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may be discouraged from picking alternative stacks if they have to do lots more work themselves. Netflix, for example, is especially concerned with aspects like fault tolerance, to ensure that the outage of one part of its system cannot take everything down. To handle this, a large amount of work has been
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the duplicated effort, and more about the fact that it is so easy to get this wrong. The risk of a service getting newly implemented fault tolerance wrong is high if it could impact more of the system. Netflix mitigates this by using sidecar services, which communicate locally with a JVM that
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anyway can have some pretty disastrous implications. 7 See Liming Chen and Algirdas Avizienis, “N-Version Programming: A Fault-Tolerance Approach to Reliability of Software Operation,” published in the Twenty-Fifth International Symposium on Fault-Tolerant Computing (1995). Chapter 4. Decomposing the Database As we’ve already explored, there are a host of ways
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hand against AI. Another type of scarce work that will be hard to automate is work that involves rare or high-stakes situations with low fault tolerance. Most AI learns in an iterative way—that is, it repeats a task over and over again, getting it a little more right each time
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, so no one culture controls the memes (though some try in vain). This decentralization makes epic social setbacks of reliably limited duration; the system is “fault-tolerant,” as computer engineers say. While Europe fell into its slough of despond, Byzantium and southern China stayed standing, India had ups and downs, and the
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann · 17 Apr 2017
Streams Keeping Systems in Sync Change Data Capture Event Sourcing State, Streams, and Immutability Processing Streams Uses of Stream Processing Reasoning About Time Stream Joins Fault Tolerance Summary 440 441 446 451 452 454 457 459 464 465 468 472 476 479 12. The Future of Data Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 Data Integration
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indi‐ vidual processes without warning. Many critical bugs are actually due to poor error handling [3]; by deliberately inducing faults, you ensure that the fault-tolerance machinery is continually exercised and tested, which can increase your confidence that faults will be handled correctly when they occur naturally. The Netflix Chaos Monkey
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flexibility and elasticityi over single-machine reliability. Hence there is a move toward systems that can tolerate the loss of entire machines, by using software fault-tolerance techniques in preference or in addition to hardware redundancy. Such systems also have operational advantages: a single-server system requires planned downtime if you
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typically random and uncorrelated), software (bugs are typically sys‐ tematic and hard to deal with), and humans (who inevitably make mistakes from time to time). Fault-tolerance techniques can hide certain types of faults from the end user. Scalability means having strategies for keeping performance good, even when load increases. In order
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and Gone,” at 21st International Conference on Data Engineering (ICDE), April 2005. [2] Walter L. Heimerdinger and Charles B. Weinstock: “A Conceptual Framework for System Fault Tolerance,” Technical Report CMU/SEI-92-TR-033, Software Engi‐ neering Institute, Carnegie Mellon University, October 1992. [3] Ding Yuan, Yu Luo, Xin Zhuang, et
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followed the path of CODASYL. Relational Versus Document Databases Today There are many differences to consider when comparing relational databases to document databases, including their fault-tolerance properties (see Chapter 5) and handling of concurrency (see Chapter 7). In this chapter, we will concentrate only on the differences in the data
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weak forms of “lying”—for example, invalid messages due to hardware issues, software bugs, and misconfiguration. Such protec‐ tion mechanisms are not full-blown Byzantine fault tolerance, as they would not withstand a determined adversary, but they are nevertheless simple and pragmatic steps toward better reliability. For example: • Network packets do sometimes
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a consistent state after a new leader is elected, ensuring that the safety properties are always met. These differences are key to the correctness and fault tolerance of a consensus algorithm. 368 | Chapter 9: Consistency and Consensus Limitations of consensus Consensus algorithms are a huge breakthrough for distributed systems: they bring
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proceeds in fixed rounds: at every iteration, the framework delivers all messages sent in the previous iteration. Actors normally have no such timing guarantee. Fault tolerance The fact that vertices can only communicate by message passing (not by querying each other directly) helps improve the performance of Pregel jobs, since messages
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message brokers write all messages to disk, they are able to achieve throughput of millions of messages per second by partitioning across multiple machines, and fault tolerance by replicating messages [22, 23]. Logs compared to traditional messaging The log-based approach trivially supports fan-out messaging, because several con‐ sumers can
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to the present. The facilities for maintaining state as streams and replaying messages are also the basis for the techniques that enable stream joins and fault tolerance in various stream processing frameworks. We discussed several purposes of stream processing, includ‐ ing searching for event patterns (complex event processing), computing windowed aggregations (
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to create applications and systems that are reliable, scalable, and maintainable. These themes have run through all of the chapters: for example, we discussed many fault-tolerance algo‐ rithms that help improve reliability, partitioning to improve scalability, and mecha‐ nisms for evolution and abstraction that improve maintainability. In this chapter we will
