CASSANDRA DEFINITIVE GUIDE PDF
O'Reilly Media, Inc. Cassandra: The Definitive Guide, the image of a seas. sppn.info~zives/03f/cis/sppn.info, became the foundational work for rela-. Application Performance Optimization Summary. Contribute to sjtuhjh/appdocs development by creating an account on GitHub. CASSANDRA: THE DEFINITIVE GUIDE BY JEFF. CARPENTER, EBEN HEWITT PDF. Hence, this website provides for you to cover your problem. We reveal you.
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lobby had surged toward Maryse; Alec had broken away from Magnus, and Isabelle had leaped to her City of Lost Souls. Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles. Cassandra: The Definitive Guide [Eben Hewitt] on sppn.info *FREE* shipping on qualifying offers. What could you do with data if scalability wasn't a problem.
It is still used today. But in the years following the invention of IMS, the new model, the disruptive model,the threatening model, was the relational database. Edgar F. Understanding andworking with a relational database required learning new terms that must have soundedvery strange indeed to users of IMS. It presented certain advantages over its predecessor,in part because giants are almost always standing on the shoulders of other giants.
While these ideas and their application have evolved in four decades, the relationaldatabase still is clearly one of the most successful software applications in history. Relational databases store invoices, customer records, prod-uct catalogues, accounting ledgers, user authentication schemes—the very world, itmight appear.
There is no question that the relational database is a key facet of themodern technology and business landscape, and one that will be with us in its variousforms for many years to come, as will IMS in its various forms.
The relational modelpresented an alternative to IMS, and each has its uses.
Thisanswer takes the long view, which says that every once in a while an idea is born thatostensibly changes things, and engenders a revolution of sorts.
The horse, the car, the plane. They each coexist, even now. We encounter scalability problems when our relational applications become successfuland usage goes up.
Joins are inherent in any relatively normalized relational databaseof even modest size, and joins can be slow. This can become untenable under veryheavy loads, as the locks mean that competing users start queuing up, waiting for theirturn to read or write the data.
This is known as vertical scaling. This can relieve you for a time. Now you have the problem of data replication and consistency during regular usage and in failover scenarios.
This might mean optimizing the channels the database uses to write to the under- lying filesystem. We turn off logging or journaling, which frequently is not a desirable or, depending on your situation, legal option. We try to improve our indexes.
Cassandra The Definitive Guide.pdf
We optimize the queries. So this becomes a painful process of picking through the data access code to find any opportunities for fine tuning. This might include reducing or reorganizing joins, throwing out resource-intensive features such as XML processing within a stored procedure, and so forth.
For larger systems, this might include distributed caches such as memcached, EHCache, Oracle Coherence, or other related prod- ucts. Now we have a consistency problem between updates in the cache and updates in the database, which is exacerbated over a cluster.
We must therefore begin here in recognition that the relational model is simply amodel. It does not purport to be exhaustive, closing the case on all otherways of representing data, never again to be examined, leaving no room for alternatives. If we take the long view of history, Dr. The relational model was held up to sus-picion, and doubtless suffered its vehement detractors.
It encountered opposition evenin the form of Dr. But the relational model now arguably enjoys the best seat in the house within the dataworld. SQL is widely supported and well understood. It is taught in introductory uni-versity courses. Often the database we end up using is dictated to usby architectural standards within our organization. Ourcolleagues in development and infrastructure have considerable hard-won knowledge.
If by nothing more than osmosis—or inertia—we have learned over the years that arelational database is a one-size-fits-all solution. There are certain problems that relational databases solve very well. Relational data has served all of us developers and DBAs well.
But the explosion of theWeb, and in particular social networks, means a corresponding explosion in the sheervolume of data we must deal with.
When Tim Berners-Lee first worked on the Web inthe early s, it was for the purpose of exchanging scientific documents betweenPhDs at a physics laboratory. That means in part that it must support enormous volumesof data; the fact that it does stands as a monument to the ingenious architecture of theWeb. But some of this infrastructure is starting to bend under the weight.
