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From Craig Russell <apache....@gmail.com>
Subject Re: [Result][Vote] vote for IoTDB incubation proposal
Date Tue, 20 Nov 2018 00:36:50 GMT
FTR, this is SOP for incubator podlings.

Here's what needs to happen, in any order.

Move the code to an Apache repository
Establish the provenance of every line of code
For each line of code, contact the author and get a license for it (one of: SGA, ICLA, CCLA)

If you cannot find the author for some small pieces of code or the author is unresponsive,
you can ignore getting the license but make a note of the issue. For example, some drive-by
patches of several (up to dozens of) lines of code or a few paragraphs of documentation. 

Specifically, relicensing and changing headers on code should be done after the code has arrived
in Apache. That way, the changes are recorded in Apache source control. Ideally the author
should make the header changes.

As part of due diligence before the first release, all the above should be done. 

Regards,

Craig

> On Nov 15, 2018, at 4:27 AM, hxd <hxdreg@qq.com> wrote:
> 
> Currently, there are 6 repositories (IoTDB, IoTDB-JDBC, TsFile, Spark-Connector, Hive-Connector,
and Grafana-Connector) totally and we will merge them all in one repositories. 
> 
> Only the first one is private. 
> 
> Actually we are lack of experiences about how to open source. 
> 
> Should we open all the source now or after all the Apache legal documents are done? 
> 
> Best,
> 
> Xiangdong Huang  
> 
>> 在 2018年11月15日,下午5:06,Willem Jiang <willem.jiang@gmail.com>
写道:
>> 
>> Here is a question for the source code repository
>> 
>> The main source git repo[1] is still a private repo.  I think we need
>> to open source the repo before sending the SGA?
>> 
>> 
>> [1]https://github.com/thulab/iotdb
>> 
>> Willem Jiang
>> 
>> Twitter: willemjiang
>> Weibo: 姜宁willem
>> On Thu, Nov 15, 2018 at 4:08 PM hxd <hxdreg@qq.com> wrote:
>>> 
>>> Hi,
>>> 
>>> In the proposal discussion process, we got 3 mentors,  Justin Mclean, Christofer
Dutz, and Willem Ning Jiang.
>>> 
>>> In the vote process, we got a new mentor, Joe Witt.
>>> 
>>> Totally, there are one Champion and four mentors, they are:
>>> 
>>> Kevin A. McGrail (the Champion),
>>> Justin Mclean,
>>> Christofer Dutz,
>>> Willem Ning Jiang, and
>>> Joe Witt
>>> 
>>> I have checked their name on http://people.apache.org/committer-index.html, and
they are accurate now.
>>> The name list on the proposal list (https://wiki.apache.org/incubator/IoTDBProposal)
is also correct.
>>> 
>>> Regards,
>>> Xiangdong Huang
>>> 
>>> 
>>> 
>>> 在 2018年11月15日,上午12:51,Kevin A. McGrail <kmcgrail@apache.org>
写道:
>>> 
>>> Congratulations!  As champion, I think the next steps are:
>>> 
>>> 1 - Xiangdong, Can you confirm the list of mentors on the proposal is accurate?
>>> 
>>> 2 - Also Xiangdong, Is there anyone else that stepped forward as a mentor during
the voting process that the project wants the IPMC to approve?
>>> 
>>> 3 - Justin, I think you have to request the creation of the podling and then
I as champion work on things like the meta data file from this page,
>>> https://incubator.apache.org/policy/incubation.html, correct?
>>> 
>>> Regards,
>>> KAM
>>> 
>>> 
>>> 
>>> 
>>> --
>>> Kevin A. McGrail
>>> VP Fundraising, Apache Software Foundation
>>> Chair Emeritus Apache SpamAssassin Project
>>> https://www.linkedin.com/in/kmcgrail - 703.798.0171
>>> 
>>> 
>>> On Wed, Nov 14, 2018 at 6:29 AM hxd <hxdreg@qq.com> wrote:
>>>> 
>>>> Hi,
>>>> 
>>>> With 8 +1 binding votes,  2 +1 non-binding votes and No +/-0 or -1 votes,
this VOTE passes.
