Introduction |
|
1 | (4) |
|
|
2 | (1) |
|
|
3 | (1) |
|
|
4 | (1) |
|
|
4 | (1) |
Part I: Getting Started with NoSOL |
|
5 | (90) |
|
Chapter 1 Introducing NoSQL: The Big Picture |
|
|
7 | (20) |
|
|
8 | (3) |
|
|
8 | (3) |
|
|
11 | (1) |
|
|
11 | (9) |
|
|
12 | (5) |
|
|
17 | (1) |
|
|
18 | (2) |
|
Why You Should Care about NoSQL |
|
|
20 | (7) |
|
|
20 | (1) |
|
Problems with conventional approaches |
|
|
21 | (5) |
|
NoSQL benefits and precautions |
|
|
26 | (1) |
|
Chapter 2 NoSQL Database Design and Terminology |
|
|
27 | (32) |
|
Managing Different Data Types |
|
|
28 | (11) |
|
|
30 | (2) |
|
|
32 | (1) |
|
|
33 | (3) |
|
|
36 | (1) |
|
|
37 | (1) |
|
|
38 | (1) |
|
|
38 | (1) |
|
|
39 | (3) |
|
Applying Consistency Methods |
|
|
42 | (9) |
|
|
42 | (2) |
|
|
44 | (1) |
|
|
45 | (1) |
|
|
45 | (3) |
|
Developing applications on NoSQL |
|
|
48 | (1) |
|
|
48 | (1) |
|
Polyglot persistence explained |
|
|
49 | (1) |
|
|
50 | (1) |
|
Integrating Related Technologies |
|
|
51 | (8) |
|
|
52 | (1) |
|
Business Intelligence, dashboarding, and reporting |
|
|
53 | (1) |
|
Batch processing with Hadoop Map/Reduce |
|
|
54 | (1) |
|
|
55 | (1) |
|
|
56 | (1) |
|
|
57 | (2) |
|
Chapter 3 Evaluating NoSQL |
|
|
59 | (36) |
|
|
59 | (13) |
|
Which type of NoSQL is for you? |
|
|
61 | (1) |
|
|
61 | (4) |
|
|
65 | (2) |
|
|
67 | (2) |
|
|
69 | (2) |
|
Extending your data layer |
|
|
71 | (1) |
|
|
72 | (5) |
|
|
73 | (1) |
|
|
73 | (1) |
|
|
73 | (1) |
|
Deciding on open-source versus commercial software |
|
|
74 | (1) |
|
|
75 | (1) |
|
Evaluating vendor capabilities |
|
|
76 | (1) |
|
Finding support worldwide |
|
|
76 | (1) |
|
|
77 | (1) |
|
|
77 | (20) |
|
Business or mission-critical features |
|
|
78 | (1) |
|
|
78 | (1) |
|
|
79 | (1) |
|
|
80 | (8) |
|
|
88 | (1) |
|
|
89 | (3) |
|
|
92 | (1) |
|
|
92 | (3) |
Part II: key-Value Stores |
|
95 | (44) |
|
Chapter 4 Common Features of Key-Value Stores |
|
|
97 | (8) |
|
|
98 | (1) |
|
|
98 | (1) |
|
Implementing ACID support |
|
|
99 | (1) |
|
|
99 | (2) |
|
|
100 | (1) |
|
Accessing data on partitions |
|
|
101 | (1) |
|
|
101 | (4) |
|
Data types in key-value stores |
|
|
102 | (1) |
|
|
103 | (1) |
|
|
103 | (2) |
|
Chapter 5 Key-Value Stores in the Enterprise |
|
|
105 | (6) |
|
|
105 | (2) |
|
Simple data model — fast retrieval |
|
|
107 | (1) |
|
|
107 | (1) |
|
|
107 | (4) |
|
|
108 | (1) |
|
Complex structure handling |
|
|
108 | (3) |
|
Chapter 6 Key-Value Use Cases |
|
|
111 | (6) |
|
Managing User Information |
|
|
111 | (3) |
|
Delivering web advertisements |
|
|
112 | (1) |
|
|
112 | (1) |
|
Supporting personalization |
|
|
113 | (1) |
|
|
114 | (3) |
|
Chapter 7 Key-Value Store Products |
|
|
117 | (16) |
|
|
118 | (3) |
|
|
118 | (1) |
|
Replicating data to slaves |
|
|
118 | (1) |
|
Data modeling in key-value stores |
|
|
119 | (1) |
|
|
120 | (1) |
|
|
120 | (1) |
|
Taking Advantage of Flash |
|
|
121 | (2) |
|
|
121 | (1) |
|
|
121 | (1) |
|
|
122 | (1) |
|
|
123 | (2) |
|
|
