The type of customers that would most benefit from this Database solution are: TiDB focuses on scalability, database clustering, and its ability to automatically scale horizontally (across nodes/instances/ product, but is meant to serve a specific market. It is important to note that merely based on its scalability and its ability to process at speed TiDB is not particularly novel, other options in the market TiDB is trying to be is speed, and the lack of delay in queries. TiDB’s ability to incorporate horizontal scaling, with vertical scaling is market defining. Competitors: TiDB feature similar optimizations

systems and inconsistent formats create silos, limiting accessibility and reducing effectiveness. Scalability & Real-Time Processing – Handling large-scale data efficiently is crucial for real-time analytics unify access across divers data types, reducing complexity in querying distributed datasets. Horizontal scalability across hybrid environments, supporting cloud, on- prem, and edge deployments. Always-on architecture Data Layer to process, analyze, and act on high-velocity data at scale. Designed for speed, scalability, and flexibility, CrateDB seamlessly integrates real-time analytics, full-text and vector search

volume space mapping • high performance framework • Use bthread (M bthread map N pthread) for scalability and performance on Multi-thread CPU • Lock free queue design • Memory zero copy design • (Cluster and Pool CRDs) in Kubernetes (in Plan) • Support Operator capability level 5 (in Plan) • horizontal / vertical scaling, auto config tuning, abnormal detection and schedule tuningStorage Engine

format ● An HTAP database needs to store huge amount of data ● Scalability is very important ● TiDB relies on multi-raft for scalability ○ One command to add / remove node ○ Scaling is fully automatic automatic ○ Smooth and painless data rebalance ● TiFlash adopts the same design Scalability ● Perfect Resource Isolation ● Data rebalance based on the “label” mechanism ○ Dedicated nodes for TiFlash /

About memory_order_relaxed? ● Just a load, just a store: Full control, excellent efficiency and scalability! ○ Assuming aligned machine-sized atomic objects, that is…What is Not to Like About memory_order_relaxed memory_order_relaxed? ● Just a load, just a store: Full control, excellent efficiency and scalability! ○ Assuming aligned machine-sized atomic objects, that is… ● Small problems: Out of thin air (OOTA) & read About memory_order_relaxed? ● Just a load, just a store: Full control, excellent efficiency and scalability! ○ Assuming aligned machine-sized atomic objects, that is… ● Small problems: Out of thin air

locks and therefore parallel task execution with near zero contention between threads ● Excellent Scalability: ○ Over 40x higher throughput than the fastest MPMC queue at scale ○ Over 100x higher throughput locks and therefore parallel task execution with near zero contention between threads ● Excellent Scalability: ○ Over 40x higher throughput than the fastest MPMC queue at scale ○ Over 100x higher throughput locks and therefore parallel task execution with near zero contention between threads ● Excellent Scalability: ○ Over 40x higher throughput than the fastest MPMC queue at scale ○ Over 100x higher throughput

All rvalue (reference)s go through the second overload, others through the first • Key problem: scalability • Two definitions • Scales poorly to multiple arguments (T mentioned in the returned type) constexpr a move happens if needed •The additional T() is either a move ctor call OR a no-op, as needed •Scalability problem remains • T still mentioned in the returned type • We can’t return arbitrary expressions

(PETRA) Design Goals ▶ High Performance ▶ Low overheads added to achieve durability ▶ High Scalability ▶ Performance scaling well with increasing number of processes ▶ Non-Blocking ▶ There is guaranteed Demonstration References Persistent Transactional Data System for Linked Data Structures (PETRA) High Scalability ▶ Transactional synchronization for conflicts on nodes ▶ Logical rollback when a semantic conflict

Robust! Useful and Robust! Tricky! • Correlations imply dependencies (with implications for scalability and side-effects) • Assumptions may be invalid for your scenarioCharley Bay - charleyb123 at gmail Robust! Useful and Robust! Tricky! • Correlations imply dependencies (with implications for scalability and side-effects) • Assumptions may be invalid for your scenario Knowing “something” can be more

property set. In particular, this is the case when printing arrays with multiple columns (where horizontal space is limited): julia> show(IOContext(stdout, :compact=>true), Polar(3, 4.0)) 3.04.0im julia> higher precedence than semicolons, performing any horizontal concatenations first and then concatenating the result. Using double semicolons for the horizontal concatenation, on the other hand, performs any 3}: [:, :, 1] = 1 3 5 2 4 6 [:, :, 2] = 7 9 11 8 10 12 Like before, spaces (and tabs) for horizontal concatenation have a higher precedence than any number of semicolons. Thus, higher-dimensional