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Promising extra thorough and timelier facts evaluation, Hewlett-Packard has launched a software kit that combines the enterprise's Vertica database with its IDOL facts evaluation platform.
The HP Haven Connector Framework Server can also allow companies to analyze information units that were too huge or unwieldy to research before.
The equipment provides "a combination of statistical and contextual understanding," of information, said Jeff Veis, HP vice chairman of advertising and marketing for massive data. "that you may pull in any sort of facts, after which do real-time excessive performance analysis."
The Vertica Analytics Platform is columnar database application built for instantly examining tremendous amounts of structured statistics the usage of SQL (Structured query Language). IDOL (clever statistics working Layer) was built to index and search unstructured data within an commercial enterprise.
HP received each Vertica and IDOL in separate offers in 2011.
Integrating Vertica and IDOL opens up a variety of unstructured statistics sources that may also be scrutinized with Vertica's SQL-primarily based evaluation capabilities, Veis spoke of.
a firm could use the server application to research distinct statistics repositories, including data kept in enterprise programs comparable to Microsoft exchange, Microsoft SharePoint, Oracle and SAP business purposes, in addition to records within cloud services akin to box.web, Salesforce.com, and Google power.
advertising departments could use the software to determine trends, or to discover references to keywords of activity, throughout these disparate statistics sources, Veis mentioned.
The connector used to link IDOL with Vertica is one of 50 enhancements that the company has made to its Haven portfolio of data evaluation software and capabilities.
The business issued HP Haven capabilities Graphing, a device for knowing relationships and connections among numerous entities. The enterprise also has a brand new speech focus utility that makes use of neural networking to improve recognition through as much as 75 percent.
HP declined to display the rate of the HP Haven Connector Framework Server.
Bringing some enterprise rigor to the wild world of huge statistics, Hewlett-Packard has issued a kit for you to enable agencies to harness HP’s Vertica analytical database engine to investigate reams of unstructured information living in Hadoop programs.
“We’re marrying the better of both worlds,” pointed out Jeff Healey, director of product advertising and marketing for HP’s large facts platform products. “We’re bringing a proven SQL engine into the Hadoop infrastructure. that you can view and discover Hadoop facts with out getting to know an entire bunch of latest knowledge.”
The HP Vertica for SQL on Hadoop package can work with the Hortonworks, MapR and Cloudera Hadoop distributions and with simple Apache Hadoop methods that may additionally were developed in condo.
HP had discovered that about half of its Vertica consumer base were additionally drawn to the usage of Hadoop, Healey talked about. In effect, this kit will permit consumers to make use of Vertica as a front end for examining information stored in Hadoop deployments.
A column-oriented database gadget, HP Vertica Analytics Platform become designed to quickly execute tremendous-scale analysis jobs. it might probably field queries written in the commonplace SQL (Structured query Language) recognizable by means of most database administrators and third-party enterprise intelligence tools.
The Vertica engine “is awfully reputable and may supply the stability that commercial enterprise valued clientele are seeking for today,” referred to Ignacio Hwang, HP senior product supervisor.
part of the package is HP’s Flex Zone, which may also be used to discover unstructured facts with SQL devoid of first going in the course of the work of applying schemas to the information. shoppers could additionally use the package to import selected data from Hadoop for quicker evaluation within Vertica itself.
HP will face quite a lot of competition in offering SQL for Hadoop. other application that addresses this need contains the open-supply Apache Hive, Cloudera’s Impala (additionally open supply), IBM’s BigSQL and Pivotal’s Hawq, among others.
One early consumer of the know-how has been the Snagajob employment carrier, which makes use of the utility to give a way for its million daily net friends to sort throughout the 400,000 jobs listings that the company automatically maintains posted.
The HP Vertica Analytics Platform is obtainable now. The enterprise did not disclose selected pricing apart from to assert it will be priced on a per-node groundwork.To comment on this text and other PCWorld content, seek advice from our fb web page or our Twitter feed.
Tandem computers Inc. these days introduced a brand new data base management gadget providing the capacity to function from diverse terminals in a dispensed mode. Tandem stated the new system, called nonstop Structured query Language, is each easy to use and faster than general relational information base systems.
E.J. Codd, who developed the primary relational data base for the international business Machines service provider within the 1960's and is now an independent consultant, noted the introduction changed into a ''dramatic'' experience that could put Tandem ''years ahead of the competition.''
In a simulated customer-use check audited by the Codd and Date Consulting community, the Tandem gadget handled more than 200 debit/credit transactions a 2d on 32 tandem processors. A related I.B.M. product accurately yields 40 transactions a 2d, prompting Tandem to claim a value to performance growth of five instances. complete hardware, application and maintenance prices of the brand new device can be about $eleven million for five years.proceed analyzing the main story
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Today, Structured Query Language is the standard means of manipulating and querying data in relational databases, though with proprietary extensions among the products. The ease and ubiquity of SQL have even led the creators of many “NoSQL” or non-relational data stores, such as Hadoop, to adopt subsets of SQL or come up with their own SQL-like query languages.
