Saveti – SQL Server

Zašto televozori nemaju tu mogućnost da se prevod nekog filma ili serije „okrene” vertikalno na levu ili desnu stranu ekrana (na levoj strani odozgo na dole a na desnoj obrnuto) ili da se „okrene” cela slika zajedno sa prevodom . Tada bi bilo lako da se ležeći, paralelno sa televizorom, gleda film ili serija i čita prevod. Da li i Vi imate neku ideju? Izvor: Whynot. net Pripremio: Milenko Kusurović

Da biste napravili server na svom računaru, prvo morate instalirati Apache Webserver. Prvo ga skinite sa www. apache. org . Nakon što skinete najnoviju verziju Apache web servera (Win32 verziju), na red dolazi instalacija. Nakon što pokrenete. exe file koji ste upravo skinuli sa neta, moraćete izabrati u koji direktorijum želite da instalirate Apache. Apache vam predlaže da ga automatski instalirate u C:Program FilesApache GroupApache . Ako koristite NT ili Win2000+, Apache će vas pitati da li želite da ga instalirate kao servis. Ako ga instalirate kao servis, Apache će se automatski uključiti pri svakom startu Windowsa i sve vreme će raditi u pozadini. Jedini trag o Apacheu ćete naći u Task Manageru, koji možete otvoriti sa CTRL+ALT+DEL . Ako ga ne instalirate kao servis, moraćete ga svaki put pokretati ručno klikom na ikonicu Start Apache koja će se nalaziti u Start meniju nakon instalacije. Time je instalacija praktično završena. Da biste proverili da li je Apache korektno instaliran, pokrenite Apache (ako nije instaliran kao servis) i upišite u vašem pretraživaču adresu http://127. 0. 0. 1/ . Ako se otvori  stranica na kojoj piše " If you can see this, it means that the installation of the Apache web server software on this system was successful . . . ", onda ste očigledno pravilno instalirali Apache server. Stranica koja se otvorila nalazi se u direktorijumu C:Program FilesApache GroupApachehtdocs. To je nakon instalacije vaš root direktorijum za web dokumente. Radi preglednosti i lakšeg rada promenite root direktorijum, kao i još nekoliko sitnica. Konfiguracija Apache servera funkcioniše veoma jednostavno koristeci httpd. conf file, koji se nalazi u C:Program FilesApache GroupApacheconf direktorijumu. httpd. conf file možete otvoriti pomoću bilo kojeg editora. Možete ga urediti takođe u Windows Notepadu. Kliknite na httpd. conf desnim tasterom miša i odaberite u meniju opciju Open with. Trebalo bi konfigurisati sledeće opcije: ServerAdmin - tu upišite vašu e-mail adresu. ServerName - tu upišite ime servera. Obično stoji localhost, ali vi možete upisati šta želite. Ako upišete npr. localhost, onda ćete u pretraživaču uvek pozivati vaš root direktorijum sa http://localhost/. DirectoryIndex - tu upišite index. html index. htm index. php index. php3. DocumentRoot - root direktorijum za vaše internet dokumente/stranice. Možete izabrati posebnu particiju/disk za to -> D:/sajt (pre toga napravite direktorijum D:/sajt). i naravno upišite ga pod znacima navoda. - tu bi takođe trebalo upisati vaš root direktorijum, odnosno D:/sajt, takođe pod znacima navoda. Ovim je instalacija Apachea završena.