In , a company like IBM was in a position to really make people listen to theirinnovations. They had the problems, and they had the brain power to solve them.
And you know best. It is not my intention to convince you by clever argument to adopt a non-relationaldatabase such as Apache Cassandra. It is only my intention to present what Cassandracan do and how it does it so that you can make an informed decision and get startedworking with it in practical ways if you find it applies.
Only you know what your dataneeds are. Would you collect more information about yourbusiness objects if you could?
What understanding of your organization would you liketo have, if only you could enable it? This will give us a basis on which toconsider more recent advances in thought around the trade-offs inherent in distributeddata systems, especially very large distributed data systems, such as those that arerequired at web scale.
SQL is powerful for a variety of reasons. It allows the user to represent complex rela-tionships with the data, using statements that form the Data Manipulation Language DML to insert, select, update, delete, truncate, and merge data. You can perform arich variety of operations using functions based on relational algebra to find a maximumor minimum value in a set, for example, or to filter and order results. SQL statementssupport grouping aggregate values and executing summary functions.
SQL also allows you to grant and revoke rights forusers and groups of users using the same syntax. SQL is easy to use. Junior developers can become proficient readily,and as is often the case in an industry beset by rapid changes, tight deadlines, andexploding budgets, ease of use can be very important.
Consider two customers are attempting to put the same item into their shoppingcarts on an ecommerce site. If I place the last item in stock into my cart an instant afteryou do, you should get the item added to your cart, and I should be informed that theitem is no longer available for download. This is guaranteed to happen when the stateof a write is consistent among all nodes that have that data. Out ofthe box, Cassandra trades some consistency in order to achieve total availability.
Pre-sumably such data is very important indeed to the companies running theseapplications, because that data is their primary product, and they are multibillion-dollar companies with billions of users to satisfy in a sharply competitive world. The detractors claim that some Big Data databases such as Cassandra have merelyeventual consistency, and that all other distributed systems have strict consistency.
Aswith so many things in the world, however, the reality is not so black and white, andthe binary opposition between consistent and not-consistent is not truly reflected inpractice. There are instead degrees of consistency, and in the real world they are verysusceptible to external circumstance. Eventual consistency is one of several consistency models available to architects. It requires that any read will always return the most recently written value. How- ever, upon closer examination, what do we find?
Most recently to whom? In one single-processor machine, this is no problem to observe, as the sequence of operations is known to the one clock. But in a system executing across a variety of geographically dispersed data centers, it becomes much more slippery.
Achieving this implies some sort of global clock that is capable of timestamping all operations, regardless of the location of the data or the user requesting it or how many possibly disparate services are required to determine the response. Causal consistency This is a slightly weaker form of strict consistency. It does away with the fantasy of the single global clock that can magically synchronize all operations without creating an unbearable bottleneck. Instead of relying on timestamps, causal con- sistency instead takes a more semantic approach, attempting to determine the cause of events to create some consistency in their order.
It means that writes that are potentially related must be read in sequence. If two different, unrelated oper- ations suddenly write to the same field, then those writes are inferred not to be causally related. But if one write occurs after another, we might infer that they are causally related.
Causal consistency dictates that causal writes must be read in sequence. Weak eventual consistency Eventual consistency means on the surface that all updates will propagate through- out all of the replicas in a distributed system, but that this may take some time.
Eventually, all replicas will be consistent. Eventual consistency becomes suddenly very attractive when you consider what is re-quired to achieve stronger forms of consistency. At the center of the problem isdata update replication. To achieve a strict consistency, all update operations will beperformed synchronously, meaning that they must block, locking all replicas until theoperation is complete, and forcing competing clients to wait. A side effect of such adesign is that during a failure, some of the data will be entirely unavailable.
The diffi-culty this approach presents is that now we are forced into the situation of detectingand resolving conflicts. A design approach must decide whether to resolve these con-flicts at one of two possible times: during reads or during writes.
That is, a distributeddatabase designer must choose to make the system either always readable or alwayswritable.
