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From "Cheng, Hao" <hao.ch...@intel.com>
Subject RE: [VOTE] Accept Mnemonic into the Apache Incubator
Date Fri, 04 Mar 2016 00:40:33 GMT
+1 (binding)

Hao

-----Original Message-----
From: Ted Dunning [mailto:ted.dunning@gmail.com] 
Sent: Friday, March 4, 2016 5:32 AM
To: general@incubator.apache.org
Subject: Re: [VOTE] Accept Mnemonic into the Apache Incubator

I thought I sent a vote in earlier today but I may have hit the wrong thread.

Please accept this vote.

+1 (binding)



On Thu, Mar 3, 2016 at 11:19 AM, P. Taylor Goetz <ptgoetz@gmail.com> wrote:

> +1 (binding)
>
> -Taylor
>
> > On Feb 29, 2016, at 12:37 PM, Patrick Hunt <phunt@apache.org> wrote:
> >
> > Hi folks,
> >
> > OK the discussion is now completed. Please VOTE to accept Mnemonic 
> > into the Apache Incubator. I’ll leave the VOTE open for at least the 
> > next 72 hours, with hopes to close it Thursday the 3rd of March, 
> > 2016 at 10am PT.
> > https://wiki.apache.org/incubator/MnemonicProposal
> >
> > [ ] +1 Accept Mnemonic as an Apache Incubator podling.
> > [ ] +0 Abstain.
> > [ ] -1 Don’t accept Mnemonic as an Apache Incubator podling because..
> >
> > Of course, I am +1 on this. Please note VOTEs from Incubator PMC 
> > members are binding but all are welcome to VOTE!
> >
> > Regards,
> >
> > Patrick
> >
> > --------------------
> > = Mnemonic Proposal =
> > === Abstract ===
> > Mnemonic is a Java based non-volatile memory library for in-place 
> > structured data processing and computing. It is a solution for 
> > generic object and block persistence on heterogeneous block and 
> > byte-addressable devices, such as DRAM, persistent memory, NVMe, 
> > SSD, and cloud network storage.
> >
> > === Proposal ===
> > Mnemonic is a structured data persistence in-memory in-place library 
> > for Java-based applications and frameworks. It provides unified 
> > interfaces for data manipulation on heterogeneous 
> > block/byte-addressable devices, such as DRAM, persistent memory, 
> > NVMe, SSD, and cloud network devices.
> >
> > The design motivation for this project is to create a non-volatile 
> > programming paradigm for in-memory data object persistence, 
> > in-memory data objects caching, and JNI-less IPC.
> > Mnemonic simplifies the usage of data object caching, persistence, 
> > and JNI-less IPC for massive object oriented structural datasets.
> >
> > Mnemonic defines Non-Volatile Java objects that store data fields in 
> > persistent memory and storage. During the program runtime, only 
> > methods and volatile fields are instantiated in Java heap, 
> > Non-Volatile data fields are directly accessed via GET/SET operation 
> > to and from persistent memory and storage. Mnemonic avoids SerDes 
> > and significantly reduces amount of garbage in Java heap.
> >
> > Major features of Mnemonic:
> > * Provides an abstract level of viewpoint to utilize heterogeneous 
> > block/byte-addressable device as a whole (e.g., DRAM, persistent 
> > memory, NVMe, SSD, HD, cloud network Storage).
> >
> > * Provides seamless support object oriented design and programming 
> > without adding burden to transfer object data to different form.
> >
> > * Avoids the object data serialization/de-serialization for data 
> > retrieval, caching and storage.
> >
> > * Reduces the consumption of on-heap memory and in turn to reduce 
> > and stabilize Java Garbage Collection (GC) pauses for latency 
> > sensitive applications.
> >
> > * Overcomes current limitations of Java GC to manage much larger 
> > memory resources for massive dataset processing and computing.
> >
> > * Supports the migration data usage model from traditional 
> > NVMe/SSD/HD to non-volatile memory with ease.
> >
> > * Uses lazy loading mechanism to avoid unnecessary memory 
> > consumption if some data does not need to use for computing immediately.