>>>> 
>>>> Thanks to everyone who voted!
>>>> 
>>>> Bellow is a voting tally:
>>>> 
>>>> Binding
>>>> Von Gosling
>>>> Christofer Dutz
>>>> Kevin A. McGrail
>>>> Felix Cheung
>>>> Matt Sticker
>>>> Joe Witt
>>>> Justin Mclean
>>>> Willem Jiang
>>>> 
>>>> 
>>>> Non-binding
>>>> Sheng Wu
>>>> Yang Bo
>>>> 
>>>> The vote thread: https://lists.apache.org/thread.html/077f029ab2b52a2b19fc8d41c07438f660a8e93dd87b3895d262263c@%3Cgeneral.incubator.apache.org%3E<https://lists.apache.org/thread.html/077f029ab2b52a2b19fc8d41c07438f660a8e93dd87b3895d262263c@%3Cgeneral.incubator.apache.org%3E>
>>>> The proposal: https://wiki.apache.org/incubator/IoTDBProposal <https://wiki.apache.org/incubator/IoTDBProposal>
>>>> 
>>>> Thanks,
>>>> 
>>>> Xiangdong Huang
>>>> 
>>>> 
>>>>> 在 2018年11月7日,下午3:46,hxd <hxdreg@qq.com> 写道:
>>>>> 
>>>>> Hi,
>>>>> 
>>>>> Sorry for the previous mail with bad format.
>>>>> I'd like to call a VOTE to accept IoTDB project, a database for managing
large amounts of time series data  from IoT sensors in industrial applications, into the Apache
Incubator.
>>>>> The full proposal is available on the wiki: https://wiki.apache.org/incubator/IoTDBProposal
>>>>> and it is also attached below for your convenience.
>>>>> 
>>>>> Please cast your vote:
>>>>> 
>>>>> [ ] +1, bring IoTDB into Incubator
>>>>> [ ] +0, I don't care either way,
>>>>> [ ] -1, do not bring IoTDB into Incubator, because...
>>>>> 
>>>>> The vote will open at least for 72 hours.
>>>>> 
>>>>> Thanks,
>>>>> Xiangdong Huang.
>>>>> 
>>>>> 
>>>>> = IoTDB Proposal  =
>>>>> v0.1.1
>>>>> 
>>>>> 
>>>>> == Abstract ==
>>>>> IoTDB is a data store for managing large amounts of time series data
such as timestamped data from IoT sensors in industrial applications.
>>>>> 
>>>>> == Proposal ==
>>>>> IoTDB is a database for managing large amount of time series data with
columnar storage, data encoding, pre-computation, and index techniques. It has SQL-like interface
to write millions of data points per second per node and is optimized to get query results
in few seconds over trillions of data points. It can also be easily integrated with Apache
Hadoop MapReduce and Apache Spark for analytics.
>>>>> 
>>>>> == Background ==
>>>>> 
>>>>> A new class of data management system requirements is becoming increasingly
important with the rise of the Internet of Things. There are some database systems and technologies
aimed at time series data management.  For example, Gorilla and InfluxDB which are mainly
built for data centers and monitoring application metrics. Other systems, for example, OpenTSDB
and KairosDB, are built on Apache HBase and Apache Cassandra, respectively.
>>>>> 
>>>>> However, many applications for time series data management have more
requirements especially in industrial applications as follows:
>>>>> 
>>>>> * Supporting time series data which has high data frequency. For example,
a turbine engine may generate 1000 points per second (i.e., 1000Hz), while each CPU only reports
1 data points per 5 seconds in a data center monitoring application.
>>>>> 
>>>>> * Supporting scanning data multi-resolutionally. For example, aggregation
operation is important for time series data.
