> - When you say "open source" repo, do you mean private repo vs public > repo?
Yes. > > - I believe Craig as Secretary will say an SGA never hurts but isn't > everything already licensed ASLv2? It's been a few weeks and a few > proposals reviewed so it could be my memory. Currently, the licenses of the dependency libs of IoTDB includes: Apache2.0, BSD (antlr3), EPL1.0 (logback) and EPL2.0 (junit). We are working on checking all the licenses once again for avoiding mistakes. Regards, Xiangdong Huang > 在 2018年11月15日，下午10:43，Kevin A. McGrail <kmcgr...@apache.org> 写道： > > Well, first, let's ask some questions: > > - When you say "open source" repo, do you mean private repo vs public > repo? > > - I believe Craig as Secretary will say an SGA never hurts but isn't > everything already licensed ASLv2? It's been a few weeks and a few > proposals reviewed so it could be my memory. > > 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 Thu, Nov 15, 2018 at 7:27 AM hxd <hxd...@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.ji...@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 <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 >>> >> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org >> For additional commands, e-mail: general-h...@incubator.apache.org >> >> --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