123 | (1) |
|
|
124 | (1) |
|
|
124 | (1) |
|
Separating Data Storage and Distribution |
|
|
125 | (3) |
|
Using Berkeley DB for single node storage |
|
|
125 | (1) |
|
|
126 | (1) |
|
|
126 | (2) |
|
|
128 | (5) |
|
|
128 | (1) |
|
|
129 | (1) |
|
|
130 | (3) |
|
|
133 | (6) |
|
Choosing a Key-Value Store |
|
|
133 | (3) |
|
Ensuring skill availability |
|
|
134 | (1) |
|
Integrating with Hadoop Map/Reduce |
|
|
134 | (1) |
|
|
135 | (1) |
|
Finding Riak Support (Basho) |
|
|
136 | (5) |
|
|
136 | (1) |
|
|
137 | (1) |
|
|
137 | (2) |
Part III: Bigtable Clones |
|
139 | (60) |
|
Chapter 9 Common Features of Bigtables |
|
|
141 | (12) |
|
Storing Data in Bigtables |
|
|
142 | (1) |
|
|
142 | (1) |
|
|
142 | (1) |
|
|
143 | (1) |
|
|
143 | (1) |
|
|
143 | (5) |
|
Partitioning your database |
|
|
144 | (1) |
|
|
145 | (1) |
|
|
146 | (2) |
|
|
148 | (2) |
|
|
148 | (1) |
|
|
148 | (1) |
|
|
149 | (1) |
|
|
150 | (3) |
|
|
150 | (1) |
|
|
150 | (1) |
|
|
151 | (2) |
|
Chapter 10 Bigtable in the Enterprise |
|
|
153 | (12) |
|
Managing Multiple Data Centers |
|
|
153 | (3) |
|
|
154 | (1) |
|
|
154 | (2) |
|
|
156 | (2) |
|
|
156 | (1) |
|
|
157 | (1) |
|
|
158 | (7) |
|
Ingesting data in parallel |
|
|
159 | (1) |
|
|
159 | (1) |
|
|
160 | (2) |
|
|
162 | (1) |
|
Configuring dynamic clusters |
|
|
163 | (2) |
|
Chapter 11 Bigtable Use Cases |
|
|
165 | (6) |
|
|
165 | (3) |
|
Using an RDBMS to store sparse data |
|
|
166 | (1) |
|
|
167 | (1) |
|
|
168 | (3) |
|
|
168 | (1) |
|
|
169 | (2) |
|
Chapter 12 Bigtable Products |
|
|
171 | (22) |
|
Managing Tabular Big Data |
|
|
172 | (7) |
|
|
172 | (4) |
|
Distributing data with HDFS |
|
|
176 | (1) |
|
Batch processing Bigtable data |
|
|
177 | (2) |
|
|
179 | (1) |
|
|
179 | (5) |
|
|
180 | (3) |
|
|
183 | (1) |
|
High-Performing Bigtables |
|
|
184 | (3) |
|
|
184 | (1) |
|
|
184 | (1) |
|
Ensuring data consistency |
|
|
185 | (1) |
|
|
186 | (1) |
|
Distributing Data Globally |
|
|
187 | (6) |
|
Substituting a key-value store |
|
|
188 | (1) |
|
|
189 | (1) |
|
Replicating data globally |
|
|
190 | (1) |
|
|
190 | (3) |
|
Chapter 13 Cassandra and DataStax |
|
|
193 | (6) |
|
Designing a Modern Bigtable |
|
|
193 | (4) |
|
|
194 | (1) |
|
|
194 | (1) |
|
|
195 | (1) |
|
|
195 | (1) |
|
|
196 | (1) |
|
Finding Support for Cassandra |
|
|
197 | (4) |
|
Managing and monitoring Cassandra |
|
|
197 | (1) |
|
|
197 | (2) |
Part IV: Document Databases |
|
199 | (58) |
|
Chapter 14 Common Features of Document Databases |
|
|
201 | (12) |
|
Using a Tree-Based Data Model |
|
|
202 | (6) |
|
Handling article documents |
|
|
204 | (1) |
|
Managing trades in financial services |
|
|
204 | (1) |
|
Discovering document structure |
|
|
205 | (1) |
|
Supporting unstructured documents |
|
|
206 | (2) |
|
Document Databases as Key-Value Stores |
|
|
208 | (1) |
|
Modeling values as documents |
|
|
208 | (1) |
|
|
208 | (1) |
|
|
209 | (4) |
|
Supporting partial updates |
|
|
209 | (1) |
|
|
210 | (1) |
|