>>>>> 
>>>>> * Supporting special queries for time series, such as pattern matching,
time series segmentation, time-frequency transformation and frequency query.
>>>>> 
>>>>> * Supporting a large number of monitoring targets (i.e. time series).
An excavator may report more than 1000 time series, for example, revolving speed of the motor-engine,
the speed of the excavator, the accelerated speed, the temperature of the water tank and so
on, while a CPU or an application monitor has much fewer time series.
>>>>> 
>>>>> * Optimization for out-of-order data points. In the industrial sector,
it is common that equipment sends data using the UDP protocol rather than the TCP protocol.
Sometimes, the network connect is unstable and parts of the data will be buffered for later
sending.
>>>>> 
>>>>> * Supporting long-term storage. Historical data is precious for equipment
manufacturers. Therefore, removing or unloading historical data is highly desired for most
industrial applications. The database system must not only support fast retrieval of historical
data, but also should guarantee that the historical data does not impact the processing speed
for “hot” or current data.
>>>>> 
>>>>> * Supporting online transaction processing (OLTP) as well as complex
analytics. It is obvious that supporting analyzing from the data files using Apache Spark/Apache
Hadoop MapReduce directly is better than transforming data files to another file format for
Big Data analytics.
>>>>> 
>>>>> * Flexible deployment either on premise or in the cloud.  IoTDB is as
simple and can be deployed on a Raspberry Pi handling hundreds of time series. Meanwhile,
the system can be also deployed in the cloud so that it supports tens of millions ingestions
per second, OLTP queries in milliseconds, and analytics using Apache Spark/Apache Hadoop MapReduce.
>>>>> 
>>>>> * * (1) If users deploy IoTDB on a device, such as a Raspberry Pi, a
wind turbine, or a meteorological station, the deployment of the chosen database is designed
to be simple. A device may have hundreds of time series (but less than a thousand time series)
and the database needs to handle them.
>>>>> * * (2) When deploying IoTDB in a data center, the computational resources
(i.e., the hardware configuration of servers) is not a problem when compared to a Raspberry
Pi. In this deployment, IoTDB can use more computation resources, and has the ability to handle
more time seires (e.g., millions of time series).
>>>>> 
>>>>> Based on these requirements, we developed IoTDB, a new data store system
for managing time series data.
>>>>> 
>>>>> IoTDB started as a Tsinghua University research project. IoTDB's developer
community has also grown to include additional institutions, for example, universities (e.g.,
Fudan University), research labs (e.g, NEL-BDS lab), and corporations (e.g., K2Data, Tencent).
Funding has been provided by various institutions including the National Natural Science Foundation
of China, and industry sponsors, such as Lenovo and K2Data.
>>>>> 
>>>>> == Rationale ==
>>>>> Because there is no existed open-sourced time series databases covering
all the above requirements, we developed IoTDB. As the system matures, we are seeking a long-term
home for the project. We believe the Apache Software Foundation would be an ideal fit. Also
joining Apache will help coordinate and improve the development effort of the growing number
of organizations which contribute to IoTDB improving the diversity of our community.
>>>>> 
>>>>> IoTDB contains multiple modules, which are classified into categories:
>>>>> 
>>>>> * '''TsFile Format''': TsFile is a new columnar file format.
>>>>> * '''Adaptor for Analytics and Visualization''': Integrating TsFile with
Apache Hadoop HDFS, Apache Hadoop MapReduce and Apache Spark. Examples of integrating IoTDB
with Apache Kafka, Apache Storm and Grafana are also provided.
>>>>> * '''IoTDB Engine''': An engine which consists of SQL parser, query plan
generator, memtable, authentication and authorization,write ahead log (WAL), crash recovery,
out-of-order data handler, and index for aggregation and pattern matching. The engine stores
system data in TsFile format.
