Transactional Memory - Maurice Herlihy

This talk encompasses an introduction to transactional memory along with some applications, including some unusual applications. This talk is intended for a general audience.

Maurice Herlihy has an A.B. in Mathematics from Harvard University, and a Ph.D. in Computer Science from M.I.T. He has served on the faculty of Carnegie Mellon University and the staff of DEC Cambridge Research Lab. He is the recipient of the 2003 Dijkstra Prize in Distributed Computing, the 2004 Gödel Prize in theoretical computer science, the 2008 ISCA influential paper award, the 2012 Edsger W. Dijkstra Prize, and the 2013 Wallace McDowell award. He received a 2012 Fulbright Distinguished Chair in the Natural Sciences and Engineering Lecturing Fellowship, and he is fellow of the ACM, a fellow of the National Academy of Inventors, the National Academy of Engineering, and the National Academy of Arts and Sciences.

#developer #programming

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Top 10 Security Practices to Encrypt Your Data During Online Transaction

People have to follow a 360 degrees approach to ensure that a data breach doesn’t take place

The Internet has blurred the line between reality and imagination. More futuristic technologies are emerging in the modern era of artificial intelligence (AI). The advanced thought of buying products online through immediate payments methods is a reality now. However, online payments security is becoming a serious issue. Online purchase of goods, solutions and services has revolutionised eCommerce, opening countless opportunities to entrepreneurs worldwide. Unfortunately, keeping the passwords, financial and other personal information safe is increasingly becoming critical. Online frauds are on a historic rise lately. According to a shocking statistics from Juniper Research in 2016, up to 70% of all types of credit card frauds are mainly the card not present (CNP) transactions. The number of online transactions in 2020 is roughly double or even triple of that in 2016. In such a case, you must protect your data during all aspects of business operation including online transactions. In order to secure data, people have to follow a 360 degrees approach to ensure that a security breach doesn’t take place internally or externally. Analytics Insight has compiled top 10 practices that keep hackers away while doing online transactions.

#big data #cybersecurity #latest news #top 10 security practices to encrypt your data during online transaction #during online transactions #data during online transaction

Transactional Memory - Maurice Herlihy

This talk encompasses an introduction to transactional memory along with some applications, including some unusual applications. This talk is intended for a general audience.

Maurice Herlihy has an A.B. in Mathematics from Harvard University, and a Ph.D. in Computer Science from M.I.T. He has served on the faculty of Carnegie Mellon University and the staff of DEC Cambridge Research Lab. He is the recipient of the 2003 Dijkstra Prize in Distributed Computing, the 2004 Gödel Prize in theoretical computer science, the 2008 ISCA influential paper award, the 2012 Edsger W. Dijkstra Prize, and the 2013 Wallace McDowell award. He received a 2012 Fulbright Distinguished Chair in the Natural Sciences and Engineering Lecturing Fellowship, and he is fellow of the ACM, a fellow of the National Academy of Inventors, the National Academy of Engineering, and the National Academy of Arts and Sciences.

#developer #programming

Implementing A Distributed Multi-document ACID Transactions in Couchbase

The Best NoSQL for Transactions

Couchbase already has had support for atomic single document operations and [Optimistic and Pessimistic Locking] for a long time. Last year, we introduced fast transactional support regardless of buckets, collections, scopes, or shards. With Couchbase 7.0 you can even use the same traditional relational transaction syntax. The combination of all these features makes Couchbase the best NoSQL for transactions at scale.

• Low Cost - Pay for what you use
• No Central Transaction Manager or Central Coordinator
• No Internal Global Clock and no MVCC
• Flexibility**

#database #transactions #couchbase transactions #distributed multi-document acid

Is Something Better Than Pandas When The Dataset Fits The Memory?

Explore Vaex, Dask, PySpark, Modin and Julia

The tabular format is still the most typical way how to store data and there’s arguably no better tool how to explore data-tables than python’s pandas. Despite its broad range of capabilities, pandas has limits. In those cases, you have to opt for an alternative. Though, does it make sense to abandon pandas and use another tool, if the dataset fits comfortably into your memory?

Pandas is a convenient tabular data processor offering a variety of methods for loading, processing, and exporting datasets to many output formats. Pandas can handle a sizeable amount of data, but it’s limited by the memory of your PC. There was a golden rule of data science. If the data fits into the memory, use pandas. Is this rule still valid?

Use pandas when data fits your PC’s memory

Pandas alternatives were only recommended in these cases:

• processing in pandas is slow
• data doesn’t fit available memory

Let’s explore a few of these alternatives on a medium-size dataset to see if we can get any benefit or to confirm that you simply use pandas and sleep without doubts.

You can review complete code on GitHub

• pandas_alternatives_POC.ipynb — exploring dask, spark, vaex and modin
• julia_POC.ipynb — exploring julia and julia performance test
• Performance_test.py — run python perf. tests in console
• Results_and_Charts.ipynb — processing performance tests logs and creating the charts

#python #julia #performance #dataframes #is something better than pandas when the dataset fits the memory? #memory

Memory Management in Swift

Our device📱basically has two types of memories where it saves our data:

• Disk memory
• Random Access Memory (RAM)

When we open an application, a small part of RAM is allocated for that application where all the instances for its classes are stored. This chunk of memory is called Heap memory and this is the part of memory we refer to when we talk about memory management.

Heap_ is the portion of memory (RAM) where dynamically allocated memory resides_

Since the heap memory is very limited, it is very important that we manage it properly 💰. Poor management of memory will considerably slow down the app and sooner or later, cause a crash.

But how does swift manage the heap memory?

Swift uses something called Automatic Reference Counting (ARC) for memory management. Every time a new object of a class is created, ARC allocates some amount of memory to store data associated with that object. Each object will have a reference count property that keeps track of everything that has a **strong **reference to it. Every time a strong reference is created, the reference count of that object gets incremented by one. And whenever a reference goes out of scope, the reference count gets decremented by one.

#arc #swift #memory-leak #memory-management #retain-cycle