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one-directional, asynchronous message streams rather than synchronous request/response interactions. Besides the advantages listed in “Message-Passing Dataflow” on page 136, such as better fault tolerance, dataflow systems can also achieve better performance. For example, say a customer is purchasing an item that is priced in one currency but paid for
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“Distributed Transactions in Practice” on page 360). When we refuse to use distributed transactions because they are too expensive, we end up having to reimplement fault-tolerance mechanisms in application code. As numerous examples throughout this book have shown, reasoning about concurrency and partial failure is difficult and counterintuitive, and so I
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without requiring coordination, while still giving strong integrity guarantees. Such coordination-avoiding data systems have a lot of appeal: they can achieve better performance and fault tolerance than systems that need to perform synchronous coordination [56]. For example, such a system could operate distributed across multiple datacenters in a multi-leader configuration
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writes, 230 auditability, 528-533 designing for, 531 self-auditing systems, 530 through immutability, 460 tools for auditable data systems, 532 availability, 8 (see also fault tolerance) in CAP theorem, 337 in service level agreements (SLAs), 15 Avro (data format), 122-127 code generation, 127 dynamically generated schemas, 126 object container
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, 361-364 recovery, 363 copy-on-write (B-trees), 82, 242 CORBA (Common Object Request Broker Architecture), 134 correctness, 6 auditability, 528-533 Byzantine fault tolerance, 305, 532 dealing with partial failures, 274 in log-based systems, 521-524 of algorithm within system model, 308 of compensating transactions, 355 of consensus
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, 512 outputs of batch and stream processing, 495 through application code, 505 versus distributed transactions, 492 deterministic operations, 255, 274, 554 accidental nondeterminism, 423 and fault tolerance, 423, 426 and idempotence, 478, 492 computing derived data, 495, 526, 531 in state machine replication, 349, 452, 458 joins, 476 DevOps, 394 differential
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introducing deliberately, 7, 280 network faults, 279-281 asymmetric faults, 300 detecting, 280 tolerance of, in multi-leader replication, 169 software errors, 8 tolerating (see fault tolerance) federated databases, 501 fence (CPU instruction), 338 fencing (preventing split brain), 158, 302-304 generating fencing tokens, 349, 370 properties of fencing tokens, 308 stream
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tags (Thrift and Protocol Buffers), 119-121 file descriptors (Unix), 395 financial data, 460 Firebase (database), 456 Flink (processing framework), 421-423 dataflow APIs, 427 fault tolerance, 422, 477, 479 Gelly API (graph processing), 425 integration of batch and stream processing, 495, 498 machine learning, 428 query optimizer, 427 stream processing, 466
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Helix (cluster manager), 216 heterogeneous distributed transactions, 360, 364 heuristic decisions (in 2PC), 363 Hibernate (object-relational mapper), 30 hierarchical model, 36 high availability (see fault tolerance) high-frequency trading, 290, 299 high-performance computing (HPC), 275 hinted handoff, 183 histograms, 16 Hive (query engine), 419, 427 for data warehouses, 93 HCatalog
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in batch processors, 422 JavaScript in MapReduce querying, 46 setting element styles (example), 45 use in advanced queries, 48 Jena (RDF framework), 57 Jepsen (fault tolerance testing), 515 jitter (network delay), 284 joins, 555 by index lookup, 403 expressing as relational operators, 427 in relational and document databases, 34 MapReduce map
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Juttle (query language), 504 K k-nearest neighbors, 429 Kafka (messaging), 137, 448 Kafka Connect (database integration), 457, 461 Kafka Streams (stream processor), 466, 467 fault tolerance, 479 leader-based replication, 153 log compaction, 456, 467 message offsets, 447, 478 request routing, 216 transaction support, 477 usage example, 4 Ketama (partitioning library
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also evolvability) from log-based messaging, 451 request routing, 214-216 582 | Index approaches to, 214 parallel query execution, 216 resilient systems, 6 (see also fault tolerance) response time as performance metric for services, 13, 389 guarantees on, 298 latency versus, 14 mean and percentiles, 14 user experience, 15 responsibility and accountability
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(Datalog), 61 S safety and liveness properties, 308 in consensus algorithms, 366 in transactions, 222 sagas (see compensating transactions) Samza (stream processor), 466, 467 fault tolerance, 479 streaming SQL support, 466 sandboxes, 9 SAP HANA (database), 93 scalability, 10-18, 489 approaches for coping with load, 17 defined, 22 describing load
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record) Spanner (database) data locality, 41 snapshot isolation using clocks, 295 TrueTime API, 294 Spark (processing framework), 421-423 bytecode generation, 428 dataflow APIs, 427 fault tolerance, 422 for data warehouses, 93 GraphX API (graph processing), 425 machine learning, 428 query optimizer, 427 Spark Streaming, 466 microbatching, 477 stream processing on top
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job, 474, 477, 478, 517 combining with batch processing lambda architecture, 497 unifying technologies, 498 comparison to batch processing, 464 complex event processing (CEP), 465 fault tolerance, 476-479 atomic commit, 477 idempotence, 478 microbatching and checkpointing, 477 rebuilding state after a failure, 478 for data integration, 494-498 586 | Index maintaining
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sessions, 229 telemetry (see monitoring) Teradata (database), 93, 200 term-partitioned indexes, 208, 217 termination (consensus), 365 Terrapin (database), 413 Tez (dataflow engine), 421-423 fault tolerance, 422 support by higher-level tools, 427 thrashing (out of memory), 297 threads (concurrency) actor model, 138, 468 (see also message-passing) atomic operations, 223
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