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From "Li,De(BDG)" <>
Subject Looking for Champion
Date Fri, 08 Jun 2018 04:45:32 GMT
Hi all,

I am Reed, as a developer worked with the team for Palo (a MPP-based interactive SQL data

We propose to contribute Palo as an Apache Incubator project, and
we are still looking for possible Champion if anyone would like to volunteer. Thanks a lot.

Best Regards,

The draft of the proposal as below:

#Apache Palo


Palo is a MPP-based interactive SQL data warehousing for reporting and analysis.


We propose to contribute the Palo codebase and associated artifacts (e.g. documentation, web-site
content etc.) to the Apache Software Foundation with the intent of forming a productive, meritocratic
and open community around Palo’s continued development, according to the ‘Apache Way’.

Baidu owns several trademarks regarding Palo, and proposes to transfer ownership of those
trademarks in full to the ASF.

###Overview of Palo

Palo’s implementation consists of two daemons: Frontend (FE) and Backend (BE).

**Frontend daemon** consists of query coordinator and catalog manager. Query coordinator is
responsible for receiving users’ sql queries, compiling queries and managing queries execution.
Catalog manager is responsible for managing metadata such as databases, tables, partitions,
replicas and etc. Several frontend daemons could be deployed to guarantee fault-tolerance,
and load balancing.

**Backend daemon** stores the data and executes the query fragments. Many backend daemons
could also be deployed to provide scalability and fault-tolerance.

A typical Palo cluster generally composes of several frontend daemons and dozens to hundreds
of backend daemons.

Users can use MySQL client tools to connect any frontend daemon to submit SQL query. Frontend
receives the query and compiles it into query plans executable by the Backend. Then Frontend
sends the query plan fragments to Backend. Backend will build a query execution DAG. Data
is fetched and pipelined into the DAG. The final result response is sent to client via Frontend.
The distribution of query fragment execution takes minimizing data movement and maximizing
scan locality as the main goal.


At Baidu, Prior to Palo, different tools were deployed to solve diverse requirements in many
ways. And when a use case requires the simultaneous availability of capabilities that cannot
all be provided by a single tool, users were forced to build hybrid architectures that stitch
multiple tools together, but we believe that they shouldn’t need to accept such inherent
complexity. A storage system built to provide great performance across a broad range of workloads
provides a more elegant solution to the problems that hybrid architectures aim to solve. Palo
is the solution.

Palo is designed to be a simple and single tightly coupled system, not depending on other
systems. Palo provides high concurrent low latency point query performance, but also provides
high throughput queries of ad-hoc analysis. Palo provides bulk-batch data loading, but also
provides near real-time mini-batch data loading. Palo also provides high availability, reliability,
fault tolerance, and scalability.


Palo mainly integrates the technology of Google Mesa and Apache Impala.

Mesa is a highly scalable analytic data storage system that stores critical measurement data
related to Google's Internet advertising business. Mesa is designed to satisfy complex and
challenging set of users’ and systems’ requirements, including near real-time data ingestion
and query ability, as well as high availability, reliability, fault tolerance, and scalability
for large data and query volumes.

Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop
data processing environment. At present, by virtue of its superior performance and rich functionality,
Impala has been comparable to many commercial MPP database query engine. Mesa can satisfy
the needs of many of our storage requirements, however Mesa itself does not provide a SQL
query engine; Impala is a very good MPP SQL query engine, but the lack of a perfect distributed
storage engine. So in the end we chose the combination of these two technologies.

Learning from Mesa’s data model, we developed a distributed storage engine. Unlike Mesa,
this storage engine does not rely on any distributed file system. Then we deeply integrate
this storage engine with Impala query engine. Query compiling, query execution coordination
and catalog management of storage engine are integrated to be frontend daemon; query execution
and data storage are integrated to be backend daemon. With this integration, we implemented
a single, full-featured, high performance state the art of MPP database, as well as maintaining
the simplicity.

##Current Status

Palo has been an open source project on GitHub (


Palo has been deployed in production at Baidu and is applying more than 200 lines of business.
It has demonstrated great performance benefits and has proved to be a better way for reporting
and analysis based big data. Still We look forward to growing a rich user and developer community.


Palo seeks to develop developer and user communities during incubation.

###Core Developers

* Ruyue Ma (,<>)
* Chun Zhao (,<>)
* Mingyu Chen (,
* De Li(,<>
* Hao Chen (,<>)
* Chaoyong Li (,<>)
* Bin Lin (,<>)


Palo is related to several other Apache projects:

* Palo can also read data stored in Apache Hadoop clusters powered by the HDFS filesystem.
* Palo is closely integrated with Impala, which is also being proposed to the Incubator.
* Palo uses Apache Thrift as its RPC and serialization framework of choice.

##Known Risks

###Orphaned Products

The core developers of Palo team plan to work full time on this project. There is very little
risk of Palo getting orphaned since at least one large company (Baidu) is extensively using
it in their production. For example, currently there are more than 200 use cases using Palo
in production. Furthermore, since Palo was open sourced at the beginning of October 2017,
it has received more than 660 stars and been forked nearly 170 times. We plan to extend and
diversify this community further through Apache.

###Inexperience with Open Source

The core developers are all active users and followers of open source. They are already committers
and contributors to the Palo Github project. All have been involved with the source code that
has been released under an open source license, and several of them also have experience developing
code in an open source environment. Though the core set of Developers do not have Apache Open
Source experience, there are plans to onboard individuals with Apache open source experience
on to the project.

###Homogenous Developers

The most of core developers are from Baidu, but after Palo was open sourced, Palo received
a lot of bug fixes and enhancements from other developers not working at Baidu.

###Reliance on Salaried Developers

Baidu invested in Palo as the OLAP solution and some of its key engineers are working full
time on the project. In addition, since there is a growing Big Data need for scalable OLAP
solutions, we look forward to other Apache developers and researchers to contribute to the
project. Also key to addressing the risk associated with relying on Salaried developers from
a single entity is to increase the diversity of the contributors and actively lobby for Domain
experts in the BI space to contribute. Apache Palo intends to do this.

###An Excessive Fascination with the Apache Brand

Palo is proposing to enter incubation at Apache in order to help efforts to diversify the
committer-base, not so much to capitalize on the Apache brand. The Palo project is in production
use already inside Baidu, but is not expected to be an Baidu product for external customers.
As such, the Palo project is not seeking to use the Apache brand as a marketing tool.


Information about Palo can be found at The following links
provide more information about Palo in open source:

* Palo wiki site:
* Codebase at Github:
* Issue Tracking:
* Overview:
* FAQ:

##Initial Source

Palo has been under development since 2017 by a team of engineers at Baidu Inc. It is currently
hosted on under an Apache license at

##External Dependencies

Palo has the following external dependencies.

* Google gflags (BSD)
* Google glog (BSD)
* Apache Thrift (Apache Software License v2.0)
* Apache Commons (Apache Software License v2.0)
* Boost (Boost Software License)
* OpenLdap (OpenLDAP Software License)
* rapidjson (Tencent)
* Google RE2 (BSD-style)
* lz4 (BSD)
* snappy (BSD)
* cyrus-sasl (CMU License)
* Twitter Bootstrap (Apache Software License v2.0)
* d3 (BSD)
* LLVM (BSD-like)

Build and test dependencies:

* ant (Apache Software License v2.0)
* Apache Maven (Apache Software License v2.0)
* cmake (BSD)
* clang (BSD)
* Google gtest (Apache Software License v2.0)

##Required Resources

###Mailing List

There are currently no mailing lists. The usual mailing lists are expected to be set up when
entering incubation:<><><>

###Subversion Directory

Upon entering incubation:
After incubation, we want to move the existing repo from to
Apache infrastructure.

###Issue Tracking

Palo currently uses GitHub to track issues. Would like to continue to do so while we discuss
migration possibilities with the ASF Infra committee.

###Other Resources

The existing code already has unit tests so we will make use of existing Apache continuous
testing infrastructure. The resulting load should not be very large.

##Initial Committers

* Ruyue Ma (,<>)
* Chun Zhao (,<>)
* Mingyu Chen (,
* De Li(,<>
* Hao Chen (,<>)
* Chaoyong Li (,<>)
* Bin Lin (,<>)


The initial committers are employees of Baidu Inc.. The nominated mentors are employees of




###Nominated Mentors

* sijie guo,<>
* Luke Han,<>
* Zheng Shao,<>

###Sponsoring Entity

We are requesting the Incubator to sponsor this project.
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