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Oracle Oracle Database 11g Release

Oracle Rolls Out Oracle NoSQL Database service | killexams.com true Questions and Pass4sure dumps

Press release

Redwood Shores, Calif.—Oct 2, 2018

Oracle these days announced the supply of Oracle NoSQL Database, the newest addition to the Oracle Database portfolio. Designed for workloads desiring speedy and predictable responses to yardstick operations, the self-driving database service permits developers to effortlessly install big-scale purposes, including UI personalization, looking carts, online fraud detection, gaming, and promoting. using innovative computing device gaining lore of and automation capabilities, NoSQL Database provides a greatly extra reputable NoSQL database with ninety nine.95 % availability and is as much as 70 percent diminish can imbue than Amazon DynamoDB1.

The wholly managed NoSQL Database handles NoSQL purposes that require low latency, records model flexibility, and elastic scaling. the usage of primary APIs, builders can focal point on software development without needing to agonize about managing servers, storage expansion, cluster deployments, utility setting up, or backup. developers can without hardship specify the throughput and capability that they want to provision, and materials are allotted and scaled for this judgement to fulfill dynamic workload necessities.

“We continue to leverage their progressive autonomous capabilities to radically change the database market," spoke of Andrew Mendelsohn, government vice president, Oracle Database. "Our latest self-driving database cloud service, NoSQL Database, provides immoderate reliability and efficiency at very low costs to achieve a incredibly bendy utility structure framework.”

Oracle self sustaining Database presents extremely top-notch performance whereas assisting in the reduction of risk and charges for organizations. operating on Oracle Cloud Infrastructure, the self sustaining database portfolio is self-using, self-securing, and self-repairing to automate key management tactics including patching, tuning and upgrading to maintain critical infrastructure automatically working. NoSQL joins Oracle independent Transaction Processing and Oracle self sufficient information Warehouse, which grew to become purchasable earlier this yr. each database cloud service is tuned to a selected workload.

NoSQL Database offers a wealth of facets to meet the needs of today’s builders:

  • contemporary: A developer-oriented solution, NoSQL Database is designed for flexibility. The database helps key value APIs together with basic declarative SQL API and command line interfaces along with elastic facts models for records illustration including relational and advert-hoc JSON.

  • Open: The provider gives a non-proprietary SQL language, supplying innovative interoperability between common relational and traditional JSON information fashions. clients even acquire deployment options to elope the identical software in the cloud or on-premises with out a platform lock-in.

  • easy: With an attainable SDK and lead for ordinary languages including Python, Node.JS and Java, Oracle offers a no hassle software structure solution to quite simply hook up with NoSQL Database.

  • 1 imbue reference is for study-most efficacious workloads.

    Contact info About Oracle

    The Oracle Cloud offers complete SaaS software suites for ERP, HCM and CX, plus ideal-in-type database Platform as a service (PaaS) and Infrastructure as a carrier (IaaS) from information facilities any over the Americas, Europe and Asia. For greater advice about Oracle (NYSE:ORCL), gladden visit us at www.oracle.com.

    logos

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    Python cx_Oracle 7 Introduces SODA doc Storage | killexams.com true Questions and Pass4sure dumps

    MariaDB TX, confirmed in production and driven via the neighborhood, is an entire database retort for any and every business — a latest database for contemporaneous functions.

    cx_Oracle 7.0, the extremely established Oracle Database interface for Python, is now construction on PyPI.

    cx_Oracle is an open source package that covers the Python Database API specification with many additions to back Oracle superior elements.

    Anthony Tuininga has just released cx_Oracle 7.0. This release brings some key technologies and new features to the Python developer:

