. We believed this was an important milestone because Impala’s objective has been to support a high-quality BI experience on Hadoop data, not to produce a “faster Apache Hive.” An enterprise-quality BI experience requires low latency and high concurrency (among other things), so surpassing a well-known proprietary MPP DBMS in these areas was important evidence of progress.

  In the past nine months, we’ve also all seen additional public validation that the original technical design for Hive, while effective for batch processing, was a dead-end for BI workloads. Recent examples have included the launch of Facebook’s Presto engine (Facebook was the inventor and world’s largest user of Hive), the emergence of Shark (Hive running on the Apache Spark DAG), and the “Stinger” initiative (Hive running on the Apache Tez [incubating] DAG).   Given the introduction of a number of new SQL-on-Hadoop implementations it seemed like a good time to do a roundup of the latest versions of each engine to see how they differ. We find that Impala maintains a significant performance advantage over the various other open source alternatives — ranging from 5x to 23x depending on the workload and the implementations that are compared. This advantage is due to some inherent design differences among the various systems, which we’ll explain below. Impala’s advantage is strongest for multi-user workloads, which arguably is the most relevant measure for users evaluating their options for BI use cases.

Configuration

Cluster

All tests were run on precisely the same cluster, which was torn down between runs to ensure fair comparisons. The cluster comprised 21 nodes, each equipped with:

  • 2 processors, 12 cores, Intel Xeon CPU E5-2630L 0 at 2.00GHz
  • 12 disk drives at 932GB each (one for the OS, the rest for HDFS)
  • 384GB of memory

Comparative Set

  • Impala 1.3.0
  • Hive-on-Tez: The final phase of the 18-month Stinger initiative (aka Hive 0.13)
  • Shark 0.9.2: A port of Hive from UC Berkeley AMPLab that is architecturally similar to Hive-on-Tez, but based on Spark instead of Tez. Shark testing was done on a native in-memory dataset (RDD) as well as HDFS.
  • Presto 0.60: Facebook’s query engine project

Queries

  • To ensure a realistic Hadoop workload with representative data-size-per-node, queries were run on a 15TB scale-factor dataset across 20 nodes.
  • We ran precisely the same open decision-support benchmark derived from TPC-DS described in our previous testing (with queries categorized into Interactive, Reporting, and Deep Analytics buckets).
  • Due to the lack of a cost-based optimizer and predicate propagation in all tested engines excepting Impala, we ran the same queries that had been converted to SQL-92-style joins from the previous testing and also manually propagated predicates where semantically equivalent. For consistency, we ran those same queries against Impala — although Impala produces identical results without these modifications.
  • In the case of Shark, manual query hints were needed in addition to the modifications above to complete the query runs. Furthermore, Shark required more memory than available in the cluster to run the Reporting and Deep Analytics queries on RDDs (and thus those queries could not be completed).
  • We selected comparable file formats across all engines, consistently using Snappy compression to ensure apples-to-apples comparisons. Furthermore, each engine was tested on a file format that ensures the best possible performance and a fair, consistent comparison: Impala on Apache Parquet (incubating), Hive-on-Tez on ORC, Presto on RCFile, and Shark on ORC. (Note that native support for Parquet in Shark as well as Presto is forthcoming.)
  • Standard methodical testing techniques (multiple runs, tuning, and so on) were used for each of the engines involved.

Results

Single User

Impala on Parquet was the performance leader by a substantial margin, running on average 5x faster than its next best alternative (Shark 0.9.2).

(Note: The results are not shown here, but the queries were also run on Impala/RCFile as a direct comparison to Presto/RCFile — and performance was consistently 20-30% slower than that of Impala/Parquet.)

The two Hive-on-DAG implementations produced similar results, which is consistent with what one would have expected given they have highly similar designs. Presto is the youngest implementation of the four and is held back by the fact that it runs on RCFile, which is a much less effective columnar format than Parquet. We look forward to re-running these benchmarks in a few months when Presto runs on Parquet.

Although these results are exciting in themselves, as previously explained, we believe that measuring latency under a multi-user workload is a more valuable metric — because you would very rarely, if ever, commit your entire cluster to a single query at a time.

