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 <hxd...@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 <kmcgr...@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 <hxd...@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 <hxd...@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 === >> > * >> > priv...@iotdb.incubator.apache.org >> > >> > * >> > d...@iotdb.incubator.apache.org >> > >> > * >> > comm...@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 ( >> > kmcgr...@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) >> > >> > > > --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