Providing alternate structures in real time |
|
|
211 | (2) |
|
Chapter 15 Document Databases in the Enterprise |
|
|
213 | (8) |
|
|
214 | (1) |
|
|
214 | (1) |
|
|
214 | (1) |
|
|
215 | (3) |
|
|
216 | (1) |
|
Using multiple datacenters |
|
|
217 | (1) |
|
Selectively replicating data |
|
|
217 | (1) |
|
|
218 | (3) |
|
Using eventual consistency |
|
|
219 | (1) |
|
|
219 | (2) |
|
Chapter 16 Document Database Use Cases |
|
|
221 | (12) |
|
|
221 | (4) |
|
Managing content lifecycle |
|
|
222 | (1) |
|
Distributing content to sales channels |
|
|
223 | (2) |
|
Managing Unstructured Data Feeds |
|
|
225 | (1) |
|
Entity extraction and enrichment |
|
|
225 | (1) |
|
Managing Changing Data Structures |
|
|
226 | (3) |
|
|
227 | (1) |
|
Managing change over time |
|
|
228 | (1) |
|
|
229 | (4) |
|
Handling incoming streams |
|
|
229 | (1) |
|
Amalgamating related data |
|
|
230 | (1) |
|
Providing answers as documents |
|
|
231 | (2) |
|
Chapter 17 Document Database Products |
|
|
233 | (18) |
|
Providing a Memcache Replacement |
|
|
233 | (3) |
|
Ensuring high-speed reads |
|
|
234 | (1) |
|
Using in-memory document caching |
|
|
234 | (1) |
|
Supporting mobile synchronization |
|
|
235 | (1) |
|
|
235 | (1) |
|
Providing a Familiar Developer Experience |
|
|
236 | (4) |
|
|
236 | (1) |
|
|
237 | (1) |
|
Linking to your programming language |
|
|
238 | (1) |
|
Evaluating Microsoft DocumentDB |
|
|
239 | (1) |
|
Providing an End-to-End Document Platform |
|
|
240 | (7) |
|
Ensuring consistent fast reads and writes |
|
|
241 | (1) |
|
|
242 | (1) |
|
Using advanced content search |
|
|
243 | (1) |
|
|
244 | (2) |
|
Evaluating MarkLogic Server |
|
|
246 | (1) |
|
Providing a Web Application Back End |
|
|
247 | (4) |
|
Trading consistency for speed |
|
|
248 | (1) |
|
Sticking with JavaScript and JSON |
|
|
249 | (1) |
|
|
249 | (1) |
|
|
249 | (2) |
|
|
251 | (6) |
|
Using an Open-Source Document Database |
|
|
251 | (3) |
|
|
252 | (1) |
|
Finding a language binding |
|
|
252 | (1) |
|
|
253 | (1) |
|
Finding Support for MongoDB |
|
|
254 | (5) |
|
|
254 | (1) |
|
Licensing advanced features |
|
|
255 | (1) |
|
Ensuring a sustainable partner |
|
|
256 | (1) |
Part V: Graph and Triple Stores |
|
257 | (58) |
|
Chapter 19 Common Features of Triple and Graph Stores |
|
|
259 | (16) |
|
Deciding on Graph or Triple Stores |
|
|
260 | (4) |
|
|
260 | (2) |
|
|
262 | (1) |
|
|
263 | (1) |
|
|
263 | (1) |
|
Deciding on Triples or Quads |
|
|
264 | (5) |
|
|
265 | (2) |
|
|
267 | (1) |
|
|
268 | (1) |
|
|
268 | (1) |
|
Managing Triple Store Structures |
|
|
269 | (6) |
|
|
269 | (2) |
|
Enhancing your vocabulary with SKOS |
|
|
271 | (1) |
|
Describing data provenance |
|
|
272 | (3) |
|
Chapter 20 Triple Stores in the Enterprise |
|
|
275 | (8) |
|
|
275 | (3) |
|
|
276 | (1) |
|
Sharding and replication for high availability |
|
|
277 | (1) |
|
Replication for disaster recovery |
|
|
278 | (1) |
|
Storing Documents with Triples |
|
|
278 | (5) |
|
|
279 | (1) |
|
|
280 | (3) |