Dynamo and Cassandra choose to be always writable, opting to defer the complexityof reconciliation to read operations, and realize tremendous performance gains.
Thealternative is to reject updates amidst network and server failures. This is done by setting the consistency level againstthe replication factor.
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The replication factor lets you decide how much you want to pay in performance togain more consistency. You set the replication factor to the number of nodes in thecluster you want the updates to propagate to remember that an update means anyadd, update, or delete operation.
The consistency level is a setting that clients must specify on every operation and thatallows you to decide how many replicas in the cluster must acknowledge a write op-eration or respond to a read operation in order to be considered successful. So if you like, you could set the consistency level to a number equal to the replicationfactor, and gain stronger consistency at the cost of synchronous blocking operationsthat wait for all nodes to be updated and declare success before returning.
So if the client setsthe consistency level to a value less than the replication factor, the update is consideredsuccessful even if some nodes are down.
The theorem states that within a large-scale distributed data system, there are three The Cassandra Elevator Pitch 19 requirements that have a relationship of sliding dependency: Consistency, Availability,and Partition Tolerance. Consistency All database clients will read the same value for the same query, even given con- current updates. Availability All database clients will always be able to read and write data. Partition Tolerance The database can be split into multiple machines; it can continue functioning in the face of network segmentation breaks.
In distributed systems, however, it is very likely that you will have networkpartitioning, and that at some point, machines will fail and cause others to becomeunreachable. Packet loss, too, is nearly inevitable. This leads us to the conclusion thata distributed system must do its best to continue operating in the face of networkpartitions to be Partition-Tolerant , leaving us with only two real options to choosefrom: Availability and Consistency.
Figure illustrates visually that there is no overlapping segment where all three areobtainable. Figure However, I have modified the placement of some systemsbased on my research. Figure shows the general focus of some of the different databases we discuss in thischapter.
Note that placement of the databases in this chart could change based onconfiguration. Where different databases appear on the CAP continuumIn this depiction, relational databases are on the line between Consistency and Avail-ability, which means that they can fail in the event of a network failure including acable breaking.
These are more focused on Availability andPartition-Tolerance. InfoQ: Cassandra supports tunable consistency feature. Can you talk about this feature how it compares with strong and eventual consistency models?
Carpenter: Tuneable consistency is an extremely powerful feature which is not always well understood. For example, it is absolutely possible to achieve strong consistency in Cassandra, depending on how you use consistency levels on your reads and writes.
In this formula, where R and W are the number of nodes that will be read and written, as determined by the consistency level used. The most common way to achieve strong consistency is by using the QUORUM consistency level on both reads and writes, where a quorum is defined as one greater than half the number of nodes quorum is 2 with a replication factor of 3 nodes, 3 of 4 nodes, 3 of 5 nodes, and so on.
For example, to ingest sensor data as fast as possible in an IoT application, you might use the consistency level ONE or even ANY, which returns as soon as any node captures the write in its commit log. This might be perfectly acceptable, especially if your application is not reading the sensor data from Cassandra in real time. Because each individual invocation of a Cassandra query has its own consistency level, you have a lot of flexibility.
InfoQ: Does Cassandra support storing the image files in the database? What are the design considerations developers need to keep in mind when managing images and other binary data in Cassandra? Carpenter: Storage of binary data is definitely a use case which Cassandra supports. A key element to successfully storing binary data is making sure the file sizes do not get too large. A recommended design technique is to break large binary objects into chunks which can then be allocated to separate partitions.
Using a fixed chunk size keeps the partitions of equal size and helps ensure a balanced cluster. Whether this sort approach works well in your situation depends on the access patterns you need to support.
Then you can use Cassandra to store and search metadata about each binary file. InfoQ: What features does Cassandra provide in the areas of security and monitoring? To support monitoring at the cluster level, there are a couple of options.
However, the situation has really changed for the better over the past couple of years. Cassandra now supports authentication and encryption for client-node and node-node communications.