Multiple Users

In this test of a concurrent workload, we ran seven Interactive queries (q42, q52, q55, q63, q68, q73, q98) 10 times concurrently. To prevent all processes from running the same queries at the same time, queries were run consistently back-to-back and randomized. Furthermore, because we could not run the full query set for Shark on RDDs, we used only the partition necessary for the Interactive queries to do the single-user and 10-user comparisons.

In this run, Impala widened its performance advantage, performing 9.5x better than the next best alternative:

Throughput and Hardware Utilization

In the above chart you can see that under the (simulated) load of 10 concurrent users, Impala slows down by 1.9x, whereas for other SQL implementations, query performance slows by 2.6x – 8.6x under the same load. This performance difference translates into quality of experience as perceived by the BI user.

We also measured total throughput, or how many queries the system could process in a given hour — which has an impact on the quantity of hardware required to run a SQL workload at a targeted performance level. This metric is a big influence on TCO, where the carrying cost of hardware is typically two-thirds of the TCO of a Hadoop system.

It’s perhaps surprising to see Shark running on data cached as RDDs resulting in slightly slower single-user queries than Shark running directly on HDFS — because the data in HDFS was already in memory (local cache) and RDDs only added overhead. This disparity will widen over time now that HDFS supports in-memory reads (HDFS-4949), which are more efficient than the OS buffer cache. In addition, in-memory writes are planned for an upcoming HDFS release (HDFS-5851). (In the coming months, we plan to re-run these benchmarks using updated versions and with HDFS caching configured.)

CPU efficiency explains how Impala is able to provide lower latency and higher throughput than the alternatives, and why a native high-performance MPP query engine offers benefits that just porting Hive onto a DAG (either Tez or Spark) does not. While utilizing a DAG removes additional I/O costs beyond the initial scan of the data, most of the performance and concurrency gains come from the CPU efficiency of the query engine itself.

Beyond Performance

Based on these results, Impala not only outperforms its nearest competitors, but also proved itself to be a more robust system that requires less manual tuning:

  • The other systems required significant rewrites of the original queries in order to run, while Impala could run the original as well as modified queries.

    Deep knowledge about how to rewrite SQL statements was required to ensure a head-to-head comparison across non-Impala systems to avoid even slower response times and outright query failures, in some cases. For most users of applications or BI tools, such manual writing of queries is highly undesirable, if not impossible.   In contrast, Impala’s cost-based optimizer and predicate propagation capability allows it to run the queries in the original SQL-89 form of the TPC-DS-derived benchmark or the modified versions with identical performance. Manual predicate propagation in particular is often challenging for users; traditional databases provide automatic propagation similar to that of Impala and incorrect placements can lead to wrong results.

  • Some systems require manual tuning of the JVM’s garbage collection parameters. Presto in particular required manual tuning of Java garbage collection in order to achieve its results. Likewise, Shark’s inability to run without manual query hints was partially due to Shark’s dependence on JVM memory management. And Tez either needs more time for startup and smaller queries when running queries in separate containers, or runs into similar challenges when reusing containers.   Impala’s query execution, however, is written in native code, which not only leads to greater performance and CPU efficiency as demonstrated above, but also offers a more stable multi-user service similar to traditional MPP query engines.

Conclusion

In summary, these new results prove out that Impala achieves better concurrent latency than its competitors while providing high query throughput, and with a far smaller CPU footprint. Furthermore, out of the entire comparative set, only Impala was able to run the queries in their original SQL-89-style join format without modification.

The results above help demonstrate that despite significant engineering investments into alternatives, Impala uniquely delivers on the requirements for BI and SQL analytics by combining:

  • Interactive SQL
  • Ability to handle highly-concurrent workloads
  • Efficient resource usage (so Impala is a “good citizen” in a shared workload environment)
  • Open formats for accessing any data
  • Multi-vendor support (from Cloudera, MapR, and Amazon) to avoid lock-in, and
  • Broad ISV support

As usual, we invite you to do the same testing for yourselves using our openly published benchmark kit — any and all feedback is welcome and appreciated. We’ll bring you more news over time as Impala continues to hit its milestones!


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