  • Oracle simple doc entry (SODA) aid - an exciting addition to the yardstick relational access mannequin is the brand new set of APIs for Oracle SODA. observe beneath.
  • delivered Connection.callTimeout to back call timeouts when cx_Oracle is the usage of Oracle client 18.1 and better. here is a efficacious backstop to stay away from out-of-control SQL and PL/SQL statement execution.The main code layer below cx_Oracle's implementation is Oracle title Interface. This API handles any of the network connectivity to Oracle Database. For each OCI duty accomplished by means of cx_Oracle, zero or more 'round-journeys' to the database can ensue - calling the database and getting a response returned.The callTimeout expense applies to each round-commute in my view, no longer to the sum of any round-journeys. Time spent processing in cx_Oracle before or after the completion of each and every round-go back and forth is not counted.
  • If the time from the birth of anybody circular-trip to the completion of that very circular-trip exceeds callTimeout milliseconds, then the operation is halted and an mistake is back.
  • in the case the set a cx_Oracle operation requires a pair of round-travel and each round-trip takes under callTimeout milliseconds, then no timeout will ensue, even though the sum of any circular-trip calls exceeds callTimeout.
  • If no circular-go back and forth is required, the operation will under no circumstances be interrupted.
  • After a timeout is triggered, cx_Oracle attempts to spotless up the inside connection state. The cleanup is allowed to rob yet another callTimeout milliseconds. If the cleanup changed into a success, a DPI-1067 mistake should be returned and the utility can continue to expend the connection. For minute values of callTimeout, the connection cleanup may additionally no longer comprehensive efficiently in the extra callTimeout length. in this case, an ORA-3114 is lower back and the connection will no longer be usable. it would be closed.
  • delivered assist for closing a session pool by means of the feature SessionPool.close(). here's valuable for being 'first-class' to the database and making inescapable that database periods aren't left dangling unless the database cleans them up. In specific, the non-compulsory 'drive' controversy is smooth should you deserve to unexpectedly halt a Python application and immediately free any of the sessions in the database.
  • added wait on for getting the contents of a SQL assortment expostulate as a dictionary, the set the keys are the indices of the assortment and the values are the facets of the assortment. observe characteristic Object.asdict().
  • On windows, cx_Oracle will now try to load the Oracle customer libraries from the equal directory because the cx_Oracle module earlier than doing the traditional home windows library location search, e.g. within the directories within the path environment variable. This new duty may be valuable if you're bundling up functions and wish to include the Oracle quick client. by means of inserting the client in the very listing as the cx_Oracle library there is not any exigency to set route, no deserve to worry about clients altering route, and no should be troubled about having multiple models of Oracle client libraries in course.
  • a change in cx_Oracle 7 is that after a DML RETURNING statement is achieved, variables bound to it's going to recur an array when calling Variable.getvalue(). makes an attempt to set cx_Oracle.__future__.dml_ret_array_val at the jiffy are ignored.
  • When a connection is used as a context manager, the connection is now closed when the shroud ends. makes an attempt to set cx_Oracle.__future__.ctx_mgr_close are now overlooked.
  • the complete unencumber notes exhibit the other new points and changes. assessment this list earlier than you improve:

    python -m pip set up cx_Oracle --upgrade SODA in Python cx_Oracle

    Oracle essential document entry (SODA) lead was firstly brought in Java and these days exposed to C. Python assist for SODA is now attainable in cx_Oracle 7 when using Oracle client 18.three libraries and connecting to Oracle Database 18.1 or better. SODA is any sizzling and new and below swift construction. For this cx_Oracle free up, we're labeling SODA assist as a "preview." With a future edition of the Oracle client libraries, this may alternate.

    SODA is customarily used to store JSON documents in Oracle Database however has the pliability to be substantive you can shop different sorts of content material.

    as soon as a DBA has granted you the SODA_APP privilege, which you can simply create collections and shop documents in them. Some basic examples are:

    p9;

    # Create the dad or mum expostulate for SODA soda = connection.getSodaDatabase() # Create a new SODA assortment # this could open an present collection, if the identify is already in use. assortment = soda.createCollection("mycollection") # Insert a document # A tackle generated key is created with the aid of default. content material = 'identify': 'Matilda', 'address': 'city': 'Melbourne' doc = collection.insertOneAndGet(content material) key = doc.key print('the valuable thing of the brand new SODA doc is: ', key)

    that you would be able to then collect files again by the expend of a key search for or by using a search. A key recognize up is simple:

    # Fetch the doc lower back doc = assortment.find().key(key).getOne() # A SodaDocument content material = doc.getContent() # A JavaScript itemprint('Retrieved SODA doc dictionary is:') print(content material)

    For documents that can be converted to JSON, that you would be able to alternatively collect them as string:

    content material = doc.getContentAsString() # A JSON string print('Retrieved SODA document string is:') print(content)

    The find() system is an operation builder, with strategies that permit modern filtering standards to be set, limiting the set of files which are then operated on by way of a terminal formulation similar to getOne(), getDocuments() and weigh number().