Ovaj korak-po-korak članak opisuje kako da onemogućite lokalni administratorski nalog. Ova informacija se ne odnosi na domenski administratorski nalog u Active Directory domenu. Važna napomenaPre nego što da onemogućite lokalni administratorski nalog, uverite se da postoji najmanje još jedan lokalni ili mrežni korisnik koji može da pristupi računaru sa administratorskim pravima. U suprotnom, kasnije nećete moći da vratite ovu akciju unazad. 1. Prijavite se kao administrator, ili kao korisnik sa administratorskim pravima. 2. Kliknite desnim tasterom miša na My Computer, a zatim kliknite na Manage. 3. U levom delu ekrana, proširite Local Users and Groups a zatim kliknite naUsers. 4. U desnom delu ekrana, dvokliknite na nalog Administrator. 5. Na kartici General selektujte opciju Account is disabled a zatim kliknite naOK. 6. Ugasite Computer Management konzolu. Novo podešavanje će imate efekta sledeći put kada pokušate da se prijavite na ovaj računar. Kako da zabranite pristup lokalnom administratoru u Windowsu 2000 U Windowsu 2000, nije moguće onemogućiti built-in naloge. Ipak, možete zabraniti pristup lokalnom administratorskom nalogu tako što ćete podesiti lokalna sigurnosna podešavanja. NapomenaPre nego što ispratite sledeće korake, uverite se da postoji najmanje još jedan lokalni ili mrežni korisnik koji može da pristupi računaru sa administratorskim pravima. 1. Prijavite se kao administrator, ili kao korisnik sa administratorskim pravima. 2. Kliknite na Start, pokažite na Programs, zatim na Administrative Tools, a zatim kliknite na Local Security Policy. 3. U levom delu ekrana, proširite Local Policies a zatim kliknite na User Rights Assignment. 4. U desnom delu ekrana, dvokliknite naDeny access to this computer from the network. 5. U Local Security Policy Setting, kliknite na Add. 6. U polju Users and Groups kliknite na nalog Administrator a zatim kliknite na Add. 7. Kliknite na OK, kliknite na OK, a zatim ugasite Local Security Settings konzolu. Morate restartovati računar da bi nova sigurnosna podešavanja imala efekta. Pripremio: Milenko Kusurović

1. Desni klik na „My Computer” ikonu2. Kliknite na „Advanced”3. Pogledajte pod „Performance” i otvorite „Settings”4. Isključite sve ili neke od sledećih opcija: • Fade or slide menus into view• Fade or slide ToolTips into view• Fade out menu items after clicking• Show Shadows under menus• Slide open combo boxes• Slide taskbar buttons• Use a background image for each folder type• Use common tasks in folders Vaš XP će izgledati isto kao pre, ali će raditi malo brže. Pripremio: Milenko Kusurović

Korak 1 Prvo treba da verifikujete da je omogućen SELinux. Kod: Trebalo bi da se pojavi Enforcing. Korak 2 Ako vidite drugačiji rezultat, pokušajte: Kod:  [root@www ~]# setenforce 1 Ako se javlja greška, onda vam SELinux nije uključen. Treba da odete na /etc/selinux/config i izmenite fajl da biste pokrenuli SELinux. Kad ga uključite, moraćete da rebutujete linux server da bi se promene učitale. Korak 3 Kad omogućite SELinux. , uradite sledeće: podesite SELinux. da dozvoli daljinske konekcije za HTTP Kod: setsebool -P httpd_can_network_connect=1 Korak 4 To je to. Gotovo. Dijana Petrićević

U ovom prilogu prikazaćemo vam metod upotrebe jedne privremene tabele u svrhu uklanjanja dupliciranih slogova u tabeli. Ponekad će vam zatrebati mogućnost uklanjanja dupliciranih slogova (onih koji imaju iste vrednosti nekih ili svih atributa). Uklanjanje viška istih slogova, sa zadržavanjem samo jednog sloga, može se izvršiti na više načina, a mi ćemo vam prikazati metod sa upotrebom jedne privremene (temporary) tabele. Za potrebe primera kreiraćemo tabelu sa dva polja i dva ista sloga. Recimo, tabela “Linkovi” može ovako da izgleda (SELECT * FROM Linkovi;): naziv URL———————————————-Krstarica www. krstarica. comYahoo www. yahoo. comKrstarica www. krstarica. com———————————————- Da bismo uklonili ponavljanje istog sloga (Krstarica), uradićemo sledeće: • Kreiraćemo privremenu tabelu selektovanjem samo različitih slogova osnovne tabele (”Linkovi“). CREATE TABLE Linkovi_Priv ASSELECT DISTINCT naziv, URLFROM Linkovi; • Možemo proveriti stanje u novonastaloj tabeli i uveriti se da sada nema dupliciranih slogova: SELECT *FROM Linkovi_Priv; Rezultat će biti: naziv URL———————————————-Krstarica www. krstarica. comYahoo www. yahoo. com———————————————- • Vršimo brisanje osnovne tabele iz baze, instrukcijom DROP: DROP TABLE Linkovi; • I na kraju, vršimo preimenovanje privremene tabele (Linkovi_Priv) u osnovnu (Linkovi), instrukcijom ALTER: ALTER TABLE Linkovi_Priv RENAME TO Linkovi; Ne zaboravite samo da ponovo kreirate sve potrebne indekse, i dozvole za rad i pristup novoj tabeli (kao na izbrisanoj originalnoj tabeli). Pripremio: Milenko Kusurović

