Thanks Justin. It is a great pleasure for us to invite you as the project mentor. I will modify the wiki to add you on the mentor list. :D Thanks Kevin, you gave us so much help to make IoTDB Project better and we are excited to make contribution to ASF.
Best, Xiangdong Huang > 在 2018年10月29日，上午10:45，Kevin A. McGrail <kmcgr...@apache.org> 写道： > > Thanks Justin. That's awesome. > > I'd like to mention that I've worked closely with the IoTDB team for a few > weeks on improving the proposal after they gave an excellent initial > presentation as well. > > 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 Sun, Oct 28, 2018 at 10:42 PM Justin Mclean <justinmcl...@gmail.com> > wrote: > >> Hi, >> >> I'm interested in your project and would like to be a mentor if you'll have >> me. I'm the mentor fur a number of IoT projects here at the ASF. >> >> Thanks, >> Justin >> >> On Mon., 29 Oct. 2018, 11:35 am Xiangdong Huang, <hxd...@qq.com> wrote: >> >>> Dear Apache Incubator Community, >>> >>> >>> I would like to open up a discussion about incubating IoTDB at Apache. >>> IoTDB is a database for managing large amounts of time series data from >>> IoT sensors in industrial applications. >>> >>> >>> The proposal is available as a draft at >>> https://wiki.apache.org/incubator/IoTDBProposal . I have also included >>> the text of the proposal below. >>> >>> >>> >>> >>> = IoTDB Proposal = >>> v0.1 >>> >>> >>> == Abstract == >>> IoTDB is a database 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, Fundan University, Microsoft >>> >>> >>> Jianmin Wang ( jimwang at tsinghua dot edu dot cn ) >>> >>> >>> Jun Yuan (richard_yuan16 at 163 dot com ) >>> >>> >>> Chen Wang ( wang_chen at tsinghua dot edu dot cn) >>> >>> >>> Xiangdong Huang (sainthxd at gmail dot com) >>> >>> >>> 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） >>> >>> >>> 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) >>> >>> >>> Yi Xu(x-y16 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) >>> >>> >>> Wangminhao Gou(gwmh18 at mails dot tsinghua dot edu dot cn) >>> >>> >>> >>> >>> == Sponsors == >>> === Champion === >>> Kevin A. McGrail (kmcgr...@apache.org) >>> >>> >>> === Nominated Mentors === >>> TODO >> --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
