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From Sheng Wu <>
Subject Suggest to redirect IoTDB to right thread
Date Wed, 07 Nov 2018 08:15:03 GMT
Hi all

I think the vote with right format is here,

Let's move the vote to that thread.

Sheng Wu
Apache SkyWalking

On 2018/11/07 06:49:00, "Xiangdong Huang" <> wrote: 
> Hi, 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:
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 ( . 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: 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 (, while its subproject TsFile (a file format
for storing time series data) is open sourced on Github (
== 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:
 * druid:
 * YCSB: 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:
 * Adaptor for Apache Hadoop MapReduce:
 * Adaptor for Apache Spark:  * Adaptor for
Grafana:  * The database engine:
(private project up to now)  * The client driver: ===
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:
 * Documentation for IoTDB and its JDBC: (Chinese only. An English
version is in progress.) == Required Resources == === Mailing Lists ===  *
 *  * === Git Repositories
===  * === 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 (
=== 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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