Re: H-Base Auto-onderdelen Database Crack
Agathe Thies
Apache HBase describes itself as "the Hadoop database," which can be a bit confusing, as Hadoop is typically understood to refer to the popular MapReduce processing framework. But Hadoop is really an umbrella name for an entire ecosystem of technologies, some of which HBase uses to create a distributed, column-oriented database built on the same principles as Google's Bigtable. HBase does not use Hadoop's MapReduce capabilities directly, though HBase can integrate with Hadoop to serve as a source or destination of MapReduce jobs.
The hallmarks of HBase are extreme scalability, high reliability, and the schema flexibility you get from a column-oriented database. While tables and column families must be defined in advance, you can add new columns on the fly. HBase also offers strong row-level consistency, built-in versioning, and "coprocessors" that provide the equivalents of triggers and stored procedures.
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HDFS -- the Hadoop Distributed File System -- is the Hadoop ecosystem's foundation, and it's the file system atop which HBase resides. Designed to run on commodity hardware and tolerate member node failures, HDFS works best for batch processing systems that prefer streamed access to large data sets. This seems to make it inappropriate for the random access one would expect in database systems like HBase. But HBase takes steps to compensate for HDFS's otherwise incongruous behavior.
More precisely, a row is a collection of key/value pairs, the key being a column identifier and the value being the content of the cell that exists at the intersection of a specific row and column. However, because HBase is a column-oriented database, no two rows in a table need have the same columns. To complicate matters further, data is versioned in HBase. The actual coordinates of a value (cell) is the tuple row key, column key, timestamp. In addition, columns can be grouped into column families, which give a database designer further control over access characteristics, as all columns within a column family will be stored in close proximity to one another.
Working with HBase
HDFS was designed on the principle that it is easier to move computation (as in a MapReduce operation) close to the data being processed than it is to move the data close to the computation. As a result, it is not in HDFS's nature to ensure that related pieces of data (say, rows in a database) are co-located. This means it's possible that a block whose data is managed by a particular RegionServer will not be stored on the same physical host as that RegionServer. However, HDFS provides mechanisms that advertise block location and -- more important -- perform block relocation upon request. HBase uses these mechanisms to move blocks so that that they are local to their owning RegionServer.
The HBase reference guide includes a Getting Started guide and an FAQ. It's a live document, so you'll find user community comments attached to each entry. The HBase website also provides links to the HBase Java API, as well as to videos and off-site sources of HBase information. More information can be found in the HBase wiki. While good, the HBase documentation is not quite on par with documentation I've seen on other database product sites, such as Cassandra and MongoDB. Nevertheless, there's plenty of material around the Internet, and the HBase community is large and active enough that any HBase questions won't go unanswered for long.
HBase is very much a developer-centric database. Its online reference guide is heavily linked into HBase's Java API docs. If you want to understand the role played by a particular HBase entity -- say, a Filter -- be prepared to be handed off to the Java API's documentation of the Filter class for a full explanation.
Given that access is by row and that rows are indexed by row keys, it follows that careful design of row key structure is critical for good performance. Ironically, programmers in the good old days of ISAM (Indexed Sequential Access Method) databases knew this well: Database access was all about the components -- and the ordering of those components -- in compound-key indexes.
HBase employs a collection of battle-tested technologies from the Hadoop world, and it's well worth consideration when building a large, scalable, highly available, distributed database, particularly for those applications where strong consistency is important.
In versions 3.x and before, Kylin has been using HBase as a storage engine to save the precomputing results generated after cube builds. HBase, as the database of HDFS, has been excellent in query performance, but it still has the following disadvantages:
Now let's talk about how we can provide the utmost experience. In risk control and recommendation scenarios, the lower the request response time (RT), the more rules the service can apply in a unit of time, and the more accurate the analysis. The storage engine must be able to operate with high concurrency, low latency, and fewer glitches, as well as high speed and stability. On the kernel of Ali-HBase, the team developed CCSMAP to optimize write cache, SharedBucketCache to optimize read cache, and IndexEncoding to optimize in-block search. In addition, by combining other technologies such as lock-free queue, coroutine, and ThreadLocal Counter, and the ZGC algorithm developed by the Alibaba JDK team, Ali-HBase achieves a single cluster P999 latency of less than 15 ms online. From another perspective, strong consistency is not required in risk control and recommendation scenarios. With some data being read-only data imported offline, reading multiple replicas is acceptable as long as the latency is low. If the request glitches between the primary and secondary replicas is an independent event, then theoretically speaking, accessing primary and secondary replicas at the same time can lower the glitch rate by an order of magnitude. Based on this, we develop DualService by taking advantage of the existing primary/secondary architecture, which supports concurrent access from clients to the primary cluster and secondary cluster. Generally, the client starts by reading from the primary database. If the primary database does not respond for a certain period of time, the client sends a concurrent request to the secondary database and waits for the first response. The application of DualService is a great success, with the service operating with nearly zero jitter.
Before we start looking into all the moving parts of HBase, let us pause to think about why there was a need to come up with yet another storage architecture. Relational database management systems (RDBMSes) have been around since the early 1970s, and have helped countless companies and organizations to implement their solution to given problems. And they are equally helpful today. There are many use cases for which the relational model makes perfect sense. Yet there also seem to be specific problems that do not fit this model very well.[5]
The reason to store values on a per-column basis instead is based on the assumption that, for specific queries, not all of the values are needed. This is often the case in analytical databases in particular, and therefore they are good candidates for this different storage schema.
Note, though, that HBase is not a column-oriented database in the typical RDBMS sense, but utilizes an on-disk column storage format. This is also where the majority of similarities end, because although HBase stores data on disk in a column-oriented format, it is distinctly different from traditional columnar databases: whereas columnar databases excel at providing real-time analytical access to data, HBase excels at providing key-based access to a specific cell of data, or a sequential range of cells.
RDBMSes have typically played (and, for the foreseeable future at least, will play) an integral role when designing and implementing business applications. As soon as you have to retain information about your users, products, sessions, orders, and so on, you are typically going to use some storage backend providing a persistence layer for the frontend application server. This works well for a limited number of records, but with the dramatic increase of data being retained, some of the architectural implementation details of common database systems show signs of weakness.
The relational database model normalizes the data into a user table, which is accompanied by a url, shorturl, and click table that link to the former by means of a foreign key. The tables also have indexes so that you can look up URLs by their short ID, or the users by their username. If you need to find all the shortened URLs for a particular list of customers, you could run an SQL JOIN over both tables to get a comprehensive list of URLs for each customer that contains not just the shortened URL but also the customer details you need.
In addition, you are making use of built-in features of the database: for example, stored procedures, which allow you to consistently update data from multiple clients while the database system guarantees that there is always coherent data stored in the various tables.
This usually works very well and will serve its purpose for quite some time. If you are lucky, you may be the next hot topic on the Internet, with more and more users joining your site every day. As your user numbers grow, you start to experience an increasing amount of pressure on your shared database server. Adding more application servers is relatively easy, as they share their state only with the central database. Your CPU and I/O load goes up and you start to wonder how long you can sustain this growth rate.
The first step to ease the pressure is to add slave database servers that are used to being read from in parallel. You still have a single master, but that is now only taking writes, and those are much fewer compared to the many reads your website users generate. But what if that starts to fail as well, or slows down as your user count steadily increases?
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