But SQL wasn’t always the “universal” language for relational databases. From the beginning (circa 1980), SQL had certain strikes against it. Many researchers and developers at the time, including me, thought that the overhead of SQL would keep it from ever being practical in a production database.
Clearly, we were wrong. But many still believe that, for all of SQL’s ease and accessibility, the price exacted in runtime performance is often too high.Before SQL
Before there was SQL, databases had tight, navigational programming interfaces, and typically were designed around a network schema called the Codasyl data model. Codasyl (Committee on Data Systems Languages) was a consortium that was responsible for the Cobol programming language (starting in 1959) and database language extensions (starting 10 years later).
When you programmed against a Codasyl database, you were navigating to records through sets, which express one-to-many relationships. Older hierarchical databases only allow a record to belong to one set. Network databases allow a record to belong to multiple sets.
Say you wanted to list the students enrolled in CS 101. First you would find "CS 101" in the Courses set by name, set that as the owner or parent of the Enrollees set, find the first member (ffm) of the Enrollees set, which is a Student record, and list it. Then you would go into a loop: Find next member (fnm) and list it. When fnm failed, you would exit the loop.
That may seem like a lot of scut work for the database programmer, but it was very efficient at execution time. Experts like Michael Stonebraker of University of California at Berkeley and Ingres pointed out that doing that sort of query in a Codasyl database such as IDMS took roughly half the CPU time and less than half the memory as the same query on a relational database using SQL.
For comparison, the equivalent SQL query to return all of the students in CS 101 would be something likeSELECT student.name FROM courses, enrollees, students WHERE course.name ="CS 101"
That syntax implies a relational inner join (actually two of them), as I’ll explain below, and leaves out some important details, such as the fields used for the joins.Relational databases and SQL
Why would you give up a doubling of execution speed and memory? There were two big reasons: ease of development and portability. I didn’t think either one mattered much in 1980 compared to performance and memory requirements, but as computer hardware improved and became cheaper people stopped caring about execution speed and memory and worried more about the cost of development.
In other words, Moore’s Law killed Codasyl databases in favor of relational databases. As it happened, the improvement in development time was significant, but SQL portability turned out to be a pipe dream.
Where did the relational model and SQL come from? E.F. “Ted” Codd was a computer scientist at the IBM San Jose Research Laboratory who worked out the theory of the relational model in the 1960s and published it in 1970. IBM was slow to implement a relational database in an effort to protect the revenues of its Codasyl database IMS/DB. When IBM finally started its System R project, the development team (Don Chamberlin and Ray Boyce) wasn’t under Codd, and they ignored Codd’s 1971 Alpha relational language paper to design their own language, Sequel (Structured English Query Language). In 1979, before IBM had even released its product, Larry Ellison incorporated the language in his Oracle Database (using IBM’s pre-launch Sequel publications as his spec). Sequel soon became SQL to avoid an international trademark violation.
The “tom-toms beating for SQL” (as Michael Stonebraker put it) were coming not only from Oracle and IBM, but also from customers. It wasn’t easy to hire or train Codasyl database designers and programmers, so Sequel (and SQL) looked much more attractive. SQL was so attractive in the later 1980s that many database vendors essentially stapled a SQL query processor on top of their Codasyl databases, to the great dismay of Codd, who felt that relational databases had to be designed from scratch to be relational.
A pure relational database, as designed by Codd, is built on tuples grouped into relations, consistent with first-order predicate logic. Real-world relational databases have tables that contain fields, constraints, and triggers, and tables are related through foreign keys. SQL is used to declare the data to be returned, and a SQL query processor and query optimizer turn the SQL declaration into a query plan that is executed by the database engine.
SQL includes a sub-language for defining schemas, the data definition language (DDL), along with a sub-language for modifying data, the data manipulation language (DML). Both of these have roots in early Codasyl specifications. The third sub-language in SQL declares queries, through the SELECT statement and relational joins.The SELECT statement in SQL
The SELECT statement tells the query optimizer what data to return, what tables to look in, what relations to follow, and what order to impose on the returned data. The query optimizer has to figure out by itself what indexes to use to avoid brute force table scans and achieve good query performance, unless the particular database supports index hints.
Part of the art of relational database design hangs on the judicious use of indexes. If you omit an index for a frequent query, the whole database can slow down under heavy read loads. If you have too many indexes, the whole database can slow down under heavy write and update loads.