>>>>> * '''IoTDB JDBC''': An implementation of Java Database Connectivity (JDBC)
for clients to connect to IoTDB using Java.
>>>>> 
>>>>> === TsFile Format ===
>>>>> 
>>>>> TsFile format is a columnar store, which is similar with Apache Parquet
and Apache CarbonData. It has the concepts of Chunk Group, Column Chunk, Page and Footer.
Comparing with Apache Parquet and Apache CarbonData, it is designed and optimized for time
series:
>>>>> 
>>>>> ==== Time Series Friendly Encoding ====
>>>>> IoTDB currently supports run length encoding (RLE), delta-of-delta encoding,
and Facebook's Gorilla encoding.
>>>>> 
>>>>> Lossy encoding methods (e.g., Piecewise Linear Approximation (PLA) and
time-frequency transformation are works-in-progress.
>>>>> 
>>>>> 
>>>>> ==== Chunk Group ====
>>>>> The data part of a TsFile consists of many Chunk Groups. Each Chunk Group
stores the data of a device at a time interval.  A Chunk Group is similar to the row group
in Apache Parquet, while there are some constraints of the time dimension:  For each device,
the time intervals of different Chunk Groups are not overlapped and the latter Chunk Group
always has a larger timestamp.
>>>>> 
>>>>> Given a TsFile and a query with a time range filter, the query process
can terminate scanning data once it reads data points whose timestamp reaches the time limit
of the filter. We call the feature ''fast-return'' and it makes the time range query in a
TsFile very efficient.
>>>>> 
>>>>> 
>>>>> 
>>>>> ==== Different Column Chunk Format (Unnecessary the Repetition (R) and
Definition (D) Fields) ====
>>>>> 
>>>>> While Apache Parquet and Apache CarbonData support complex data types,
e.g., nested data and sparse columns, TsFile is exclusively designed for time series whose
data model is \<device_id, series_id, timestamp, value\>.
>>>>> 
>>>>> In a `Chunk Group`, each time series is a `Column Chunk`. Even though
these time series belong to the same device, the data points in different time series are
not aligned in the time dimension originally.
>>>>> 
>>>>> For example, if you have a device with 2 sensors on the same data collection
frequencies, sensor 1 may collect data at time 1521622662000 while the other one collects
data at time 1521622662001 (delta=1ms). Therefore, each Column Chunk has its timestamps and
values, which is quite different from Apache Parquet and Apache CarbonData.  Because we store
the time column along with each value column instead of making different chunks share the
same time column for the sake of diverse data frequency for different time series, we do not
store any null value on disk to align across time series. Besides, we do not need to attach
 `repetition` (R) and `definition` (D) fields on each value. Therefore, the disk space is
saved and the query latency is reduced (because we do not align data by calculating R and
D fields).
>>>>> 
>>>>> 
>>>>> ==== Domain Specific Information in Each Page ====
>>>>> Similar to Apache Parquet and Apache CarbonData, a `Column Chunk` consists
of several `Pages`, and each `Page` has a `Page header`. The `Page header` is a summary of
the data in the page.
>>>>> 
>>>>> Because TsFile is optimized for time series, the page header contains
more domain specific information, such as the minimal and maximal value, the minimal and the
maximal timestamp, the frequency and so on. TsFile can even store the histogram of values
in the page header.
>>>>> 
>>>>> This header information helps IoTDB in speeding up queries by skipping
unnecessary pages.
>>>>> 
>>>>> 
>>>>> === Adaptor for Analytics ===
>>>>> The TsFile provides:
>>>>> 
>>>>> * InputFormat/OutputFormat interfaces for Reading/Writing data.
>>>>> * Deep integration with Apache Spark/Hadoop MapReduce including predicate
push-down, column pruning, aggregation push down, etc. So users can use Apache Spark SQL/HiveQL
to connect and query TsFiles.
