+1 (binding) -Taylor
> On Feb 29, 2016, at 12:37 PM, Patrick Hunt <ph...@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 ==== > priv...@mnemonic.incubator.apache.org (moderated subscriptions) > comm...@mnemonic.incubator.apache.org > d...@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-unsubscr...@incubator.apache.org > For additional commands, e-mail: general-h...@incubator.apache.org >
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