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) > >
