M1k4el4's Projects
Beautiful static documentation for your API
DrawerLayout-like ViewGroup, where a "drawer" is hidden under the content view, which can be shifted to make the drawer visible.
Android Library for making animated tutorials inside your app
A highly modular and portable open source XMPP client library written in Java for Android and Java (SE) VMs
Small strings compression library
A server that accepts SMPP client connections.
SMSLib v3.5.x
Snapcast OS
SoapUI is a free and open source cross-platform functional testing solution for APIs and web services.
WebSocket Transport Plugin for SoapUI
The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here.
A tool for building/building artifacts from source and injecting into docker images
:four_leaf_clover: Lean & mean spacemacs-ish Vim distribution
libGDX game jam entry
A Sinatra inspired framework for java
Final Project for IoT: Big Data Processing and Analytics class in UCSC Extension. Analyzing U.S nationwide temperature from IoT sensors in real-time
Stanford CoreNLP wrapper for Apache Spark
Library for organizing batch processing pipelines in Apache Spark
REST job server for Apache Spark
Reliable spark-kafka connector. Kafka 1.x and Spark 2.x
An implement of Factorization Machines (LibFM)
A library to expose more of Apache Spark's metrics system
Natural Language Understanding Library for Apache Spark.
Public runnable examples of using John Snow Labs' NLP for Apache Spark.
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
In radar, we design a new scheduler based on tasks' size and node capability. Traditional locality-aware scheduler is not suitable for heterogeneous tasks and shared clusters. We propose to add two eyes for scheduler. One is tasks' size which we can get from HDFS. Another is node's capability which we can get by exploiting the recurring characteristics of spark streaming batches. We make scheduling decisions according to the following principles: (1) large task first which we can amortize the large tasks' impact on stage execution time through multi waves, (2) fast nodes large tasks and slow nodes small tasks which we can achieve better load balancing, (3) choose task in corresponding location of node capability order for this node. By doing so, we can avoid 86.57% speculative tasks, lower 20.96% latency and save about 10% resources in Tencent production clusters.
Structured Streaming in Apache Spark 2.2+ GitBook