As far as I'm concerned, using Parquet as Kylin's storage format is pretty
appropriate. From the aspect of integrating Spark, Spark made a lot of
optimizations for Parquet, e.g. We can enjoy Spark's vectorized reading and
lazy dict decoding, etc.
And here are my thoughts about integrating Spark and our query engine. As
Shaofeng mentioned, a cuboid is a Parquet file, and you can think of this
as a small table and we can read this cuboid as a DataFrame directly, which
can be directly queried by Spark, a bit like this:
ss.read.parquet("path/to/CuboidFile").filter("xxx").agg("xxx").select("xxx").
(We need to implement some Kylin's advanced aggregations, as for some
Kylin's basic aggregations like sum/min/max, we can use Spark's directly)
*Compare to our old query engine, the advantages are as follows:*
1. It is distributed! Our old query engine will get all data into a query
node and then calculate, it's a single point of failure and often leads OOM
when in a huge amount of data.
2. It is simple and easy to debug(every step is very clear and
transparent), you can collect data after every single phase,
e.g.(filter/aggregation/projection, etc.), so you can easily check out
which operation/phase went wrong. Our old query engine uses Calcite for
post-calculation, it's difficult when pinpointing problems, especially when
relating to code generation, and you cannot insert your own logic during
computation.
3. We can fully enjoy all efforts that Spark made for optimizing
performance, e.g. Catalyst/Tungsten, etc.
4. It is easy for unit tests, you can test every step separately, which
could reduce the testing granularity of Kylin's query engine.
5. Thanks to Spark's DataSource API, we can change Parquet to other data
formats easily.
6. A lot of upstream tools for Spark like many machine learning tools can
directly be integrated with us.
==================
======================================================================================================================
Hi Kylin developers.
HBase has been Kylin’s storage engine since the first day; Kylin on
HBase
has been verified as a success which can support low latency & high
concurrency queries on a very large data scale. Thanks to HBase, most
Kylin
users can get on average less than 1-second query response.
But we also see some limitations when putting Cubes into HBase; I shared
some of them in the HBaseConf Asia 2018[1] this August. The typical
limitations include:
- Rowkey is the primary index, no secondary index so far;
Filtering by row key’s prefix and suffix can get very different
performance
result. So the user needs to do a good design about the row key;
otherwise,
the query would be slow. This is difficult sometimes because the user
might
not predict the filtering patterns ahead of cube design.
- HBase is a key-value instead of a columnar storage
Kylin combines multiple measures (columns) into fewer column families
for
smaller data size (row key size is remarkable). This causes HBase often
needing to read more data than requested.
- HBase couldn't run on YARN
This makes the deployment and auto-scaling a little complicated,
especially
in the cloud.
In one word, HBase is complicated to be Kylin’s storage. The
maintenance,
debugging is also hard for normal developers. Now we’re planning to
seek a
simple, light-weighted, read-only storage engine for Kylin. The new
solution should have the following characteristics:
- Columnar layout with compression for efficient I/O;
- Index by each column for quick filtering and seeking;
- MapReduce / Spark API for parallel processing;
- HDFS compliant for scalability and availability;
- Mature, stable and extensible;
With the plugin architecture[2] introduced in Kylin 1.5, adding multiple
storages to Kylin is possible. Some companies like Kyligence Inc and
Meituan.com, have developed their customized storage engine for Kylin in
their product or platform. In their experience, columnar storage is a
good
supplement for the HBase engine. Kaisen Kang from Meituan.com has shared
their KOD (Kylin on Druid) solution[3] in this August’s Kylin meetup in
Beijing.
We plan to do a PoC with Apache Parquet + Apache Spark in the next
phase.
Parquet is a standard columnar file format and has been widely
supported by
many projects like Hive, Impala, Drill, etc. Parquet is adding the page
level column index to support fine-grained filtering. Apache Spark can
provide the parallel computing over Parquet and can be deployed on
YARN/Mesos and Kubernetes. With this combination, the data persistence
and
computation are separated, which makes the scaling in/out much easier
than
before. Benefiting from Spark's flexibility, we can not only push down
more
computation from Kylin to the Hadoop cluster. Except for Parquet, Apache
ORC is also a candidate.
Now I raise this discussion to get your ideas about Kylin’s
next-generation
storage engine. If you have good ideas or any related data, welcome
discuss in
the community.
Thank you!
[1] Apache Kylin on HBase
https://www.slideshare.net/ShiShaoFeng1/apache-kylin-on-hbase-extreme-olap-engine-for-big-data
[2] Apache Kylin Plugin Architecture
https://kylin.apache.org/development/plugin_arch.html
[3] 基于Druid的Kylin存储引擎实践
https://blog.bcmeng.com/post/kylin-on-druid.html--
Best regards,
Shaofeng Shi 史少锋
