In the last year, we've gone from millions of pieces of data to billions of pieces of data. I will speak on a solution for scaling up and about the challenges presented. Also covered will be the future of data at Qihoo 360 with MongoDB.
1. What does millions visits means?
2. How to know the problem? Tools?
3. Steps for emergency repair
4. The real problem is “Socket”
5. Profiling with XHProf
6. Cachings and twemproxy
7. Summarize of skills
HBase is a NoSQL database modeled after Google's Bigtable. It provides a distributed, scalable, big data store. Key features include horizontal scaling, reliability, column orientation, high performance random reads/writes, and seamless integration with Hadoop. Data is stored in tables containing rows, column families, and columns. The system is comprised of clients, Zookeeper, masters, region servers, and HDFS for data storage. Region servers handle read/write operations on regions, while masters manage region assignment and load balancing.
In the last year, we've gone from millions of pieces of data to billions of pieces of data. I will speak on a solution for scaling up and about the challenges presented. Also covered will be the future of data at Qihoo 360 with MongoDB.
1. What does millions visits means?
2. How to know the problem? Tools?
3. Steps for emergency repair
4. The real problem is “Socket”
5. Profiling with XHProf
6. Cachings and twemproxy
7. Summarize of skills
HBase is a NoSQL database modeled after Google's Bigtable. It provides a distributed, scalable, big data store. Key features include horizontal scaling, reliability, column orientation, high performance random reads/writes, and seamless integration with Hadoop. Data is stored in tables containing rows, column families, and columns. The system is comprised of clients, Zookeeper, masters, region servers, and HDFS for data storage. Region servers handle read/write operations on regions, while masters manage region assignment and load balancing.
The document summarizes the author's experience at the JavaOne conference. Some of the key topics discussed included trends in Java SE and Java EE, details about sessions on the JVM including garbage collection and memory management, and tips shared in experience talks about optimizing applications and handling production issues. The summary notes that the session content and speakers were important factors, many speakers were from India, and exchanging with speakers required preparation.
share the common java memory problem cases solutions,including:
1. java.lang.OutOfMemoryError
2. full gc frequently
3. cms gc error: promotion failed or concurrent mode failure
How do we manage more than one thousand of Pegasus clusters - backend partacelyc1112009
A presentation in Apache Pegasus meetup in 2021 from Wang Dan.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
Pegasus: Designing a Distributed Key Value System (Arch summit beijing-2016)涛 吴
This slide delivered by Zuoyan Qin, Chief engineer from XiaoMi Cloud Storage Team, was for talk at Arch summit Beijing-2016 regarding how Pegasus was designed.