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:"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

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

    has been verified as a success which can support low latency & high

    concurrency queries on a very large data scale. Thanks to HBase, most

    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

    result. So the user needs to do a good design about the row key;

    the query would be slow. This is difficult sometimes because the user

    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

    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,

    in the cloud.

    In one word, HBase is complicated to be Kylin’s storage. The

    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, have developed their customized storage engine for Kylin in

    their product or platform. In their experience, columnar storage is a

    supplement for the HBase engine. Kaisen Kang from has shared

    their KOD (Kylin on Druid) solution[3] in this August’s Kylin meetup in


    We plan to do a PoC with Apache Parquet + Apache Spark in the next

    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

    computation are separated, which makes the scaling in/out much easier

    before. Benefiting from Spark's flexibility, we can not only push down

    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

    storage engine. If you have good ideas or any related data, welcome
discuss in

    the community.

    Thank you!

    [1] Apache Kylin on HBase

    [2] Apache Kylin Plugin Architecture

    [3] 基于Druid的Kylin存储引擎实践

    Best regards,

    Shaofeng Shi 史少锋

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