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The Architecture of Open Source Applications: Elegance, Evolution, and a Few Fearless Hacks > The NoSQL Ecosystem > Flashcards

The NoSQL Ecosystem Flashcards

1
Q

NoSQL: definition, according to the community

A

Not Only a SQL interface, referring to providing an alternative rather than a wholesale replacement for SQL

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2
Q

SQL’s expressiveness makes it challenging to ___

A

reason about the cost of each query, and thus the cost of a workload

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3
Q

Application developers may find using relational data models to be challenging because ___

A

it may not be perfect for modeling every kind of data (i.e. lists, queues, sets, etc)

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4
Q

If relational data grows past the capacity of one server, then ___

A

the tables in the database will have to be partitioned across computers, leading to denormalization

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5
Q

Complex query logic is typically left to the application, resulting in ___

A

a data store with more predictable query performance because of lack of variability in queries

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6
Q

Two characteristics of Google’s BigTable

A

hierarchical range-based partitioning scheme

strict consistency

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7
Q

Two characteristics of Amazon’s Dynamo

A

Maps keys to application-specific blobs of data

Loose consistency makes the partitioning model resilient to failure

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8
Q

Considerations regarding NoSQL systems (SPACTSDD)

A 
Scalability
Partitioning
Analytical workloads
Consistency
Transactional semantics
Single-server performance
Data and query model
Durability
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9
Q

The simplest form of a NoSQL store is a ___

A

key-value store

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10
Q

key-value store

A

each key is mapped to a value containing arbitrary data

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11
Q

store popularized by Redis

A

key-data structure store

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12
Q

key-data structure store

A

assigns each value a type (i.e. integer, string, list, set, sorted set, etc)

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13
Q

store common to CouchDB, MongoDB, Riak

A

key-document store

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14
Q

key-document store

A

map a key to some document that contains structured information in a JSON or JSON-like format

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15
Q

key-document stores grant a lot of freedom in document modeling, however ___

A

application-based query logic can become exceedingly complex

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16
Q

store common to HBase, Cassandra

A

BigTable column family store

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17
Q

column family store

A
  • complex key identifies a row containing data stored in one or more Column Families
  • each row can contain multiple columns with a CF
  • values within each column are timestamped
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18
Q

store common to HyperGraphDB, Neo4J

A

graph store

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19
Q

exception to key-only lookup: MongoDB

A

allows indexing of data based on any number of properties and has a relatively high-level language for specifying which data to retrieve

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20
Q

exception to key-only lookup: BigTable-based systems

A

support scanners to iterate over a column family and select particular items by a filter on a column

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21
Q

exception to key-only lookup: CouchDB

A

allows creation of different views of the data and running MapReduce tasks across the table to facilitate more complex lookups and updates

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22
Q

ACID

A

Atomic
Consistency
Isolation
Durability

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23
Q

Most NoSQL systems choose performance over ___

A

full ACID guarantees

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24
Q

Redis is an exception to NoSQL’s no-transaction trend, in that ___

A

it provides a MULTI command to combine multiple operations atomically and a WATCH command to allow isolation

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25
Q

benefits of schema-free storage

A

supports less structured data requirements and requires less structured data requirements

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26
Q

after a few iterations of a project relying on sloppy-schema NoSQL systems, ___

A

data and schema versioning is usually present in application-level code

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27
Q

single-server durability

A

ensures that any data modification will survive a server restart or power loss

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28
Q

the OS may not immediately write data to an on-disk file, instead ___

A

buffering the write to group several writes together in a single operation

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29
Q

typical hard drives can perform ___ random accesses (seeks) per second

A

100 - 200

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30
Q

typical hard drives are limited to ___ of sequential writes

A

30 - 100 MB/s

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31
Q

ensuring efficient single-server durability means ___

A

limiting the number of random writes the system incurs and increasing the number of sequential writes per hard drive

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32
Q

Ideally, one wants to minimize ___ and maximize ___, all while ___

A

the number of writes between fsync calls

the number of those writes that are sequential

never telling the user their data has written until the write has been fsynced

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33
Q

Techniques for improving performance of single-server durability guarantees

A

Control fsync frequency
Increase sequential writes by logging
Increase throughput by grouping writes

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34
Q

Memcached offers ___ in exchange for ___

A

no on-disk durability

extremely fast in-memory operations

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35
Q

Redis offers several options for ___

A

when to call fsync

36
Q

To reduce random writes, some systems ___

A

append update operations to a sequentially written file (a log)

37
Q

log-structured merge tree / log-structured hash table

A

combining logs and lookup data structures into one

38
Q

Techniques such as log-structure merge trees / hash tables and modified B+ trees ___

A

result in improved write throughput, but require a periodic log compaction

39
Q

group commit

A

grouping multiple concurrent updates within a short window into a single fsync call

40
Q

benefit of group commit

A

increase in throughput, as multiple log appends can happen in a single fsync

41
Q

drawback of group commit

A

higher latency per update, as users must wait on several concurrent updates for acknowledgement of their own update

42
Q

Multi-server durability varies between systems as either ___ or ___

A

traditional primary-replica structure

replication where multiple servers store copies of the data

43
Q

scaling up

A

adding more RAM and disks to handle load on one machine

44
Q

scaling out

A

replicate data and spread requests across multiple machines

45
Q

the ideal horizontal scalability goal is

A

linear scalability

46
Q

linear scalability

A

doubling the number of machines in your storage system doubles the query capacity of the system