In addition, you can apply role based access at the keyspace or table level. File-level encryption for Apache Cassandra is a work in progress, as there are multiple file types to address SSTables, commit logs, hints, and indexes , but is a feature provided by DSE. InfoQ: Can you discuss the Cassandra cluster across multiple data centers and what are the advantages and limitations of Cassandra clusters?
Carpenter: One of the major advantages of Cassandra clusters is that distribution of data across multiple data centers is part of the core design, whereas other databases frequently rely on back-end replication mechanisms which are grafted on later.
Cassandra The Definitive Guide.pdf
That being said, the performance of operations such as repairs does require careful planning and tuning. It is important when building clusters across multiple data centers to consider the networking implications across multiple data centers when configuring timeouts. I would always recommend having a private, high speed connection between data centers if your budget allows.
InfoQ: What are some development tools that can help with the developer productivity when working on Cassandra based applications?
Carpenter: There are several tools that developers should have in their toolbox to help with data modeling, application development and testing. In my experience, most of the key make-or-break choices are made in your data model, before you ever write a line of application code. In terms of support for data modeling and experimenting with various approaches, I recommend DataStax DevCenter.
DevCenter is a free tool that allows you to design schemas and run queries on live clusters. It also traces all of your queries by default and provides a nice tabular report to help you analyze the traces. This is really important for helping to educate developers on what Cassandra is doing behind the scenes, and helps developers learn to avoid anti-patterns such as multi-partition queries that can lead to poor performance. It can also be helpful to get an idea of how your data models will perform at scale before you invest too much in a particular design.
I recommend using the Cassandra-stress tool that comes with Cassandra to generate a simulated read and write load. The caveat is that the tool does not yet support some of the more complex CQL elements such as User Defined Types UDTs , but you can still get useful results by working around this. Historically there were a number of Cassandra client drivers in various languages, developed by different authors and with different feature sets.
To help with testing, The Cassandra Cluster Manager ccm is a great tool implemented in Python that you can use to run a small cluster on your local machine with minimal setup. This is very useful for unit testing and for experimenting with different configuration settings. Spark is really gaining a lot of traction as a technology that can integrate data from a number of sources, including Cassandra and others.
We can implement some interesting real-time analytic jobs to learn about system behavior by fusing operational data with logging and metrics data. A corollary is that each service should have exclusive ownership over its data. There are various approaches to enforcing this ownership.
For example, large organizations such as Netflix have gone so far as to create a separate cluster per service, in cases where the scale makes sense. In cases where you have the need to coordinate changes across multiple data types, you can create additional microservices to compose the microservices that manage those data types, or use an asynchronous-style architecture to orchestrate the changes.
In terms of containerized deployments, I think there are some interesting challenges to overcome in terms of networking, but progress is being made. I tend to think of containerized Cassandra as more appropriate for development environments where you want to be able to bring up and tear down a cluster quickly, with perhaps a little bit of data loading. InfoQ: What are the features that are currently not available in Cassandra but you would like to see in the future releases?
I think the larger issue is the learning curve for configuring Cassandra and keeping it running. They actually recommended steering away from Cassandra, due to its operational complexity, unless you require the ability to scale to a hundred or more nodes.
For example, it would be helpful to have some wizards that can help guide the processes of configuring, monitoring and tuning.
The out of the box configuration is pretty sensible, but perhaps we can develop configuration templates for a wider range of common deployment patterns. I do see a lot of promising work in the community in the creation of open source tools to automate some of these complex operational tasks. For example, repair is an important part of how Cassandra maintains consistent data. Repair runs as a background task on a node and there several options for how to run it, which can be confusing to new users.
The Cassandra Reaper is a tool which automates repairs across a cluster.Where possible, I have tried to call out rel- evant differences, but you might be using a different version by the time you read this, and the implementation may have changed. The Definitive Guide provides the technical details and practical examples you need to assess this database management system and put it to work in a production environment. We appreciate, but do not require, attribution.
Designing Fine-Grained Systems. A keyspace contains the processing steps of the data replication and is similar to a schema in a relational database. Relational databases store invoices, customer records, prod-uct catalogues, accounting ledgers, user authentication schemes—the very world, itmight appear.