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
On Sun, Jun 22, 2014 at 5:53 PM, Debasish Das debasish.da...@gmail.com
wrote:
600s for Spark vs 5s for Redshift...The numbers look much different from
the amplab benchmark...
https://amplab.cs.berkeley.edu/benchmark/
Is it like SSDs or something that's helping redshift or the whole data is
On Mon, Jun 23, 2014 at 8:50 AM, Aaron Davidson ilike...@gmail.com wrote:
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,
Hi folks,
I was looking at the benchmark provided by Cloudera at
http://blog.cloudera.com/blog/2014/05/new-sql-choices-in-the-apache-hadoop-ecosystem-why-impala-continues-to-lead/
.
Is it real that Shark cannot execute some query if you don't have enough
memory?
And is it true/reliable that Impala
For the second question, I would say it is mainly because the projects have
not the same aim. Impala does have a cost-based optimizer and predicate
propagation capability which is natural because it is interpreting
pseudo-SQL query. In the realm of relational database, it is often not a
good idea
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,
600s for Spark vs 5s for Redshift...The numbers look much different from
the amplab benchmark...
https://amplab.cs.berkeley.edu/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