>>>>> 
>>>>> 
>>>>> === IoTDB Engine ===
>>>>> The IoTDB engine is a database engine, which uses TsFile as its storage
file format. The IoTDB Engine supports SQL-like query plus many useful functions:
>>>>> 
>>>>> * Tree-based time series schema
>>>>> * Log-Structured Merge (LSM)-based storage
>>>>> * Overflow file for out-of-order data
>>>>> * Scalable index framework
>>>>> * Special queries for time series
>>>>> 
>>>>> ==== Tree-based Time Series Schema ====
>>>>> IoTDB manages all the time series definitions using a tree structure.
A path from the root of the tree to a leaf node represents a time series. Therefore, the unique
id of a time series is a path, e.g., `root.China.beijing.windFarm1.windTurbine1.speed`.
>>>>> 
>>>>> This kind of schema can express `group by` naturally. For example, `root.China.beijing.windFarm1.*.speed`
represents the speed of all the wind turbines in wind farm 1 in Beijing, China.
>>>>> 
>>>>> ==== Log-Structured Merge (LSM)-based Storage ====
>>>>> In a time series, the data points should be ordered by their timestamps.
In IoTDB, we use Log-Structured Merge (LSM) based mechanism. Therefore, a part of the data
is stored in memory first and can be called as `memtable`. At this time, if data points come
out-of-order, we resort them in memory. When this part of data exceeds the configured memory
limit, we flush it on disk as a `Chunk Group` into an unclosed TsFile.  Finally, a TsFile
may contain several Chunk Groups, for reducing the number of small data files, which is helpful
to reduce the I/O load of the storage system and reduces the execution time of a file-merge
in LSM. Notice that the data is time-ordered in one Chunk Group on disk, and this layout is
helpful for fast filtering in one Chunk Group for a query.
>>>>> 
>>>>> Rule 1: In a TsFile, the Chunk Groups of one device are ordered by timestamp
(Rule 1), and it is helpful for fast filtering among Chunk Groups for a query.
>>>>> 
>>>>> Rule 2: When the size of the unclosed TsFile reaches the threshold defined
in the configuration file, we close the file and generate a new one to store new arriving
data spanning the entire data set. Like many systems which use LSM-based storage, we never
modify a TsFile which has been closed except for the file-merge process (Rule 2).
>>>>> 
>>>>> Rule 3: To reduce the number of TsFiles involved in a query process,
we guarantee that the data points in different TsFiles are not overlapping on the time dimension
after file mergence (Rule 3).
>>>>> 
>>>>> ==== Overflow File for Out-of-order Data ====
>>>>> When a part of data is flushed on disk (and will form a `Chunk Group`
in a TsFile), the newly arriving data points whose timestamps are smaller than the largest
timestamp in the Tsfile are `out-of-order`.
>>>>> 
>>>>> To store the out-of-order data, we organize all the troublesome `out-of-order`
data point insertions into a special TsFile, named `UnSequenceTsFile`. In an UnSequenceTsFile,
the Chunk Groups of one device may be overlapping in the time dimension, which violates the
Rule 1 and costs additional time compared to a normal TsFile for query filtering.
>>>>> 
>>>>> There is another special operation: updating all the data points in a
time range, e.g., `update all the speed values of device1 as 0 where the data time is in [1521622000000,
1521622662000]`. The operation is called when: (1) a sensor malfunctions and the database
receives wrong data for a period; (2) we may want to reset all the records. Many NoSQL time
series databases do not support such an operation. To support the operation in IoTDB, we use
a tree-based structure, Treap, to store this part of operations and store them as `Overflow`
files.
>>>>> 
>>>>> Therefore, there are 3 kinds of data files: TsFiles, UnSequenceTsFiles
and Overflow files.  TsFiles should store most of the data. The volume of UnSequenceTsFiles
depends on the workload: if there are too many out-of-order and the time span of out-of-order
is huge, the volume will be large. Overflow files handle fewest data operations but will depend
on the use of the special operations.