    With JSON documents, an entire filtering specification language will also be used to pattern match documents. a brief specimen is:

    # find any files with names fondness 'Ma%' print("Names matching 'Ma%'") files = assortment.find().filter('identify': '$like': 'Ma%').getDocuments() for d in files: content material = d.getContent() print(content["name"])

    A runnable instance is in SodaBasic.py

    check out the cx_Oracle SODA manual and the Introduction to fundamental Oracle document access (SODA) manual to recognize its vim and ease.

    try it out!

    cx_Oracle References

    Homepage: oracle.github.io/python-cx_Oracle/index.html

    installation guidelines: cx-oracle.readthedocs.io/en/newest/installation.html

    Documentation: cx-oracle.readthedocs.io/en/latest/index.html

    liberate Notes: cx-oracle.readthedocs.io/en/latest/releasenotes.html

    supply Code Repository: github.com/oracle/python-cx_Oracle

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    Oracle adds a NoSQL database to its self sustaining lineup | killexams.com true Questions and Pass4sure dumps

    Oracle Corp. is increasing its self reliant database lineup with the liberate of the Oracle self reliant NoSQL Database.

    introduced Tuesday, the brand new database is being provided as a fully managed provider that’s optimized for NoSQL applications that require lessen latency, information model flexibility and elastic scaling, the business talked about. these types of applications consist of advertising, gaming, online fraud detection and looking carts.

    NoSQL databases are designed to accommodate a big altenative of information models, including key-value, document, columnar and graph codecs. NoSQL, which basically stands for “not most efficacious SQL,” is an alternative to average relational databases in which data is placed in tables and information schema is cautiously designed before the database is built. NoSQL databases are specifically constructive for working with massive units of allotted facts.

    The autonomous capabilities of Oracle’s new NoSQL database means developers can easily specify the potential and throughput they exigency to provision, and notice elements allotted and scaled instantly in order to meet their workload necessities.

    Oracle announced its first self sustaining database providing, the 18c independent Database, ultimate yr, describing it as a “self-riding” database that’s capable of ply initiatives equivalent to updates and application patches immediately to carve back the tall cost of human labor. That database, which can also be provisioned, accomplish upgrades and excellent tuning and patch itself while running, turned into firstly made obtainable for facts warehousing initiatives and later for on-line transaction processing.

    Oracle Corp. government Chairman and Chief technology Officer Larry Ellison (pictured) has up to now referred to the business’s self sustaining databases are “probably the most essential component they now acquire ever accomplished,” critical to its plans to tackle public cloud computing leader Amazon web capabilities Inc.

    So it’s no amaze the business is launching an self sufficient NoSQL database to tackle Amazon’s DynamoDB database, which is an analogous product. It’s also now not astounding that Oracle said its product is advanced, with claims that the Oracle self sustaining NoSQL Database expenses 70 % under DynamoDB for read-most efficacious workloads and provides enhanced reliability than its rival.

    “NoSQL remains the database of alternative powering next-generation applications within the business, so for Oracle to lengthen its self using capabilities to its new NoSQL providing is vital step for CxOs deployment options,” referred to Holger Mueller, valuable analyst and vice president of Constellation analysis Inc. “as with any new choices, it’s respectable to observe them popping out of the gate, however they should be validated by way of early adopters. nevertheless, the self sustaining capabilities of the Oracle stack continue to be a really enjoyable satisfactory for CxOs”

    The Oracle self reliant NoSQL Database boasts a complete latitude of performance, together with assist for key expense software programming interfaces and command line interfaces. it could actually additionally deal with bendy records fashions for records illustration and offers a yardstick SQL language to deliver interoperability for relational and JSON statistics models.

