Real-Time Unified Data Layer1. Introduction Data teams are facing more challenges than ever. As applications generate and consume unprecedented volumes of data across a growing number of sources and formats Equipment Effectiveness (OEE). Energy companies must balance EV charger loads and manage grid performance in real time. Banks need to analyze audit logs from their website and application in real time frauds. Logistics companies need real-time tracking and historical analysis of shipments, fleet performance, and warehouse operations to optimize delivery times, reduce costs, and improve supply chain efficiency

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2 Web framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Python Module Index 257 Index 259 iiiTornado Documentation, Release 6.5.1 Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user. CONTENTS 1Tornado Documentation, Release 6

Tornado [https://www.tornadoweb.org] is a Python web framework and asynchronous networking library, originally developed at FriendFeed [https://en.wikipedia.org/wiki/FriendFeed]. By using non-blocking org/wiki/Push_technology#Long_polling], WebSockets [https://en.wikipedia.org/wiki/WebSocket], and other applications that require a long-lived connection to each user. Quick links Current version: 6.5.1 (download s] Hello, world Here is a simple “Hello, world” example web app for Tornado: import asyncio import tornado class MainHandler(tornado.web.RequestHandler): def get(self): self.write("Hello

or small-medium-large as units for assigning story points. Over time, as the teams accumulate performance data, this iterative and incremental4 process improves accuracy in allocating points. Point team to plan the amount of work to accomplish in the next sprint and continually measure its performance. Teams use burn down charts (Figure 3) to track progress during a sprint. Figure 3: Example mitigation strategy, since early working software products reduce risk by validating requirements and performance characteristics rather than by conducting exhaustive paper analysis. The requirements process

Multi-replicas consistency 3. The client l Provides read and write data interfaces for upper-layer applications l Interacts with MDS to add, delete, modify, and query metadata l Interacts with the chunkServer possible to allocate space frequently at the beginning, but after the allocation is complete, performance recovers. 3. The Client queries the ChunkServer for the leader ChunkServer node of the copyset CurveBS, so metadata scalability is very good in this way. 2. Fs-data cluster is used to store file data. Curve-fuse Supports Object storage by S3 apis and CurveBS CurveBS performance considerations

Curve High performance Cloud native Distributed storage system https://www.opencurve.io/Agenda • What is Curve • Use Cases • CurveBS • Key Features • Comparing to Ceph • CurveFS • Comparing Block Storage (CurveBS) • CurveBS: a high performance cloud native distributed block storage • Curve File System (CurveFS) • CurveFS: a high performance cloud native file systemUse Cases • Container on-prem OSSCurveBS • high performance • mainly used for (SSD, three replicas) • csi / storage class for kubernete, nbd for HOST/VMPerformance (vs. Ceph RBD)Performance (vs. Ceph RBD)CurveBS Features

transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 29.4 External applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 29.5 Parallelization with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 34 Performance Tips 416 34.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 416 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . .

transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 29.4 External applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 29.5 Parallelization with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 34 Performance Tips 416 34.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 416 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . .

transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 30.4 External applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 30.5 Parallelization with backtrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 35 Performance Tips 444 35.1 Performance critical code should be inside a function . . . . . . . . . . . . . . . . . 444 untyped global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . .