ZooKeeper System Evaluation

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Questions and Answers

What is the primary focus of the evaluation discussed?

Latency of requests

Network configuration

Server hardware specifications

Throughput performance (correct)

How many servers were used in the cluster for evaluation?

139

50 (correct)

Which hardware component is specified as present in each server?

8GB of RAM

Xeon dual-core processor (correct)

SSD storage

AMD quad-core processor

What does Table 1 illustrate?

The performance of a saturated system Signup and view all the answers

What does the evaluation primarily benchmark?

Throughput and latency of requests Signup and view all the answers

What is the maximum throughput observed in the 50 server cluster?

460k operations per second Signup and view all the answers

What variation is shown in Figure 5?

Throughput as the ratio of reads to writes varies Signup and view all the answers

Which component is mentioned regarding connectivity for the servers?

Gigabit ethernet Signup and view all the answers

What mechanism does ZooKeeper use to maintain communication with the server?

Heartbeat Signup and view all the answers

If a ZooKeeper client cannot contact a server, what will it attempt to do?

Reconnect to a different ZooKeeper server Signup and view all the answers

After how long of inactivity does the ZooKeeper client send a heartbeat?

s/3 ms Signup and view all the answers

What happens to a ZooKeeper session if no communication is received within a specific timeframe?

The session is timed out Signup and view all the answers

What is the maximum number of total requests that the ZooKeeper system can handle in extreme cases as mentioned?

50000 Signup and view all the answers

How does ZooKeeper handle overwhelming requests to prevent system overload?

By implementing performance throttling Signup and view all the answers

What happens when a ZooKeeper client switches to a new server?

The client attempts to reestablish its previous session Signup and view all the answers

What value represents the session timeout for ZooKeeper in the context provided?

s milliseconds Signup and view all the answers

What is a significant downside of saving the master state in a datastore?

All worker accesses still go through the primary master. Signup and view all the answers

What advantage does using a coordination service provide in a master-worker architecture?

It allows dedicated handling of worker interactions. Signup and view all the answers

What is a challenge in implementing coordination logic among workers without a master?

It can be difficult to implement correctly. Signup and view all the answers

What limitation is associated with detecting worker faults by the primary master?

It limits scalability in resource management. Signup and view all the answers

In a master-worker architecture, what is a key problem that arises when connecting workers and backup masters?

Connections to workers need to be recreated by the backup master. Signup and view all the answers

What does the implementation of a coordination service ensure regarding shared state?

Shared state is stored reliably and monitored. Signup and view all the answers

What is a benefit of using a master in a master-worker setup?

It centralizes control over worker tasks. Signup and view all the answers

Which of the following statements is true concerning the master-worker model with a coordination service?

A coordination service enhances reliability in worker interactions. Signup and view all the answers

What should happen if a new leader crashes before completing configuration updates?

Another leader must clean up the incomplete updates. Signup and view all the answers

What does the regular znode 'ready' indicate in the configuration process?

It must be deleted before changes are made to the configuration. Signup and view all the answers

What is the function of the ephemeral znode created by the leader?

To indicate the leader's process is still active. Signup and view all the answers

What will occur if the leader dies after starting configuration changes?

There will be a risk of using partially updated information. Signup and view all the answers

What should a new leader do immediately when taking charge of the configuration?

Delete the 'ready' znode to begin the update. Signup and view all the answers

Which of the following describes a key aspect of a partially updated configuration?

It must be avoided in favor of a complete configuration. Signup and view all the answers

How does the failure of a leader influence the configuration process?

A new leader must ensure a complete clean-up of the process. Signup and view all the answers

What action should a new leader take with the 'ready' znode after all configuration changes are sent?

Recreate it to signal readiness. Signup and view all the answers

What is the simple version of implementing locks as described?

Create an ephemeral node with a watch attribute. Signup and view all the answers

In concurrent history, what does a read operation return when relaxed conditions are applied?

The last value written by any client at the time of the call Signup and view all the answers

What must a global history respect regarding operations by clients?

The global history respects the ordering of operations by each client Signup and view all the answers

What happens when a client successfully acquires the lock?

