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From Aaron Davidson <>
Subject Re: Shark vs Impala
Date Mon, 23 Jun 2014 07:50:57 GMT
Note that regarding a "long load time", data format means a whole lot in
terms of query performance. If you load all your data into compressed,
columnar Parquet files on local hardware, Spark SQL would also perform far,
far better than it would reading from gzipped S3 files. You must also be
careful about your queries; certain queries can be answered much more
efficiently due to specific optimizations implemented in the query engine.
For instance, Parquet keeps statistics. so you could theoretically do a
count(*) over petabytes of data in less than a second, blowing away any
competition that resorts to actually reading data.

On Sun, Jun 22, 2014 at 6:24 PM, Matei Zaharia <>

> In this benchmark, the problem wasn’t that Shark could not run without
> enough memory; Shark spills some of the data to disk and can run just fine.
> The issue was that the in-memory form of the RDDs was larger than the
> cluster’s memory, although the raw Parquet / ORC files did fit in memory,
> so Cloudera did not want to run an “RDD” number where some of the RDD is
> not in memory. But the wording “could not complete” is confusing — the
> queries complete just fine.
> We do plan to update the AMPLab benchmark with Spark SQL as well, and
> expand it to include more of TPC-DS.
> Matei
> On Jun 22, 2014, at 9:53 AM, Debasish Das <>
> wrote:
> 600s for Spark vs 5s for Redshift...The numbers look much different from
> the amplab benchmark...
> Is it like SSDs or something that's helping redshift or the whole data is
> in memory when you run the query ? Could you publish the query ?
> Also after spark-sql are we planning to add spark-sql runtimes in the
> amplab benchmark as well ?
> On Sun, Jun 22, 2014 at 9:13 AM, Toby Douglass <> wrote:
>> I've just benchmarked Spark and Impala.  Same data (in s3), same query,
>> same cluster.
>> Impala has a long load time, since it cannot load directly from s3.  I
>> have to create a Hive table on s3, then insert from that to an Impala
>> table.  This takes a long time; Spark took about 600s for the query, Impala
>> 250s, but Impala required 6k seconds to load data from s3.  If you're going
>> to go the long-initial-load-then-quick-queries route, go for Redshift.  On
>> equivalent hardware, that took about 4k seconds to load, but then queries
>> are like 5s each.

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