    “We continue to leverage their progressive self sufficient capabilities to transform the database market,” spoke of Andrew Mendelsohn, government vice chairman of Oracle Database. “Our newest self-riding database cloud provider, Oracle self sufficient NoSQL Database, offers vehement reliability and performance at very low charges to achieve a incredibly bendy application construction framework.”

    image: Oracle considering you’re here …

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    Oracle Releases Autonomous NoSQL Database Service | killexams.com true questions and Pass4sure dumps

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    Company takes the fight to market leader Amazon’s DynamoDB

    Oracle today announced the generic availability of its “self-driving” Autonomous NoSQL Database, the newest addition to the Oracle Autonomous Database portfolio, which was released with much fanfare late last year.

    The company said that the database uses machine learning and automation capabilities to deliver a NoSQL database with 99.995 percent availability: it claims this can be delivered at up to 70 percent lower cost than vie Amazon’s DynamoDB.

    See also: Oracle Releases 1500 APIs

    It supports key value APIs including simple declarative SQL API and command line interfaces along with elastic data models for data representation including relational and ad-hoc JSON, Oracle said, and is “self-driving, self-securing, and self-repairing”.

    That means it can automate key management processes including patching, tuning and upgrading to maintain critical infrastructure automatically running.

    Developers using the fully managed service can specify the throughput and capacity that they wish to provision, and resources are allocated and scaled accordingly to meet dynamic workload requirements.

    Oracle Autonomous Database Portfolio Growing

    It comes eight weeks after Oracle CEO LArry Ellison at Oracle headquarters to unveil the Oracle Autonomous Transaction Processing Cloud, which has been designed to ply intricate sets of high-performance transactions as well as mixed workloads, e.g. batch processing, reporting, Internet of Things data, etc.

    That was the second of Oracle’s cloud-based autonomous databases, with the first, Oracle Autonomous Data Warehouse, optimised for analytics.

    Autonomous NoSQL Database

    That autonomous capability enables lower operating costs, improved security and reliability, lower IT labor costs, and gives database administrators more time to focus on new ideas instead of tuning databases and allocating storage, Oracle claims.

    Oracle said in a release that the database “enables developers to easily deploy massive-scale applications, including UI personalization, shopping carts, online fraud detection, gaming, and advertising.”

    The service provides a non-proprietary SQL language, delivering interoperability between yardstick relational and yardstick JSON data models. Users also acquire deployment options to elope the very application in the cloud or on-premises with no platform lock-in, Oracle said. It comes with its own SDK and back for languages including Python, Node.JS and Java.


    Oracle Database Cloud - Database as a Service Quick Start | killexams.com true questions and Pass4sure dumps

    Use the DBaaS Monitor to monitor your Oracle Database Cloud - Database as a Service, and the database instances in it. You connect to the Monitor via Port 443 of your DBaaS VM. You opened this port in a previous section of the tutorial, ''Enabling Secure Network Access to your Database Instance".

  • From the My Services console, recur to the Database Cloud Service console.Click the menu next to the database instance you wish to monitor, and note the variety of console choices.Click Open DBaaS Monitor Console.

    open dbaas monitor consoleDescription of this image
  • If your browser warns that your connection is not private, click Advanced, and then click the link to proceed to the Public IP address shown. The log in dialog appears. Enter dbaas_monitor as the user name. For the password, expend whatever password was specified when your database instance was created. Click Log In.

    monitor login dialogDescription of this image
  • The DBaaS Monitor home appears, displaying generic information about the database instance.  The Database zone contains sections showing snapshot information about database status, listener status, online database storage, alerts, sessions, and wait events.  The Operating System zone contains sections showing snapshot information about memory, CPU, file system storage, and processes.

    You can click each section to drill down to a more circumstantial view of the monitored area.

    Alternatively, you can access the very circumstantial views using the Database and OS drop-down menus.

    monitor home pageDescription of this image
  • In the Database area, click Online Database Storage.

    database monitor pageDescription of this image
  • The Storage page displays a breakdown of the storage currently allocated to the database. It shows the overall database storage, followed by the storage for each PDB. For each PDB, you can note or camouflage storage information about its tablespaces.

    In CDB$ROOT and PDB1, click note tablespaces.