> >
> > * Bypasses JNI call for the interaction between Java runtime 
> > application and its native code.
> >
> > * Provides an allocation aware auto-reclaim mechanism to prevent 
> > external memory resource leaking.
> >
> >
> > === Background ===
> > Big Data and Cloud applications increasingly require both high 
> > throughput and low latency processing. Java-based applications 
> > targeting the Big Data and Cloud space should be tuned for better 
> > throughput, lower latency, and more predictable response time.
> > Typically, there are some issues that impact BigData applications'
> > performance and scalability:
> >
> > 1) The Complexity of Data Transformation/Organization: In most 
> > cases, during data processing, applications use their own 
> > complicated data caching mechanism for SerDes data objects, spilling 
> > to different storage and eviction large amount of data. Some data 
> > objects contains complex values and structure that will make it much 
> > more difficulty for data organization. To load and then parse/decode 
> > its datasets from storage consumes high system resource and computation power.
> >
> > 2) Lack of Caching, Burst Temporary Object Creation/Destruction 
> > Causes Frequent Long GC Pauses: Big Data computing/syntax generates 
> > large amount of temporary objects during processing, e.g. lambda, 
> > SerDes, copying and etc. This will trigger frequent long Java GC 
> > pause to scan references, to update references lists, and to copy 
> > live objects from one memory location to another blindly.
> >
> > 3) The Unpredictable GC Pause: For latency sensitive applications, 
> > such as database, search engine, web query, real-time/streaming 
> > computing, require latency/request-response under control. But 
> > current Java GC does not provide predictable GC activities with 
> > large on-heap memory management.
> >
> > 4) High JNI Invocation Cost: JNI calls are expensive, but high 
> > performance applications usually try to leverage native code to 
> > improve performance, however, JNI calls need to convert Java objects 
> > into something that C/C++ can understand. In addition, some 
> > comprehensive native code needs to communicate with Java based 
> > application that will cause frequently JNI call along with stack 
> > marshalling.
> >
> > Mnemonic project provides a solution to address above issues and 
> > performance bottlenecks for structured data processing and computing.
> > It also simplifies the massive data handling with much reduced GC 
> > activity.
> >
> > === Rationale ===
> > There are strong needs for a cohesive, easy-to-use non-volatile 
> > programing model for unified heterogeneous memory resources 
> > management and allocation. Mnemonic project provides a reusable and 
> > flexible framework to accommodate other special type of memory/block 
> > devices for better performance without changing client code.
> >
> > Most of the BigData frameworks (e.g., Apache Spark™, Apache™ 
> > Hadoop®, Apache HBase™, Apache Flink™, Apache Kafka™, etc.) have 
> > their own complicated memory management modules for caching and 
> > checkpoint. Many approaches increase the complexity and are 
> > error-prone to maintain code.
> >
> > We have observed heavy overheads during the operations of data 
> > parse, SerDes, pack/unpack, code/decode for data loading, storage, 
> > checkpoint, caching, marshal and transferring. Mnemonic provides a 
> > generic in-memory persistence object model to address those 
> > overheads for better performance. In addition, it manages its 
> > in-memory persistence objects and blocks in the way that GC does, 
> > which means their underlying memory resource is able to be reclaimed 
> > without explicitly releasing it.
> >
> > Some existing Big Data applications suffer from poor Java GC 
> > behaviors when they process their massive unstructured datasets.  
> > Those behaviors either cause very long stop-the-world GC pauses or 
> > take significant system resources during computing which impact 
> > throughput and incur significant perceivable pauses for interactive analytics.
> >
> > There are more and more computing intensive Big Data applications 
> > moving down to rely on JNI to offload their computing tasks to 
> > native code which dramatically increases the cost of JNI invocation and IPC.
> > Mnemonic provides a mechanism to communicate with native code 
> > directly through in-place object data update to avoid complex object 
> > data type conversion and stack marshaling. In addition, this project 
> > can be extended to support various lockers for threads between Java 
> > code and native code.