47
Q

sharding

A

the act of splitting your read and write workloads across multiple machines to scale out your storage system

48
Q

sharding your data means that no one machine ___ but also is unable to ____

A

has to handle the write workload on the entire dataset

answer queries about the entire dataset

49
Q

sharding adds ___

A

system complexity

50
Q

two ways to scale without sharding

A

read replicas

caching

51
Q

read replica structure

A

make copies of the data on multiple machines, while write requests go to a primary node

52
Q

Generally, the less stringent the demands for freshness of content, the more you can ___

A

use read replicas to improve read-only query performance

53
Q

___ and ___ allow you to scale up your read-heavy workloads

A

read replicas

caching

54
Q

To add memory to Memcached’s cache pool:

A

just add another Memcached host

55
Q

sharding through coordinators: Lounge and BigCouch

A

a coordinator distributes requests to individual CouchDB instances based on the key of the requested doc

56
Q

sharding through coordinators: Gizzard

A

takes standalone data stores and arranges them in trees of any depth to partition keys by key range

57
Q

NoSQL systems built around Dynamo’s consistent hashing technique

A

Voldemort
Riak
Cassandra

58
Q

consistent hashing

A

a kind of hashing such that when a hash table is resized, only K/n keys need to be remapped on average, where K is the number of keys, and n is the number of slots

59
Q

range partitioning differs from consistent hashing in that ___

A

two keys that are next to each other in the key’s sort order are likely to appear in the same partition

60
Q

range partitioning allows active management of load by ___

A

having a load manager that can reduce the size of a range on an overloaded server

61
Q

tablets in BigTable

A

stores a range of row keys and values within a column family, maintaining all necessary logs and data structures to answer queries

62
Q

as BigTable’s tablets change in size, ___

A

two small tablets may merge or a big tablet splits in two

63
Q

a primary server in BigTable manages ___

A

tablet size, load, and availability

64
Q

to recognize and handle machine failures, the BigTable paper describe the use of Chubby, which is ___

A

a distributed locking system for managing server membership and liveness

65
Q

ZooKeeper is used in several Hadoop-based projects to ___

A

manage secondary leader servers and tablet server reassignment

66
Q

BigTable employs a hierarchical approach to range-partitioning by ___

A

maintaining tablet assignment in a metadata table, which is also sharded into tablets

67
Q

HBase uses BigTable’s hierarchical approach to range-partitioning by ___

A

using HDFS to handle data storage, replication, and consistency, leaving the rest to servers

68
Q

MongoDB handles range-partitioning by ___

A

using config nodes to specify key ranges, staying in sync with a two-phase commit protocol

69
Q

Cassandra allows fast range scans over data by ___

A

preserving order in its partitioning, mapping data to the server directly managing its key range

70
Q

Gizzard’s routing servers ___

A

form routing hierarchies of any depth, assigning ranges of keys to servers below them in the hierarchy

71
Q

range partitioning is the obvious choice when ___

A

one will be frequently be performing range scans over the keys of the data, avoiding random node jumps over the network

72
Q

range partitioning requires the up-front cost of ___

A

maintaining routing and configuration nodes

73
Q

when executed well, range partitioning data can be load-balanced ___

A

in small chunks which can be re-assigned in high-load situations

74
Q

In practice, maintaining replicas are hard and the following will happen:

A

crash and get out of sync
crash and never come back
networks will partition two sets of replicas
messages between machines will get delayed or lost

75
Q

two major approaches to data consistency in NoSQL ecosystem

A

strong consistency

eventual consistency

76
Q

systems that promote strong consistency ___

A

ensure that the replicas of a data item will always be able to come to consensus on the value of a key

77
Q

the minimum R, W, and N choices for ensuring strong consistency while allowing temporary replica disagreements is

A

R + W = N + 1

78
Q

in HDFS, a write cannot succeed until ___, while a read ___

A

it has been replicated to all N servers (W=N)

will be satisfied by a single replica (R=1)

79
Q

Dynamo-based systems use a type of versioning called

A

vector clocks

80
Q

Voldemort handles conflicts by ___

A

returning multiple copies of the key to the requesting client application

81
Q

Cassandra resolved conflicts by ___

A

using the most recently timestamped version of the data

82
Q

Voldemort’s and Cassandra’s conflict resolution are both present in ___

A

Riak

83
Q

CouchDB provides a hybrid of Voldemort’s and Cassandra’s conflict resolution:

A

it identifies a conflict and allows users to query for conflicted keys for manual repair, but deterministically picks a version to return to users until conflicts are repaired

84
Q

read repair is handled in Dynamo-based systems by ___

A

repairing out-of-sync replicas of the data in the background while returning the non-conflicting data to the requestor

85
Q

hinted handoff

A

assigning a node to temporarily take over an unavailable node’s write workload, forwarding all those writes when the node is available again

86
Q

Cassandra and Riak synchronize from one another using ___

A

Merkle trees

87
Q

gossip

A

periodically (~1s) a node will communicate with a random node to exchange knowledge on other nodes’ health

The Architecture of Open Source Applications: Elegance, Evolution, and a Few Fearless Hacks (6 decks)

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