Another important art is choosing a good, unique primary key for every table. You not only have to consider the impact of the primary key on common queries, but how it will play in joins when it appears as a foreign key in another table, and how it will affect the data’s locality of reference.
In the advanced case of database tables that are split up into different volumes depending on the value of the primary key, called horizontal sharding, you also have to consider how the primary key will affect the sharding. Hint: You want the table distributed evenly across volumes, which suggests that you don’t want to use date stamps or consecutive integers as primary keys.
Discussions of the SELECT statement may start simple, but can quickly become confusing. Consider:SELECT * FROM Customers;
Simple, right? It asks for all fields and all rows of the Customers table. Suppose, however, that the Customers table has a hundred million rows and a hundred fields, and one of the fields is a large text field for comments. How long will it take to pull down all that data over a 10 megabit per second network connection if each row contains an average of 1 kilobyte of data?
Perhaps you should cut down how much you send over the wire. Consider:SELECT TOP 100 companyName, lastSaleDate, lastSaleAmount, totalSalesAmount FROM CustomersWHERE state ="Ohio" AND city ="Cleveland"ORDER BY lastSaleDate DESCENDING;
Now you’re going to pull down a lot less data. You have asked the database to give you only four fields, to only consider the companies in Cleveland, and to give you just the 100 companies with the most recent sales. To do that most efficiently at the database server, however, the Customers table needs an index on state+city for the WHERE clause and an index on lastSaleDate for the ORDER BY and TOP 100 clauses.
By the way, TOP 100 is valid for SQL Server and SQL Azure, but not MySQL or Oracle. In MySQL, you’d use LIMIT 100 after the WHERE clause. In Oracle, you’d use a bound on ROWNUM as part of the WHERE clause, i.e. WHERE... AND ROWNUM <=100. Unfortunately, the ANSI/ISO SQL standards (and there are nine of them to date, stretching from 1986 to 2016) only go so far, beyond which each database introduces its own proprietary clauses and features.Relational joins in SQL
So far, I’ve described the SELECT syntax for single tables. Before I can explain JOIN clauses, you need to understand foreign keys and relations between tables. I’ll explain this by using examples in DDL, using SQL Server syntax.
The short version of this is fairly simple. Every table that you want to use in relations should have a primary key constraint; this can either be a single field or a combination of fields defined by an expression. For example:CREATE TABLE Persons (PersonID int NOT NULL PRIMARY KEY,PersonName char(80),...
Every table that needs to relate to Persons should have a field that corresponds to the Persons primary key, and to preserve relational integrity that field should have a foreign key constraint. For example:CREATE TABLE Orders (OrderID int NOT NULL PRIMARY KEY,...PersonID int FOREIGN KEY REFERENCES Persons(PersonID));
There are longer versions of both statements that use the CONSTRAINT keyword, which lets you name the constraint. That’s what most database design tools generate.
Primary keys are always indexed and unique (the field values cannot be duplicated). Other fields can optionally be indexed. It’s often useful to create indexes for foreign key fields and for fields that appear in WHERE and ORDER BY clauses, although not always because of the potential overhead from writes and updates.
How would you write a query that returns all of the orders placed by John Doe?SELECT PersonName, OrderID FROM PersonsINNER JOIN Orders ON Persons.PersonID = Orders.PersonIDWHERE PersonName ="John Doe";
In fact, there are four kinds of JOIN: INNER, OUTER, LEFT, and RIGHT. The INNER JOIN is the default (you can omit the word INNER), and it’s the one that includes only rows that contain matching values in both tables. If you want to list persons whether or not they have orders, you’d use a LEFT JOIN, for example:SELECT PersonName, OrderID FROM PersonsLEFT JOIN Orders ON Persons.PersonID = Orders.PersonIDORDER BY PersonName;
When you start doing queries that join more than two tables, use expressions, or coerce data types, the syntax can get a little hairy at first. Fortunately, there are database development tools that can generate correct SQL queries for you, often by dragging and dropping tables and fields from the schema diagram into a query diagram.SQL stored procedures
Sometimes the declarative nature of the SELECT statement doesn’t get you where you want to go. Most databases have a facility called stored procedures; unfortunately this is an area where the databases pretty much all use proprietary extensions to the ANSI/ISO SQL standards.
In SQL Server, the initial dialect for stored procedures (or stored procs) was Transact-SQL, aka T-SQL; in Oracle, it was PL-SQL. Both databases have added additional languages for stored procedures, such as C#, Java, and R. A simple T-SQL stored procedure might only be a parameterized version of a SELECT statement. Its advantages are ease of use and efficiency. Stored procedures are optimized when they are saved, not every time they are executed.