> >
> > === Initial Goals ===
> > Our initial goal is to bring Mnemonic into the ASF and transit the 
> > engineering and governance processes to the "Apache Way."  We would 
> > like to enrich a collaborative development model that closely aligns 
> > with current and future industry memory and storage technologies.
> >
> > Another important goal is to encourage efforts to integrate 
> > non-volatile programming model into data centric 
> > processing/analytics frameworks/applications, (e.g., Apache Spark™, 
> > Apache HBase™, Apache Flink™, Apache™ Hadoop®, Apache Cassandra™,  etc.).
> >
> > We expect Mnemonic project to be continuously developing new 
> > functionalities in an open, community-driven way. We envision 
> > accelerating innovation under ASF governance in order to meet the 
> > requirements of a wide variety of use cases for in-memory 
> > non-volatile and volatile data caching programming.
> >
> > === Current Status ===
> > Mnemonic project is available at Intel’s internal repository and 
> > managed by its designers and developers. It is also temporary hosted 
> > at Github for general view 
> > https://github.com/NonVolatileComputing/Mnemonic.git
> >
> > We have integrated this project for Apache Spark™ 1.5.0 and get 2X 
> > performance improvement ratio for Spark™ MLlib k-means workload and 
> > observed expected benefits of removing SerDes, reducing total GC 
> > pause time by 40% from our experiments.
> >
> > ==== Meritocracy ====
> > Mnemonic was originally created by Gang (Gary) Wang and Yanping Wang 
> > in early 2015. The initial committers are the current Mnemonic R&D 
> > team members from US, China, and India Big Data Technologies Group 
> > at Intel. This group will form a base for much broader community to 
> > collaborate on this code base.
> >
> > We intend to radically expand the initial developer and user 
> > community by running the project in accordance with the "Apache 
> > Way." Users and new contributors will be treated with respect and 
> > welcomed. By participating in the community and providing quality 
> > patches/support that move the project forward, they will earn merit. 
> > They also will be encouraged to provide non-code contributions 
> > (documentation, events, community management, etc.) and will gain 
> > merit for doing so. Those with a proven support and quality track 
> > record will be encouraged to become committers.
> >
> > ==== Community ====
> > If Mnemonic is accepted for incubation, the primary initial goal is 
> > to transit the core community towards embracing the Apache Way of 
> > project governance. We would solicit major existing contributors to 
> > become committers on the project from the start.
> >
> > ==== Core Developers ====
> > Mnemonic core developers are all skilled software developers and 
> > system performance engineers at Intel Corp with years of experiences 
> > in their fields. They have contributed many code to Apache projects.
> > There are PMCs and experienced committers have been working with us 
> > from Apache Spark™, Apache HBase™, Apache Phoenix™, Apache™ Hadoop® 
> > for this project's open source efforts.
> >
> > === Alignment ===
> > The initial code base is targeted to data centric processing and 
> > analyzing in general. Mnemonic has been building the connection and 
> > integration for Apache projects and other projects.
> >
> > We believe Mnemonic will be evolved to become a promising project 
> > for real-time processing, in-memory streaming analytics and more, 
> > along with current and future new server platforms with persistent 
> > memory as base storage devices.
> >
> > === Known Risks ===
> > ==== Orphaned products ====
> > Intel’s Big Data Technologies Group is actively working with 
> > community on integrating this project to Big Data frameworks and applications.
> > We are continuously adding new concepts and codes to this project 
> > and support new usage cases and features for Apache Big Data ecosystem.
> >
> > The project contributors are leading contributors of Hadoop-based 
> > technologies and have a long standing in the Hadoop community. As we 
> > are addressing major Big Data processing performance issues, there 
> > is minimal risk of this work becoming non-strategic and unsupported.
> >
> > Our contributors are confident that a larger community will be 
> > formed within the project in a relatively short period of time.
> >
> > ==== Inexperience with Open Source ==== This project has long 
> > standing experienced mentors and interested contributors from Apache 
> > Spark™, Apache HBase™, Apache Phoenix™, Apache™ Hadoop® to help us 
> > moving through open source process. We are actively working with 
> > experienced Apache community PMCs and committers to improve our 
> > project and further testing.