The client becomes responsible for deleting the lock. Signup and view all the answers

What factor affects the interleaving of operations in concurrent histories?

Delays in processing due to distances between processes Signup and view all the answers

What is a major problem associated with the implementation of locks as described?

The system can experience a herd effect upon lock release. Signup and view all the answers

What ultimately determines whether a client can hold the lock after attempting to create it?

The existence of the znode (the lock node). Signup and view all the answers

What does the presence of WAN links contribute to in the context of cloud computing?

Creating delays between sending and receiving operation results Signup and view all the answers

What is the purpose of the watch attribute in lock implementation?

To notify clients when the lock is released. Signup and view all the answers

Which statement best describes the relationship between clients in a concurrent processing environment?

Clients operate independently and can write values at different times Signup and view all the answers

Which of the following best describes the 'herd effect' mentioned with respect to lock implementation?

The result of too many clients attempting to access the lock simultaneously. Signup and view all the answers

What happens during a read operation if it is called by the first client after a write operation by the second client?

The read operation may return the value written by the second client if it is the last value at that moment Signup and view all the answers

What primary characteristic of operations in concurrent history is noted?

They take time to be processed, leading to interleaving Signup and view all the answers

What happens to clients when they try to create the lock after it has been established?

All clients fail since the lock already exists. Signup and view all the answers

Which of the following is NOT a characteristic of the lock implementation described?

Multiple clients can hold the lock at the same time. Signup and view all the answers

How can the same local operations from two clients yield different valid global histories?

Through varying speeds of process execution and scheduling Signup and view all the answers

Study Notes

Cloud Computing - Lesson 7: Fault Tolerance and Consistency

Announcements:
- Quiz #2: Covers lectures 3-4. Answers available after this lecture.
- Quiz #3: Covers lectures 5-6. Available today. Deadline November 6, 10:45 AM (+1 week smart).
- Guest industry presentation: December 11 (lecture time) by Mike Martin, Senior Cloud Solution Architect at Microsoft.

Lecture Objectives

Discuss the need for geo-replication in cloud data stores.
Present consistency models, coherence protocols, and associated tradeoffs.
Examine how coordination problems are solved using a specific storage service.

Part 1: Replication and Consistency for Cloud Data Storage

Focuses on replication and consistency for cloud data storage systems.

Need for Fault Tolerance

All components in data centers can fail (servers, hardware, software, disks, humans).
Backblaze studies show SSDs offer slightly improved reliability but still have failure rates.
Unplanned server shutdowns and power outages are common issues.
Data center failures can be a concern due to broader outages of the power supply which UPS units may not be able to handle.

Replication

Storing multiple copies of the same data within the same data center provides fault tolerance. This ensures data survival if a server or component fails.

Georeplication

Replicating data across multiple data centers (geographically distributed) supports resilience against full data center failures.
It allows serving local users with local copies of the data.

Maintaining Coherent Copies

A coherence protocol ensures consistent updates across all data copies.
Significant latency can exist between data centers. This issue is critical to maintaining data consistency during updates.

Consistency

Multiple data copies with concurrent reads and writes demand coherent update protocols.
Consistency models define acceptable interactions among concurrent operations.
Strong consistency models mandate higher coordination costs, more traffic, and greater latency costs.
A consistency model determines how multiple clients view modifications to data items.

Setup

Abstract stored objects as registers (for simplicity).
Processes—agents interacting with storage objects.
Data abstractions include BLOBs, JSON documents, and general data structures (like queues, sets, and lists).

Single-process History

Implicit rules in a single-process system; the reader returns the latest value written by that same process.
Sequential execution order is critical to ensure integrity.

Concurrent History (with instantaneous operations)

Reads don't necessarily return the last value written by the same client (different from single-process)
Operations can be performed and reordered across different processes.
Operations might not be instantaneous (due to remote locations or network delays) and can run concurrently.

Concurrent Global History (with instantaneous Operations)

The ordering of client operations is not always strictly relevant.

But Operations Are Not Instantaneous in Practice

Processes are geographically separated.
WAN connections introducing delays.
Operations require time to be processed.

