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Further evaluation trials were conducted in conjunction with enhanced coagulation trials conducted at the Barossa WTP by United Water International during the time of this project. At the request of United Water International, models were developed on the basis of allowing dose prediction for selected percentage removals of coagulable DOC.

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This variability increases with lower coagulant dosing. Nonetheless, during the time of this project and subsequently, this model option has been reliably used by UWI after site specific correlation of a selected model percentage to a required actual removal percentage of DOC and where maximising DOC removal was not required at a particular WTP.

This approach is always recommended for this model option. These models require raw water DOC as an input variable and were developed so that coagulant dose predictions are similar to natural surface and ground waters as doses from models that do not require raw water DOC as an input variable.

The accuracies of pH predictions using the model developed in the previous CRC project 1. The results from this study indicate that better pH prediction accuracy may be obtained using models based on carbonate theory. The current models can be further developed to include coagulants other than alum and ferric chloride.

Models should also be expanded to include prediction of lime compounds used in the water industry to control pH in low buffered waters and for pH elevation prior to distribution in the network system. Papers published from work conducted through this project and relevant preceding papers from CRC for Water Quality and Treatment project 1. International Congress on Modelling and Simulation. ANU, Canberra, Australia. Management of productivity at water utilities, International Water Association Specialisation Conference.

Prague, AWWA, February, NOM Research: This Is Not an Installation Guide Installing the databases in this book is sometimes easy, sometimes challenging, and sometimes downright ugly.

Cutting out installation steps allows us to pack in more useful examples and a discussion of concepts, which is what you really want anyway, right? Administration Manual? Each of these databases has myriad options, settings, switches, and configuration details, most of which are well documented on the Web.

Microsoft environments tend to strive for an integrated environment, which limits many choices to a smaller predefined set. This is not our own bias so much as a reflection of the current state of affairs. If you run Windows and want to give it a try anyway, we recommend setting up Cygwin1 to give you the best shot at success. You may also want to consider running a Linux virtual machine. Code Examples and Conventions This book contains code in a variety of languages.

In part, this is a consequence of the databases that we cover. Except where noted, code listings are provided in full, usually ready to be executed at your leisure. Samples and snippets are syntax highlighted according to the rules of the language involved. Thanks for coming along with us on this journey through the modern database landscape. Eric Redmond and Jim R.

Wilson 1. For years the relational model has been the de facto option for problems big and small. These options are collectively known as NoSQL and make up the bulk of this book. In this book, we explore seven databases across the spectrum of database styles.

In the process of reading the book, you will learn the various functionality and trade-offs each database has—durability vs. Databases come in a variety of genres, such as relational, key-value, columnar, document-oriented, and graph. Popular databases—including those covered in this book—can generally be grouped into one of these broad categories.

Databases are not created in a vacuum. They are designed to solve problems presented by real use cases. RDBMS databases arose in a world where query flexibility was more important than flexible schemas. On the other hand, column-oriented datastores were built to be well suited for storing large amounts of data across several machines, while data relationships took a backseat. Databases often support a variety of connection options. In the final chapter, we present a more complex database setup tied together by a Node.

Any datastore will support writing data and reading it back out again. What else it does varies greatly from one to the next. Some allow querying on arbitrary fields. Some provide indexing for rapid lookup. Some support ad hoc queries; for others, queries must be planned.

Is schema a rigid framework enforced by the database or merely a set of guidelines to be renegotiated at will? Understanding capabilities and constraints will help you pick the right database for the job. How does this database function and at what cost? Does it support sharding? How about replication? Does it distribute data evenly using consistent hashing, or does it keep like data together?

Is this database tuned for reading, writing, or some other operation? How much control do you have over its tuning, if any? Scalability is related to performance. Talking about scalability without the context of what you want to scale to is generally fruitless. This book will give you the background you need to ask the right questions to establish that context.

Our goal is not to guide a novice to mastery of any of these databases. A full treatment of any one of them could and does fill entire books. But by the end you should have a firm grasp of the strengths of each, as well as how they differ. An individual song may share all of the same notes with other songs, but some are more appropriate for certain uses. Similarly, some databases are better for some situations over others.

Relational The relational model is generally what comes to mind for most people with database experience. Relational database management systems RDBMSs are set-theory-based systems implemented as two-dimensional tables with rows and columns.

Data values are typed and may be numeric, strings, dates, uninterpreted blobs, or other types. The types are enforced by the system. Importantly, tables can join and morph into new, more complex tables, because of their mathematical basis in relational set theory.

Key-Value The key-value KV store is the simplest model we cover. As the name implies, a KV store pairs keys to values in much the same way that a map or hashtable would in any popular programming language. Some KV implementations permit complex value types such as hashes or lists, but this is not required.

Some KV implementations provide a means of iterating through the keys, but this again is an added bonus. A filesystem could be considered a key-value store, if you think of the file path as the key and the file contents as the value.

As with relational databases, many open source options are available. Some of the more popular offerings include memcached and its cousins memcachedb and membase , Voldemort, and the two we cover in this book: Redis and Riak. Values in Riak can be anything, from plain text to XML to image data, and relationships between keys are handled by named structures called links. It also has one of the most robust query mechanisms for a KV store. And by caching writes in memory before committing to disk, Redis gains amazing performance in exchange for increased risk of data loss in the case of a hardware failure.

This characteristic makes it a good fit for caching noncritical data and for acting as a message broker. We leave it until the end—see Chapter 8, Redis, on page —so we can build a multidatabase application with Redis and others working together in harmony. Columnar Columnar, or column-oriented, databases are so named because the important aspect of their design is that data from a given column in the two-dimensional table sense is stored together. The difference may seem inconsequential, but the impact of this design decision runs deep.

In columnoriented databases, adding columns is quite inexpensive and is done on a row-by-row basis. Each row can have a different set of columns, or none at all, allowing tables to remain sparse without incurring a storage cost for null values. With respect to structure, columnar is about midway between relational and key-value. HBase This column-oriented database shares the most similarities with the relational model of all the nonrelational databases we cover.

HBase makes strong consistency guarantees and features tables with rows and columns—which should make SQL fans feel right at home. Document Document-oriented databases store, well, documents. In short, a document is like a hash, with a unique ID field and values that may be any of a variety of types, including more hashes.

Documents can contain nested structures, Download from Wow! Introduction and so they exhibit a high degree of flexibility, allowing for variable domains.

The system imposes few restrictions on incoming data, as long as it meets the basic requirement of being expressible as a document. Different document databases take different approaches with respect to indexing, ad hoc querying, replication, consistency, and other design decisions.

Choosing wisely between them requires understanding these differences and how they impact your particular use cases. Mongo server configurations attempt to remain consistent—if you write something, subsequent reads will receive the same value until the next update.

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Breckenridge made snow, and the slopes were immaculately groomed, but there was an inevitable sameness to the conditions on the mountain. Without fresh snow, the total experience was lacking. I had studied object-oriented databases at the University of Texas at Austin because after a decade of relational dominance, I thought that object-oriented databases had a real chance to take root. Still, the next decade brought more of the same relational models as before. I watched dejectedly as Oracle, IBM, and later the open source solutions led by MySQL spread their branches wide, completely blocking out the sun for any sprouting solutions on the fertile floor below. Over time, the user interfaces changed from green screens to client-server to Internet-based applications, but the coding of the relational layer stretched out to a relentless barrage of sameness, spanning decades of perfectly compe- tent tedium.

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