A more complicated T-SQL stored procedure might use multiple SQL statements, input and output parameters, local variables, BEGIN...END blocks, IF...THEN...ELSE conditions, cursors (row-by-row processing of a set), expressions, temporary tables, and a whole host of other procedural syntax. Obviously if the stored procedure language is C#, Java, or R, you’re going to use the functions and syntax of those procedural languages. In other words, despite the fact that the motivation for SQL was to use standardized declarative queries, in the real world you see lots of database-specific procedural server programming.
That doesn’t quite take us back to the bad old days of Codasyl database programming (although cursors come close), but it does walk back from the ideas that SQL statements should be standardized and that performance concerns should be left to the database query optimizer. In the end, a doubling of performance is often too much to leave on the table.
Structured Query Language (SQL) is a programming language designed for database management and manipulation – primarily used to find and pull information from large databases.
SQL is normally displayed on relational databases, a form of database derived from Edgar F. Codd's relational model of storing information in way that makes it accessible and identifiable in relation to other information in it.© iStock
The language was developed at IBM by Donald Chamberlin and Raymond Boyce in 1974, where it was initially named SEQUEL (Structured English Query Language), building on Edgar F. Codd’s relational model.
SQL contains four main categories, with an additional two commands that are mostly used for computer databases, and has since been integrated with more features after becoming a standard of the American National Standards Institute (ANSI) in 1986 and International Organisation for Standardisation (ISO) in 1987.
SQL is a fourth-generation language, meaning commands are close to human language. By contrast, the Java and C++ programming languages are third-generation languages that are more abstract to the untrained eye.Read next: The best NoSQL database options for the enterprise
SQL has become the industry standard for manipulating and querying data in a relational database. The uses of the language are broken into three other components aside from writing queries. These are:
Data Manipulation Language (DML), which is used to manipulate database content in retrieve, update, delete and insert modes. The DML has four commands that can be used to manipulate the information which is the Select, Insert, Update and Delete commands.
Data Definition Language (DDL) is used to manage tables and index structures, also with synchronised commands that can be used to create, alter and delete database tables.
Data Control Language (DCL) is often used to manage who can access the database, providing rights to certain users. The two commands that come with this are Grant and Revoke, which are the two core commands of relational database security and authentication.
Data Query Language (DQL) is similar to DML, as it is used to select, insert, update and delete data from the database. The two can be used together.
The other SQL commands are data administration and transactional control commands.
The commands, also known as statements, can be used for different processes such as control of transactions, connections, and diagnostics. With this, the database server processes the inputted command and responds to the user’s command.'+article.headline +''; listEl.prepend('
WASHINGTON — The Mueller Report confirms the Russians tried to hack the Illinois Board of Elections website in 2016.
“In one instance in approximately June 2016, the GRU compromised the computer network of the Illinois State Board of Elections by exploiting a vulnerability in the SBOE’s website. The GRU then gained access to a database containing information on millions of registered Illinois voters and extracted data related to thousands of U.S. voters before the malicious activity was identified,” the report states.
This was part of an effort of the Russian intelligence agency — the GRU — to determine “vulnerabilities” on websites of more than two dozen states, including Illinois.
READ IT HERE: Redacted version of special counsel’s report
The Chicago Sun-Times reported on the hacking attempt in 2017.
The hack had nothing to do with counting votes in elections in Illinois. The hackers looked at voting registration data: name, address, date of birth, gender and the last four digits in the Social Security number. In all, hackers searched through about 80,000 records, with the elections board confirming the records of just under 3,000 voters were viewed by the hackers.
A report on the breach prepared by the Illinois State Board of Elections on Aug. 26, 2016, and obtained by the Sun-Times, details how the hackers were detected by state board information technology staffers.
“Processor usage had spiked to 100% with no explanation,” said the report, with an analysis of server logs showing the “heavy load” was “malicious in nature,” and aimed at the online voter application website.
The particular form of the Illinois cyberattack was a “SQL injection” — as in Structured Query Language — where malicious code can be planted.
“We saw data being downloaded, and it was going to a place where it shouldn’t have been going, so we shut them down,” Ken Menzel, general counsel for the state elections board, told the Sun-Times in 2017.
“We had something that wasn’t properly battened down in one of the fields on the on-line portion of the website,” Menzel said then. That was “a mistake on our end.”
The data was going to a server in the Netherlands — not one of the 109 separate election jurisdictions in the state of Illinois.
Server logs showed the cyberattack began June 23, 2016, with malicious SQL inquiries. Once detected the attack was detected, the staff put in code changes on July 12, 2016.
On July 13, the website was taken offline. On July 19, the Illinois General Assembly and the Illinois Attorney General were notified. The AG’s office notified the FBI, which started an investigation in cooperation with the Department of Homeland Security. On July 21, the site was coming back online.
Attackers continued to hit state board IP addresses five times per second until Aug. 12, 2016, Menzel told the Sun-Times in 2017. That’s when attacks abruptly ceased.
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