A Non-coherent History (According to Our Previous Consistency Criteria)

Second client might read an older value than was present when the read request began.
Reading returns a data value from a previous moment in time.

The Symmetry

A relaxed consistency requirement allowing operations to be reordered.
Operations have a time interval between invocation and return and may not be instantaneous.

Linearizability

Operations appear to happen atomically between invocation and return.
A strong consistency model is implied to easily track events.
Some constraints include avoiding stale reads and non-monotonic reads.

Sequential Consistency

Operations appear to occur atomically.
Reads return the most recent values written by any process before the read.
But there is no guarantee about the total sequence order among clients.

Sequentially Consistent History

Writes occur in the relative sequence order defined by each process, from each client's perspective.

Composability

Combining operations on separate objects may not yield a consistent state, where operations on separate objects are not necessarily sequentially consistent when combined.

Implementing Coherence

Maintaining coherence across multiple copies requires coordination mechanisms.
The added constraints increase protocol complexity and cost with stricter order stipulations.

Options for Implementing Coherence

Synchronous and asynchronous replication options to update replicas immediately or in background, and the various tradeoffs in performance.

Linearizability: Synchronous, Write-All

Write operations are applied across all replicas synchronously.
Reads are from local replicas providing speed.
This model guarantees consistency but can block if a replica is unavailable.

Linearizability: Synchronous, Write-Quorum

Write operations only require a majority of replicas to respond.
This provides non-blocking writes, assuming the majority of the replicas are available to maintain high availability.
Reads read from the majority to give the most recent value written.

How Can We Get Timestamps?

Timestamps aid in ordering writes accurately across a distributed network of geographically distant servers, where no single system can determine the exact time across the network.
Option 1: Collect timestamps from majority of replicas. Requires a round trip.
Option 2: Using technologies, such as Google TrueTime API, which exposes bounds on clock drift from a centralized system— GPS signals, can dramatically improve the accuracy on the timestamp data.

Asynchronous Replication

Operations are handled by local replicas.
Updates to other replicas happen asynchronously.
Availability is increased but can cause potential conflicts when updating replicas simultaneously through different processes.

Sequential Consistency: Asynchronous Primary Copy

A single replica acts as the primary coordinator for the updates.
Writes are forwarded only to the primary. Then, the primary notifies other replicas; however, the writes are ordered only after they are broadcast to all other replicas.
Reads are serviced only from the primary, ensuring write order consistency.

Availability and Partitions

Strongly consistent models require synchronization across all replicas, which can impact availability, especially during network partitions when a part of the replicas is unreachable.
The system may still be usable through a "partition-tolerant" approach (a mechanism where the system can continue without fully synchronizing data across all replicas as they would in a "strongly consistent" environment).

Partitions between Data Centers

Data center partitions arise from network problems, making it difficult to coordinate updates between different data centers.

Types of Partition

A study classified failures due to network partitions based on the impact on nodes and communications. (Complete, partial, simplex).

The CAP Imppossibility Result

Consistency (any strong from), Availability, and Partition tolerance are mutually exclusive in distributed systems.
A system can only satisfy two of the three criteria.

AP System: Amazon Dynamo

Dynamo implements an AP (Availability and Partition tolerance) system to provide availability in case of partitioning.
Items are stored in a Distributed Hash Table on multiple servers.

The Dynamo DHT

Servers are organized as a virtual ring.
Each server owns the range of keys between its predecessor and itself on the ring.
Data is replicated on three subsequent nodes on the ring.

Dynamo Coherence

Writes are handled by any replica, while version vectors track the history seen by each replica before updates are initiated.
These models allow some concurrent writes while maintaining a "causality tracking", but without complete consistency across all replicas.

Eventual Consistency

Writes will eventually be visible from all replicas.
Order of writes isn't guaranteed as soon as they happen; a future point in time is all that is guaranteed.
Resolving conflicts requires a resolution mechanism (like last-writer-wins or others).

Advantages of Eventual Consistency

Fast writes and reads, and the availability to support the consistency of service processes despite partition issues.

Disadvantages of Eventual Consistency

Potential for complexity in programming.
Potential for non-deterministic behavior when reads don't reflect the latest writes.

