This document demonstrates how the oplog in MongoDB replica sets works by replicating operations from the primary to secondaries. It shows insert, update, remove operations on a collection on the primary and then verifies that the same operations have been replicated to the oplog and applied on a secondary by finding the same document changes. It also shows getMore commands from secondaries tailing the oplog on the primary to stay up to date as new operations are applied.
Under the Hood of a Shard-per-Core Database ArchitectureScyllaDB
Most databases are based on architectures that pre-date advances to modern hardware. This results in performance issues, the need to overprovision, and a high total cost of ownership. In this webinar we will discuss the advances to modern server technology and take a deep dive into Scylla’s shard-per-core architecture and our asynchronous engine, the Seastar framework.
Join us to learn how Seastar (and Scylla):
Avoid locks and contention on the CPU level
Bypass kernel bottlenecks
Implement its per-core shared-nothing autosharding mechanism
Utilize modern storage hardware
Leverage NUMA to get the best RAM performance
Balance your data across CPUs and nodes for best and smoothest performance
Plus we’ll cover the advantages of unlocking vertical scalability.
RocksDB is an embedded key-value store written in C++ and optimized for fast storage environments like flash or RAM. It uses a log-structured merge tree to store data by writing new data sequentially to an in-memory log and memtable, periodically flushing the memtable to disk in sorted SSTables. It reads from the memtable and SSTables, and performs background compaction to merge SSTables and remove overwritten data. RocksDB supports two compaction styles - level style, which stores SSTables in multiple levels sorted by age, and universal style, which stores all SSTables in level 0 sorted by time.
This document demonstrates how the oplog in MongoDB replica sets works by replicating operations from the primary to secondaries. It shows insert, update, remove operations on a collection on the primary and then verifies that the same operations have been replicated to the oplog and applied on a secondary by finding the same document changes. It also shows getMore commands from secondaries tailing the oplog on the primary to stay up to date as new operations are applied.
Under the Hood of a Shard-per-Core Database ArchitectureScyllaDB
Most databases are based on architectures that pre-date advances to modern hardware. This results in performance issues, the need to overprovision, and a high total cost of ownership. In this webinar we will discuss the advances to modern server technology and take a deep dive into Scylla’s shard-per-core architecture and our asynchronous engine, the Seastar framework.
Join us to learn how Seastar (and Scylla):
Avoid locks and contention on the CPU level
Bypass kernel bottlenecks
Implement its per-core shared-nothing autosharding mechanism
Utilize modern storage hardware
Leverage NUMA to get the best RAM performance
Balance your data across CPUs and nodes for best and smoothest performance
Plus we’ll cover the advantages of unlocking vertical scalability.
RocksDB is an embedded key-value store written in C++ and optimized for fast storage environments like flash or RAM. It uses a log-structured merge tree to store data by writing new data sequentially to an in-memory log and memtable, periodically flushing the memtable to disk in sorted SSTables. It reads from the memtable and SSTables, and performs background compaction to merge SSTables and remove overwritten data. RocksDB supports two compaction styles - level style, which stores SSTables in multiple levels sorted by age, and universal style, which stores all SSTables in level 0 sorted by time.
The document appears to be a collection of symbols, numbers, and punctuation with no discernible meaning. It does not contain any identifiable words, sentences, or concepts that could be summarized in 3 sentences or less.
MongoDB is a document-oriented database that stores data in flexible, JSON-like documents. It supports features like replication, auto-sharding, and indexing. The document discusses using MongoDB with Ameba Pico's photo tagging service, including initial implementation with one shard, expanding to multiple shards as user numbers grow over time, and repairing and upgrading shards over time to support the increasing load.
The document discusses life hacks and web development. It mentions DevLOVE, Google, LifeHacks, matsukaz, TRICHORD, GAE/J, Slim3, JSONIC, iCal4j, and developing apps for iPhone, Android, Twitter, Google, and Facebook.