> >
> > ==== Homogeneous Developers ====
> > All initial committers and interested contributors are employed at 
> > Intel. As an infrastructure memory project, there are wide range of 
> > Apache projects are interested in innovative memory project to fit 
> > large sized persistent memory and storage devices. Various Apache 
> > projects such as Apache Spark™, Apache HBase™, Apache Phoenix™, 
> > Apache Flink™, Apache Cassandra™ etc. can take good advantage of 
> > this project to overcome serialization/de-serialization, Java GC, 
> > and caching issues. We expect a wide range of interest will be 
> > generated after we open source this project to Apache.
> >
> > ==== Reliance on Salaried Developers ==== All developers are paid by 
> > their employers to contribute to this project. We welcome all others 
> > to contribute to this project after it is open sourced.
> >
> > ==== Relationships with Other Apache Product ==== Relationship with 
> > Apache™ Arrow:
> > Arrow's columnar data layout allows great use of CPU caches & SIMD. 
> > It places all data that relevant to a column operation in a compact 
> > format in memory.
> >
> > Mnemonic directly puts the whole business object graphs on external 
> > heterogeneous storage media, e.g. off-heap, SSD. It is not necessary 
> > to normalize the structures of object graphs for caching, checkpoint 
> > or storing. It doesn’t require developers to normalize their data 
> > object graphs. Mnemonic applications can avoid indexing & join 
> > datasets compared to traditional approaches.
> >
> > Mnemonic can leverage Arrow to transparently re-layout qualified 
> > data objects or create special containers that is able to 
> > efficiently hold those data records in columnar form as one of major 
> > performance optimization constructs.
> >
> > Mnemonic can be integrated into various Big Data and Cloud 
> > frameworks and applications.
> > We are currently working on several Apache projects with Mnemonic:
> > For Apache Spark™ we are integrating Mnemonic to improve:
> > a) Local checkpoints
> > b) Memory management for caching
> > c) Persistent memory datasets input
> > d) Non-Volatile RDD operations
> > The best use case for Apache Spark™ computing is that the input data 
> > is stored in form of Mnemonic native storage to avoid caching its 
> > row data for iterative processing. Moreover, Spark applications can 
> > leverage Mnemonic to perform data transforming in persistent or 
> > non-persistent memory without SerDes.
> >
> > For Apache™ Hadoop®, we are integrating HDFS Caching with Mnemonic 
> > instead of mmap. This will take advantage of persistent memory 
> > related features. We also plan to evaluate to integrate in Namenode 
> > Editlog, FSImage persistent data into Mnemonic persistent memory area.
> >
> > For Apache HBase™, we are using Mnemonic for BucketCache and 
> > evaluating performance improvements.
> >
> > We expect Mnemonic will be further developed and integrated into 
> > many Apache BigData projects and so on, to enhance memory management 
> > solutions for much improved performance and reliability.
> >
> > ==== An Excessive Fascination with the Apache Brand ==== While we 
> > expect Apache brand helps to attract more contributors, our 
> > interests in starting this project is based on the factors mentioned 
> > in the Rationale section.
> >
> > We would like Mnemonic to become an Apache project to further foster 
> > a healthy community of contributors and consumers in BigData 
> > technology R&D areas. Since Mnemonic can directly benefit many 
> > Apache projects and solves major performance problems, we expect the 
> > Apache Software Foundation to increase interaction with the larger community as
well.
> >
> > === Documentation ===
> > The documentation is currently available at Intel and will be posted
> > under: https://mnemonic.incubator.apache.org/docs
> >
> > === Initial Source ===
> > Initial source code is temporary hosted Github for general viewing:
> > https://github.com/NonVolatileComputing/Mnemonic.git
> > It will be moved to Apache http://git.apache.org/ after podling.
> >
> > The initial Source is written in Java code (88%) and mixed with JNI 
> > C code (11%) and shell script (1%) for underlying native allocation 
> > libraries.
> >
> > === Source and Intellectual Property Submission Plan === As soon as 
> > Mnemonic is approved to join the Incubator, the source code will be 
> > transitioned via the Software Grant Agreement onto ASF 
> > infrastructure and in turn made available under the Apache License, 
> > version 2.0.