>>>>> 
>>>>> ==== LSM-tree ====
>>>>> Normally, LSM-based storage engines merge data files level by level so
that it looks like a tree structure. In this way, data is well organized. The disadvantage
is that data will be read and written several times. If the tree has 4 levels, each data point
will be rewritten at least 4 times.
>>>>> 
>>>>> Currently, we do not merge all the TsFiles into one because (1) the number
of TsFiles is kept lower than many LSM storage engines because a memtable is mapped to several
Chunk Groups rather than a file; (2) different TsFiles are not overlapping with each other
in the time dimension (because of Rule 3).
>>>>> 
>>>>> As mentioned before,  TsFile supports ''fast-return'' to accelerate queries.
However, UnSequenceTsFile and Overflow files do not allow this feature. The time spans of
UnSequenceTsFile, Overflow file andTsFile may be overlapped, which leads to more files involved
in the query process. To accelerate these queries, there is a merging process to reorganize
files in the background. All the three kinds of files: TsFiles, UnSequenceTsFiles and Overflow
files, are involved in the merging process. The merging process is implemented using multi-threading,
while each thread is responsible for a series family.
>>>>> After merging, only TsFiles are left. These files have non-overlapping
time spans and support the ''fast-return'' feature.
>>>>> 
>>>>> ==== Scalable Index Framework ====
>>>>> We allow users to implement indexes for faster queries. We currently
support an index for pattern matching query (KV-Match index, ICDE 2019). Another index for
fast aggregation (PISA index, CIKM 2016) is a work-in-progress.
>>>>> 
>>>>> ==== Special Queries ====
>>>>> We currently support `group by time interval` aggregation queries and
`Fill by` operations, which are similar to those of InfluxDB. Time series segmentation operations
and frequency queries are work-in-progress.
>>>>> 
>>>>> == Initial Goals ==
>>>>> The initial goals are to be open sourced and to integrate with the Apache
development process. Furthermore, we plan for incremental development, and releases along
with the Apache guidelines.
>>>>> 
>>>>> == Current Status ==
>>>>> We have developed the system for more than 2 years. There are currently
13k lines of code, some of which are generated by Antlr3 and Thrift.  There are 230 issues
which have been solved and more than 1500 commits.
>>>>> 
>>>>> The system has been deployed in the staging environment of the State
Grid Corporation of China to handle ~3 million time series (i.e, ~30,000 power generation
assembly * ~100 sensors) and an equipment service company in China managing ~2 million time
series (i.e, ~20k devices * 100 sensors). The insertion speed reaches ~2 million points/second/node,
which is faster than InfluxDB, OpenTSDB and Apache Cassandra in our environment.
>>>>> 
>>>>> There are many new features in the works including those mentioned herein.
We will add more analytics functions, improve the data file merge process, and finish the
first released version of IoTDB.
>>>>> 
>>>>> == Meritocracy ==
>>>>> The IoTDB project operates on meritocratic principles. Developers who
submit more code with higher quality earn more merit. We have used `Issues` and `Pull Requests`
modules on Github for collecting users' suggestions and patches. Users who submit issues,
pull requests, documents and help the community management are welcomed and encouraged to
become committers.
>>>>> 
>>>>> == Community ==
>>>>> 
>>>>> The IoTDB project users communicate on Github (
>>>>> https://github.com/thulab/tsfile) . Developers make the communication
on a website which is similar with JIRA (Currently, only registered users can apply to access
the project for communication, url: https://tower.im/projects/36de8571a0ff4833ae9d7f1c5c400c22/
>>>>> ). We have also introduced IoTDB at many technical conferences. Next,
we will build the mailing list for more convenience, broader communication and archived discussions.
>>>>> 
>>>>> If IoTDB is accepted for incubation at the Apache Software Foundation,
the primary goal is to build a larger community. We believe that IoTDB will become a key project
for time series data management, and so, we will rely on a large community of users and developers.