    In PDB1, click the SYSTEM tablespace.

    storage pageDescription of this image
  • The tablespace circumstantial page provides an interactive report on the segments that exist within the tablespace. Most segments are user objects, and they include tables, big objects, and indexes.

    click update access ruleDescription of this image
  • From the Database drop-down menu, click Alerts. An interactive report displays the entries in the database's alert log. The alert log is a chronological log of messages and errors commonly used to learn whether the background processes acquire encountered errors.

    click alert logDescription of this image

    database alert logDescription of this image

  • From the Database drop-down menu, click Sessions to view open user sessions. An interactive report displays information about any currently opened sessions in the database. The top-most table shows summarized data about each open session. If you click inescapable rows in this table, you can scroll down to the bottom of the window to observe more circumstantial information about that session.

    click sessionsDescription of this image

    sessions pageDescription of this image

    session  circumstantial row infoDescription of this image

  • From the Database drop-down menu, click Waits to view the wait events in true time. Your database may not note wait events. In this example, the wait events are essentially of CPU consumption, plus a cramped bit of system I/O, and various waits categorized as "Other".

    click wait eventsDescription of this image

    wait eventsDescription of this image

  • From the Database drop-down menu, click Manage. The Manage page allows you to observe the status of the database and allows you to whirl the database on and off. In 12c databases, the status of individual PDBs is also displayed. On this page you can also create, clone, plug, unplug or modify PDBs. 

    Click Create PDB and examine the Create PDB dialog. Then cancel out of the dialog.

    click configure select rdbmsDescription of this image

    database configuration pageDescription of this image

  • From the Database drop-down menu, click Parameters to view or modify current parameter values. 

    click parameter settingsDescription of this image

    The Database Parameters page appears. To modify the value of a parameter, select the parameter, change its value, and click the redeem changes button at the top. You will acquire an occasion to review your change before applying it. Changes that are saved to remembrance will rob sequel immediately across any sessions, but will be lost upon the next database instance shutdown. Changes that are saved to the PFILE will rob sequel upon the next database instance startup and are persistent.


  • Performance Caching in a Snowflake Data Warehouse | killexams.com true questions and Pass4sure dumps

    Like any modern database, Snowflake uses various levels of caching to maximize query performance and reduce disk I/O and machine load. This article provides an overview of the techniques used by Snowflake, the data warehouse built for the cloud.

    System Architecture

    Before starting, it’s worth considering the underlying Snowflake architecture and explaining when Snowflake caches data. The diagram below illustrates the overall architecture, which consists of three layers:

    Image title

  • The Service Layer accepts SQL requests from users, coordinates queries, and manages transactions and results. Logically, this can be assumed to hold the result cache, a cached copy of the results of every query executed.
  • The Compute Layer actually does the weighty lifting. This is where the actual SQL is executed across the nodes of a Virtual Data Warehouse. This layer holds a cache of data queried, and is often referred to as Local Disk I/O, although in reality this is implemented using SSD storage. All data in the compute layer is temporary and only held as long as the virtual warehouse is active.
  • The Storage Layer provides long-term storage of results. This is often referred to as Remote Disk and is currently implemented on either Amazon S3 or Microsoft Blob storage.
  • Snowflake Caching Layers

    The diagram below illustrates the levels at which data and results are cached for subsequent use:

  • The Result Cache holds the results of every query executed in the past 24 hours. These are available across virtual warehouses, so query results returned to one user are available to every user in the system.
  • The Local Disk Cache caches data used in queries. Whenever data is needed for a given query, it's retrieved from the Remote Disk storage, and cached for subsequent queries here.
  • The Remote Disk holds the long-term storage. This smooth is accountable for data resilience, which in the case of Amazon Web Services, means 99.999999999% durability, even in the event of an entire data center failure.
  • Image title

    Benchmark Testing Setup

    Every Snowflake database is delivered with a pre-built and populated set of Transaction Processing Council (TPC) benchmark tables. To test the result of caching, I set up a succession of test queries against a minute subset of the data, which is illustrated below.

    Image title

    All the queries were executed on a MEDIUM sized cluster (8 nodes) and joined the tables. The tables were queried exactly as is, without any performance tuning.