|
Chapter 21 Triple Store Use Cases |
|
|
283 | (10) |
|
Extracting Semantic Facts |
|
|
284 | (2) |
|
Extracting context with subjects |
|
|
284 | (1) |
|
|
285 | (1) |
|
|
286 | (1) |
|
|
287 | (3) |
|
Taking advantage of open data |
|
|
287 | (1) |
|
Incorporating data from GeoNames |
|
|
288 | (1) |
|
Incorporating data from DBpedia |
|
|
289 | (1) |
|
Linked open-data publishing |
|
|
289 | (1) |
|
|
290 | (1) |
|
Managing the Social Graph |
|
|
290 | (3) |
|
Storing social information |
|
|
291 | (1) |
|
Performing social graph queries |
|
|
291 | (2) |
|
Chapter 22 Triple Store Products |
|
|
293 | (16) |
|
Managing Documents and Triples |
|
|
294 | (6) |
|
Storing documents and relationships |
|
|
294 | (2) |
|
Combining documents in real time |
|
|
296 | (1) |
|
|
297 | (1) |
|
|
298 | (1) |
|
|
298 | (1) |
|
Evaluating MarkLogic Server |
|
|
299 | (1) |
|
|
300 | (4) |
|
|
301 | (1) |
|
|
301 | (1) |
|
|
302 | (1) |
|
|
302 | (1) |
|
Integrating with SoIr and MongoDB |
|
|
303 | (1) |
|
|
304 | (1) |
|
Using a Distributed Graph Store |
|
|
304 | (5) |
|
Adding metadata to relationships |
|
|
305 | (1) |
|
Optimizing for query speed |
|
|
305 | (1) |
|
Using custom graph languages |
|
|
305 | (1) |
|
|
306 | (3) |
|
Chapter 23 Neo4j and Neo Technologies |
|
|
309 | (6) |
|
|
309 | (3) |
|
Advanced path-finding algorithms |
|
|
310 | (1) |
|
Scaling up versus scaling out |
|
|
310 | (1) |
|
Complying with open standards |
|
|
311 | (1) |
|
Finding Support for Neo4j |
|
|
312 | (5) |
|
|
313 | (1) |
|
|
313 | (1) |
|
|
314 | (1) |
|
|
314 | (1) |
|
|
314 | (1) |
Part VI: Search Engines |
|
315 | (44) |
|
Chapter 24 Common Features of Search Engines |
|
|
317 | (10) |
|
Dissecting a Search Engine |
|
|
318 | (5) |
|
|
318 | (1) |
|
|
318 | (1) |
|
|
319 | (2) |
|
|
321 | (2) |
|
|
323 | (1) |
|
|
323 | (1) |
|
|
324 | (1) |
|
|
324 | (3) |
|
|
325 | (1) |
|
|
325 | (2) |
|
Chapter 25 Search Engines in the Enterprise |
|
|
327 | (8) |
|
|
327 | (2) |
|
|
328 | (1) |
|
|
328 | (1) |
|
Creating a Search Application |
|
|
329 | (6) |
|
Configuring user interfaces |
|
|
329 | (1) |
|
What a good search API gives you |
|
|
330 | (1) |
|
Going beyond basic search with analytics |
|
|
331 | (4) |
|
Chapter 26 Search Engine Use Cases |
|
|
335 | (6) |
|
Searching E-Commerce Products |
|
|
335 | (3) |
|
|
335 | (1) |
|
Geospatial distance scoring |
|
|
336 | (2) |
|
Enterprise Data Searching |
|
|
338 | (1) |
|
|
338 | (1) |
|
|
338 | (1) |
|
Searching application data |
|
|
339 | (1) |
|
|
339 | (2) |
|
Enabling proactive working |
|
|
339 | (1) |
|
|
340 | (1) |
|
Chapter 27 Types of Search Engines |
|
|
341 | (12) |
|
Using Common Open-Source Text Indexing |
|
|
341 | (3) |
|
|
342 | (1) |
|
|
342 | (1) |
|
Evaluating Lucene/SolrCloud |
|
|
343 | (1) |
|
Combining Document Stores and Search Engines |
|
|
344 | (4) |
|
|
345 | (1) |
|
|
345 | (1) |
|
Operating on in-memory data |
|
|
346 | (1) |
|
Retrieving fine-grained results |
|
|
346 | (1) |
