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From "Debo Dutta (dedutta)" <>
Subject Re: [DISCUSS] Mnemonic incubator proposal
Date Sun, 21 Feb 2016 23:26:07 GMT
Hi Yanping

This is very interesting and timely. Would love to contribute, participate


On 2/21/16, 11:47 AM, "Wang, Yanping" <> wrote:

>Hi all 
>We'd like to start a discussion regarding a proposal to submit Mnemonic
>to the Apache Incubator.
>The proposal text is available on the Wiki here:
>and pasted below for convenience.
>We are excited to make this proposal, and look forward to the community's
>= 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
>=== 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
>* 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
>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
>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
>==== Relationships with Other Apache Product ====
>Mnemonic can be integrated into various Big Data and Cloud frameworks and
>We are currently working on several Apache projects with Mnemonic:
>For Apache Spark™ we integrated 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
>=== Initial Source ===
>Initial source code is temporary hosted Github for general viewing:
>It will be moved to Apache 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
>=== 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 ( ) [Apache 2.0]
>JDK8 or OpenJDK 8 ( [Oracle or Openjdk JDK License]
>Nvml ( ) [optional] [Open Source]
>PMalloc ( ) [optional] [Apache
>Build and test dependencies:
>org.testng.testng v6.8.17  ( [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 ( [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
>=== Required Resources ===
>We request that following resources be created for the project to use
>==== Mailing lists ====
> (moderated subscriptions)
>==== Git repository ====
>==== Documentation ====
>==== JIRA instance ====
>=== 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)
>=== 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

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