> >
> > === External Dependencies ===
> > The required external dependencies are all Apache licenses or other 
> > compatible Licenses
> > Note: The runtime dependent licenses of Mnemonic are all declared as 
> > Apache 2.0, the GNU licensed components are used for Mnemonic build 
> > and deployment. The Mnemonic JNI libraries are built using the GNU 
> > tools.
> >
> > maven and its plugins (http://maven.apache.org/ ) [Apache 2.0]
> > JDK8 or OpenJDK 8 (http://java.com/) [Oracle or Openjdk JDK License] 
> > Nvml (http://pmem.io ) [optional] [Open Source] PMalloc 
> > (https://github.com/bigdata-memory/pmalloc ) [optional] [Apache
> 2.0]
> >
> > Build and test dependencies:
> > org.testng.testng v6.8.17  (http://testng.org) [Apache 2.0] 
> > org.flowcomputing.commons.commons-resgc v0.8.7 [Apache 2.0] 
> > org.flowcomputing.commons.commons-primitives v.0.6.0 [Apache 2.0] 
> > com.squareup.javapoet v1.3.1-SNAPSHOT [Apache 2.0]
> > JDK8 or OpenJDK 8 (http://java.com/) [Oracle or Openjdk JDK License]
> >
> > === Cryptography ===
> > Project Mnemonic does not use cryptography itself, however, Hadoop 
> > projects use standard APIs and tools for SSH and SSL communication 
> > where necessary.
> >
> > === Required Resources ===
> > We request that following resources be created for the project to 
> > use
> >
> > ==== Mailing lists ====
> > private@mnemonic.incubator.apache.org (moderated subscriptions) 
> > commits@mnemonic.incubator.apache.org
> > dev@mnemonic.incubator.apache.org
> >
> > ==== Git repository ====
> > https://github.com/apache/incubator-mnemonic
> >
> > ==== Documentation ====
> > https://mnemonic.incubator.apache.org/docs/
> >
> > ==== JIRA instance ====
> > https://issues.apache.org/jira/browse/mnemonic
> >
> > === Initial Committers ===
> > * Gang (Gary) Wang (gang1 dot wang at intel dot com)
> >
> > * Yanping Wang (yanping dot wang at intel dot com)
> >
> > * Uma Maheswara Rao G (umamahesh at apache dot org)
> >
> > * Kai Zheng (drankye at apache dot org)
> >
> > * Rakesh Radhakrishnan Potty  (rakeshr at apache dot org)
> >
> > * Sean Zhong  (seanzhong at apache dot org)
> >
> > * Henry Saputra  (hsaputra at apache dot org)
> >
> > * Hao Cheng (hao dot cheng at intel dot com)
> >
> > === Additional Interested Contributors ===
> > * Debo Dutta (dedutta at cisco dot com)
> >
> > * Liang Chen (chenliang613 at Huawei dot com)
> >
> > === Affiliations ===
> > * Gang (Gary) Wang, Intel
> >
> > * Yanping Wang, Intel
> >
> > * Uma Maheswara Rao G, Intel
> >
> > * Kai Zheng, Intel
> >
> > * Rakesh Radhakrishnan Potty, Intel
> >
> > * Sean Zhong, Intel
> >
> > * Henry Saputra, Independent
> >
> > * Hao Cheng, Intel
> >
> > === Sponsors ===
> > ==== Champion ====
> > Patrick Hunt
> >
> > ==== Nominated Mentors ====
> > * Patrick Hunt <phunt at apache dot org> - Apache IPMC member
> >
> > * Andrew Purtell <apurtell at apache dot org > - Apache IPMC member
> >
> > * James Taylor <jamestaylor at apache dot org> - Apache IPMC member
> >
> > * Henry Saputra <hsaputra at apache dot org> - Apache IPMC member
> >
> > ==== Sponsoring Entity ====
> > Apache Incubator PMC
> >
> > --------------------------------------------------------------------
> > - To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> > For additional commands, e-mail: general-help@incubator.apache.org
> >
>
>
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