|
|
347 | (1) |
|
Evaluating Enterprise Search |
|
|
348 | (1) |
|
|
348 | (1) |
|
Integrating NoSQL and HP Autonomy |
|
|
348 | (1) |
|
|
349 | (1) |
|
Evaluating Google search appliance |
|
|
349 | (1) |
|
Storing and Searching JSON |
|
|
349 | (4) |
|
|
350 | (1) |
|
|
350 | (2) |
|
|
352 | (1) |
|
|
353 | (6) |
|
Using the Elasticsearch Product |
|
|
353 | (4) |
|
|
354 | (1) |
|
|
354 | (1) |
|
|
355 | (1) |
|
|
355 | (2) |
|
Finding Support for Elasticsearch |
|
|
357 | (4) |
|
Evaluating Elasticsearch BV |
|
|
357 | (2) |
Part VII: Hybrid NoSQL Databases |
|
359 | (40) |
|
Chapter 29 Common Hybrid NoSQL Features |
|
|
361 | (8) |
|
The Death of Polyglot Persistence |
|
|
362 | (1) |
|
One product, many features |
|
|
362 | (1) |
|
Best-of-breed solution versus single product |
|
|
363 | (1) |
|
Advantages of a Hybrid Approach |
|
|
363 | (6) |
|
Single product means lower cost |
|
|
364 | (1) |
|
How search technology gives a better column store |
|
|
365 | (1) |
|
How semantic technology assists content discovery |
|
|
366 | (3) |
|
Chapter 30 Hybrid Databases in the Enterprise |
|
|
369 | (6) |
|
Selecting a Database by Functionality |
|
|
369 | (2) |
|
Ensuring functional depth and breadth |
|
|
370 | (1) |
|
Following a single product's roadmap |
|
|
370 | (1) |
|
Building Mission-Critical Applications |
|
|
371 | (4) |
|
|
371 | (1) |
|
Ensuring data is accessible |
|
|
372 | (1) |
|
Operating in high-security environments |
|
|
373 | (2) |
|
Chapter 31 Hybrid NoSQL Database Use Cases |
|
|
375 | (6) |
|
Digital Semantic Publishing |
|
|
375 | (2) |
|
Journalists and web publishing |
|
|
376 | (1) |
|
Changing legislation over time |
|
|
377 | (1) |
|
|
377 | (4) |
|
|
378 | (1) |
|
|
378 | (1) |
|
|
379 | (2) |
|
Chapter 32 Hybrid NoSQL Database Products |
|
|
381 | (8) |
|
Managing Triples and Aggregates |
|
|
381 | (2) |
|
Generating triples from documents |
|
|
382 | (1) |
|
|
383 | (1) |
|
|
383 | (1) |
|
Combining Documents and Triples with Enterprise Capabilities |
|
|
383 | (6) |
|
Combined database, search, and application services |
|
|
384 | (1) |
|
Schema free versus schema agnostic |
|
|
385 | (1) |
|
Providing Bigtable features |
|
|
385 | (1) |
|
Securing access to information |
|
|
386 | (1) |
|
|
387 | (2) |
|
|
389 | (10) |
|
Understanding MarkLogic Server |
|
|
389 | (1) |
|
|
390 | (6) |
|
Range indexing and aggregate queries |
|
|
391 | (1) |
|
Combining content and semantic technologies |
|
|
392 | (1) |
|
|
393 | (1) |
|
Replication on intermittent networks |
|
|
394 | (1) |
|
|
394 | (1) |
|
Compartmentalizing information |
|
|
395 | (1) |
|
|
396 | (3) |
|
Finding trained developers |
|
|
396 | (1) |
|
|
397 | (1) |
|
Using MarkLogic in the cloud |
|
|
397 | (2) |
Part VIII: The Part of Tens |
|
399 | (20) |
|
Chapter 34 Ten Advantages of NoSQL over RDBMS |
|
|
401 | (6) |
|
Chapter 35 Ten NoSQL Misconceptions |
|
|
407 | (6) |
|
Chapter 36 Ten Reasons Developers Love NoSQL |
|
|
413 | (6) |
Index |
|
419 | |