Anomaly 1: Comment Reordering

Example demonstrating that the order of comments in distributed systems can be random, with potential discrepancies in the write process.

Anomaly II: Ooops

Example illustrating a scenario where an application-level conflict (that is outside of the data) can result in a non-deterministic outcome, where the actions don't align with user expectations.

Anomaly III: Photo Privacy Violation

Illustrative scenario demonstrating conflicting data modifications in distributed environments where one user modifies the data and another user later also modifies it at a different time.

Eventual Consistency and Dropbox

Dropbox prioritizes availability (including offline use case) and sacrifices strong consistency.
Local copies are treated as geographically distant replicas, and concurrent updates can have conflicts that need resolution.

Conflicts Arise Even in Non-partitioned Mode

Illustrates conflicts that can be apparent even when no system partitions are present, showcasing that consistency can break down due to concurrent writes.

Dropbox Experiment Takeaway

Choice of a consistency model is a tradeoff: choose high availability instead of strong consistency.

Part 2: Coordination Kernels and the Example of Apache ZooKeeper

Presents solutions for the coordination issues in distributed systems.

Introduction

Many distributed computer systems use coordination processes.

Example of Coordinating Computation in Apache Hadoop

Distributed systems like Hadoop rely on coordination functions.

Example of Difficulties with a Simple Model - Master Crashes

Master failure in a system means no task assignments happen.

Example of Difficulties with a Simple Model - Worker Crashes

Worker crashes can cause task loss and need for reassignment.

Example of Difficulties with a Simple Model - Worker Does Not Receive Assignment

Worker tasks do not always get assigned; coordination is required.

Duplicate the master?

Redundancy for high availability involves complicated state sync.

Save the Master State in Some Data Store

Maintaining master state as a shared resource to facilitate recovery.

Use of a Coordination Service

Dedicated service manages master-worker interactions.

Use of a Coordination Service - Alternative 1: No Master

Workers manage coordination logic locally (without a central master, with a potential risk for inconsistency).

Use of a Coordination Service - Alternative 2: Elected Master

Election mechanism chooses a single server as the master (leader) and other workers can become a master as well.

Coordination is Difficult

Many difficulties in coordination processes like reliability, latency, bandwidth, and node health.

Apache ZooKeeper

A dedicated coordination service to help manage distributed processes.

ZooKeeper: The Big Picture

ZooKeeper is a specialized server to facilitate coordination.

ZooKeeper Data Model

Uses sessions, ephemeral and regular nodes, with hierarchies like a file system.

ZooKeeper API: create

Used to create znodes (nodes) within the ZooKeeper distributed naming service, where processes can use to coordinate themselves.

ZooKeeper API: setData

Allows updating data associated with a specific znode.

ZooKeeper API: delete

Removes znodes.

ZooKeeper API: Reading

Used to access and retrieve data stored in a znode.

Watches and Notifications

Watches allow clients to track node modifications.

ZooKeeper Consistency Model

Ordering of client operations using TCP order, reads are not linearizable but the system is sequentially consistent, allowing reads from many servers.

ZooKeeper Coordination Recipes

Practical coordination paradigms.

Implementing Shared Configuration (1)

Maintaining configuration consistency.

Implementing Shared Configuration (2)

Watching for configuration changes to prevent accessing stale data.

Implementing Rendezvous

A solution enables a rendezvous point within the ZooKeeper service to coordinate processes based on a chosen path reference.

Implementing Group Membership

Dynamically tracking the state of nodes in the system.

Implementing Locks

Different locks can be implemented for read/write and have different requirements in concurrency.

Performance

ZooKeeper performance scales adequately; a table shows the speed.

Throughput with Failures

ZooKeeper performance is robust under various failures and recoveries.

Conclusion

Replication, coherence, and coordination are critical to high-availability systems, and ZooKeeper is a powerful coordination service designed to offer these core components.

References

List of cited research papers and articles that support the concepts discussed.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

This quiz tests your knowledge on the evaluation of the ZooKeeper system, focusing on its architecture, performance metrics, and communication mechanisms. It covers aspects like server configuration, throughput, request handling, and session management. Perfect for those studying distributed systems and ZooKeeper functionality.