+1 (binding) from my side.
This will be a good addition to all of the pretty new IoT projects at apache.
Chris
Am 07.11.18, 10:02 schrieb "Gosling Von" <fengji...@gmail.com>:
+1
Good luck ~
Von Gosling
> 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)
>
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