>>>>> 
>>>>> TODO: IoTDB is currently on a private Github repository (
>>>>> https://github.com/thulab/iotdb), while its subproject TsFile (a file
format for storing time series data) is open sourced on Github (https://github.com/thulab/tsfile
>>>>> ).
>>>>> 
>>>>> == Core Developers ==
>>>>> IoTDB was initially developed by 2 dozen of students and teachers at
Tsinghua University. Now, more and more developers have joined coming from other universities:
Fudan University, Northwestern Polytechnical University and Harbin Institute of Technology
in China.  Other developers come from business companies such as Lenovo and Microsoft. We
will be working to bring more and more developers into the project making contributions to
IoTDB.
>>>>> 
>>>>> == Relationships with Other Apache Products ==
>>>>> IoTDB requires some Apache products (Apache Thrift, commons, collections,
httpclient).
>>>>> 
>>>>> IoTDB-Spark-connector and IoTDB-Hadoop-connector have been developed
for supporting analysing time series data by using Apache Spark and MapReduce.
>>>>> 
>>>>> Overall, IoTDB is designed as an open architecture, and it can be integrated
with many other systems in the future.
>>>>> 
>>>>> As mentioned before, in the IoTDB project, we designed a new columnar
file format, called TsFile, which is similar to Apache Parquet. However, the new file format
is optimized for time series data.
>>>>> 
>>>>> 
>>>>> 
>>>>> == Known Risks ==
>>>>> 
>>>>> === Orphaned Products ===
>>>>> Given the current level of investment in IoTDB, the risk of the project
being abandoned is minimal. Time series data is more and more important and there are several
constituents who are highly inspired to continue development. Tsinghua and NEL-BDS Lab relies
on IoTDB as a platform for a large number of long-term research projects. We have deployed
IoTDB in some company's staging environments for future applications.
>>>>> 
>>>>> === Inexperience with Open Source ===
>>>>> Students and researchers in Tsinghua University have been developing
and using open source software for a long time. It is wonderful to be guided to join a formal
open-source process for students. Some of our committers
>>>>> have  experiences contributing to open source, for example:
>>>>> 
>>>>> * druid:
>>>>> https://github.com/druid-io/druid/commit/f18cc5df97e5826c2dd8ffafba9fcb69d10a4d44
>>>>> 
>>>>> * druid:
>>>>> https://github.com/druid-io/druid/commit/aa7aee53ce524b7887b218333166941654788794
>>>>> 
>>>>> * YCSB:
>>>>> https://github.com/brianfrankcooper/YCSB/pull/776
>>>>> 
>>>>> 
>>>>> Additionally, several ASF veterans and industry veterans have agreed
to mentor the project and are listed in this proposal. The project will rely on their guidance
and collective wisdom to quickly transition the entire team of initial committers towards
practicing the Apache Way.
>>>>> 
>>>>> 
>>>>> === Reliance on Salaried Developers ===
>>>>> Most of current developers are students and researchers/professors in
universities, and their researches focus on big data management and analytics. It is unlikely
that they will change their research focus away from big data management.  We will work to
ensure that the ability for the project to continuously be stewarded and to proceed forward
independent of salaried developers is continued.
>>>>> 
>>>>> === An Excessive Fascination with the Apache Brand ===
>>>>> Most of the initial developers come from Tsinghua University with no
intent to use the Apache brand for profit. We have no plans for making use of Apache brand
in press releases nor posting billboards advertising acceptance of IoTDB into Apache Incubator.