    The following query was executed multiple times, and the elapsed time and query arrangement were recorded each time.

    Image title

    The screenshot below illustrates the results of the query which summarise the data by Region and Country. In total, the SQL queried, summarized, and counted 1.5 billion rows of data. The screenshot shows the first eight lines returned.

    Image title

    Test Sequence

    The test sequence was as follows:

  • Run from cold: Starting a new virtual warehouse (with no local disk caching), and executing the query.
  • Run from warm: Disabling the result caching and repeating the query. This made expend of the local disk caching.
  • Run from hot: Again, repeated the query, but with the result caching switched on.
  • Each query included over 60Gb of raw data, although as Snowflake was able to automatically compress the data, the actual data transfers were around 12Gb. As Snowflake is a columnar data warehouse, it automatically returns the columns needed rather then the entire row to maximize query performance.

    Run From Cold

    This query returned in around 20 seconds and demonstrates it scanned around 12Gb of compressed data, with 0% from the local disk cache. This means it had no benefit from disk caching.

    Image title

    The bar chart above demonstrates around 50% of the time was spent on local or remote disk I/O, and only 2% on actually processing the data. Clearly any design changes they can accomplish to reduce the disk I/O will wait on this query.

    The results also demonstrate the queries were unable to fulfill any partition pruning which might better query performance. We’ll cover the sequel of partition pruning and clustering in the next article.

    Run From Warm

    This query was executed immediately after, but with the result cache disabled, and it completed in 1.2 seconds – around 16 times faster. In this case, the Local Disk cache (which is actually SSD on Amazon Web Services) was used to recur results, and disk I/O is no longer a concern.

    Image title

    In the above case, the disk I/O has been reduced to around 11% of the total elapsed time, and 99% of the data came from the (local disk) cache. While querying 1.5 billion rows, this is clearly an excellent result.

    Run From Hot

    This query returned results in milliseconds and involved re-executing the query, but with this time, the result cache enabled. Normally, this is the default situation, but it was disabled for testing purposes.

    Image title

    The above profile indicates the entire query was served directly from the result cache (taking around 2 milliseconds). Although not immediately obvious, many dashboard applications involve repeatedly refreshing a succession of screens and dashboards by re-executing the SQL. In these cases, the results are immediately returned in milliseconds.

    Although more information is available in the Snowflake Documentation, a succession of tests demonstrated the result cache will be reused unless the underlying data (or SQL query) has changed. As a succession of additional tests demonstrated inserts, updates and deletes which don't affect the underlying data are ignored, and the result cache is used, provided data in the micro-partitions remains unchanged.

    Finally, results are normally retained for 24 hours, although the clock is reset every time the query is re-executed, up to a restrict of 30 days, after which results query the remote disk.

    Summary

    The sequence of tests was designed purely to illustrate the sequel of data caching on Snowflake. The tests included:-

  • Raw Data: Including over 1.5 billion rows of TPC generated data, a total of over 60Gb of raw data
  • Initial Query: Took 20 seconds to complete, and ran entirely from the remote disk.  Quite impressive.
  • Second Query: Was 16 times faster at 1.2 seconds and used the Local Disk (SSD) cache.
  • Result Set Query: Returned results in 130 milliseconds from the result cache (intentionally disabled on the prior query).  
  • To allocate the above results in context, I repeatedly ran the very query on Oracle 11g production database server for a tier one investment bank and it took over 22 minutes to complete.

    Finally, unlike Oracle where additional custody and application must be made to ensure remedy partitioning, indexing, stats gathering and data compression, Snowflake caching is entirely automatic and available by default. Absolutely no application was made to tune either the queries or the underlying design, although there are options available, which I'll debate in the next article. token up below for further details.

    Further Reading

    Thank you for reading this. If you institute this article spicy and would fondness to know more, you can read more about Snowflake, and Data Warehousing at www.analytics.today.

    One of the most well-liked articles I’ve written describes my undergo as an Oracle warehouse architect and compares this to how Snowflake would ply the problems I encountered. You can read more at The yardstick Data Warehouse Architecture.

    Topics:

    big data ,caching ,database ,performance ,performance optimization ,queries ,performance caching



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