>>>>> 
>>>>> 
>>>>> == Initial Source ==
>>>>> IoTDB's github address and some required dependencies:
>>>>> 
>>>>> * The storage file format:
>>>>> https://github.com/thulab/tsfile
>>>>> 
>>>>> * Adaptor for Apache Hadoop MapReduce:
>>>>> https://github.com/thulab/tsfile-hadoop-connector
>>>>> 
>>>>> * Adaptor for Apache Spark:
>>>>> https://github.com/thulab/tsfile-spark-connector
>>>>> 
>>>>> * Adaptor for Grafana:
>>>>> https://github.com/thulab/iotdb-grafana
>>>>> 
>>>>> * The database engine:
>>>>> https://github.com/thulab/iotdb
>>>>> (private project up to now)
>>>>> * The client driver:
>>>>> https://github.com/thulab/iotdb-jdbc
>>>>> 
>>>>> 
>>>>> 
>>>>> === External Dependencies ===
>>>>> To the best of our knowledge, all dependencies of IoTDB are distributed
under Apache compatible licenses. Upon acceptance to the incubator, we would begin a thorough
analysis of all transitive dependencies to verify this fact and introduce license checking
into the build and release process.
>>>>> 
>>>>> == Documentation ==
>>>>> * Documentation for TsFile:
>>>>> https://github.com/thulab/tsfile/wiki
>>>>> 
>>>>> * Documentation for IoTDB and its JDBC:
>>>>> http://tsfile.org/document
>>>>> (Chinese only. An English version is in progress.)
>>>>> 
>>>>> == Required Resources ==
>>>>> === Mailing Lists ===
>>>>> *
>>>>> private@iotdb.incubator.apache.org
>>>>> 
>>>>> *
>>>>> dev@iotdb.incubator.apache.org
>>>>> 
>>>>> *
>>>>> commits@iotdb.incubator.apache.org
>>>>> 
>>>>> 
>>>>> === Git Repositories ===
>>>>> *
>>>>> https://git-wip-us.apache.org/repos/asf/incubator-iotdb.git
>>>>> 
>>>>> 
>>>>> === Issue Tracking ===
>>>>> *  JIRA IoTDB (We currently use the issue management provided by Github
to track issues.)
>>>>> 
>>>>> 
>>>>> == Initial Committers ==
>>>>> Tsinghua University, K2Data Company, Lenovo, Microsoft
>>>>> 
>>>>> Jianmin Wang (jimwang at tsinghua dot edu dot cn )
>>>>> 
>>>>> Xiangdong Huang (sainthxd at gmail dot com)
>>>>> 
>>>>> Jun Yuan (richard_yuan16 at 163 dot com)
>>>>> 
>>>>> Chen Wang ( wang_chen at tsinghua dot edu dot cn)
>>>>> 
>>>>> Jialin Qiao (qjl16 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Jinrui Zhang (jinrzhan at microsoft dot com)
>>>>> 
>>>>> Rong Kang (kr11 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Tian Jiang(jiangtia18 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Shuo Zhang (zhangshuo at k2data dot com dot cn)
>>>>> 
>>>>> Lei Rui (rl18 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Rui Liu (liur17 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Kun Liu (liukun16 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Gaofei Cao (cgf16 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Xinyi Zhao (xyzhao16 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Dongfang Mao (maodf17 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Tianan Li(lta18 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Yue Su (suy18 at mails dot tsinghua dot edu dot cn)
>>>>> 
>>>>> Hui Dai (daihui_iot at lenovo dot com, yuct_iot at lenovo dot com )
>>>>> 
>>>>> == Sponsors ==
>>>>> === Champion ===
>>>>> Kevin A. McGrail (
>>>>> kmcgrail@apache.org
>>>>> )
>>>>> 
>>>>> === Nominated Mentors ===
>>>>> Justin Mclean (justin at classsoftware dot com)
>>>>> 
>>>>> Christofer Dutz (christofer.dutz at c-ware dot de)
>>>>> 
>>>>> Willem Jiang (willem.jiang at gmail dot com)
>>>>> 
>>>>> 
>>> 
>>> 
>> 
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>> 
> 
> 
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Craig L Russell
Secretary, Apache Software Foundation
clr@apache.org <mailto:clr@apache.org> http://db.apache.org/jdo <http://db.apache.org/jdo>

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