Articles, Tutorials and Talks A tale of troubleshooting database performance, with Cassandra and sysdig In this article, I'll show you  a Cassandra performance issue that we recently dealt with, and I want to cover how we spotted the problem, what kind of troubleshooting we did to better understand it, and how we eventually solved it. RDBMS & Graphs: Drivers for Connecting to a Graph Database This week, we'll discuss the language drivers and APIs specific to Neo4j with plenty of resources for further exploration. At this point, if you are curious about other, non-Neo4j graph databases, we encourage you to explore the available drivers within their respective communities. DynamoDB Design Patterns and Best Practices In this talk, we'll walk you through common NoSQL design patterns for a variety of applications to help you learn how to design a schema, store, and retrieve data with DynamoDB. We will discuss best practices with DynamoDB to develop IoT, AdTech, and gaming apps. Redis Performance Monitoring with the ELK Stack This post looks at how you can do Redis performance monitoring using the ELK Stack to ship, analyze, and visualize the data. Analyze a Time Series in Real Time with AWS Lambda, Amazon Kinesis and Amazon DynamoDB Streams This post explains how to perform time-series analysis on a stream of Amazon Kinesis records, without the need for any servers or clusters, using AWS Lambda, Amazon Kinesis Streams, Amazon DynamoDB and Amazon CloudWatch.   We demonstrate how to do time-series analysis on live web analytics events stored in Amazon Kinesis Streams and present the results in near real-time for use cases like live key performance indicators, ad-hoc analytics, and quality assurance, as used in our AWS-based data science and analytics  RAVEN (Reporting, Analytics, Visualization, Experimental, Networks) platform at JustGiving. Churn Prediction with PySpark using MLlib and ML Packages The prediction process is heavily data driven and often utilizes advanced machine learning techniques. In this post, we'll take a look at what types of customer data are typically used, do some preliminary analysis of the data, and generate churn prediction models - all with PySpark and its machine learning frameworks. SASIIndex SASIIndex, or "SASI" for short, is an implementation of Cassandra's Index interface that can be used as an alternative to the existing implementations. This post goes on describe how to get up and running with SASI, demonstrates usage with examples, and provides some details on its implementation. Building an Async Networking Layer for mongos      How to design an HBase data model for recommendations      Getting Started with Couchbase and Spark on Apache Zeppelin Strong consistency in Manhattan Financial Markets are Graphs Using Neo4j to Take Us to the Stars Books Spark: Big Data Cluster Computing in Production "Spark: Big Data Cluster Computing in Production" goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more. Interesting Projects, Tools and Libraries Chronicle-Map Replicate your Key Value Store across your network, with consistency, persistance and performance. mailcap A mail capture and archival server for RethinkDB. DalmatinerDB DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. The goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. HDocDB HDocDB is a client layer for using HBase as a store for JSON documents. It implements many of the interfaces in the OJAI framework. Upcoming Events and Webinars Webinar: Continuous Applications: Spark, Kafka, Beam, and Beyond After this webcast, you'll be able to: Comprehend various streaming concepts like watermarks, triggers and how they affect system requirements and design Understand complexities of continuous applications architectures and design trade-offs involved Develop complex big data applications beyond simple aggregations with simplicity, completeness and changing semantics Webinar: Low-latency ingestion and analytics with Apache Kafka and Apache Apex (Hadoop) This talk will cover a fully fault tolerant, scalable, and operational ingestion from Kafka using Apache Apex application, running natively in Hadoop. The talks will deep dive into technical details of the connectors in Apache Malhar. Details of production use cases will also be discussed. Webinar: Data Modeling, Data Querying, and NoSQL: A Deep Dive Attend and learn: How a flexible data model and structured query language simplifies development Best practices for data modeling -- when to embed data; when to reference it How to do SQL-like queries on semi-structured data Webinar: MongoDB and Analytics: Building Solutions with the MongoDB BI Connector In this webinar, we will cover the architecture needed to use the BI Connector with MongoDB. We will also demonstrate how to build reports with your data.

Virusi, crvi i trojanski konji su zlonamerni programi koji mogu da izazovu štetu na vašem računaru i podacima koje imate na njemu. Oni takođe mogu da uspore Internet vezu, pa čak i da koriste vaš računar za dalje širenje na računare vaših prijatelja, porodice, kolega, kao i na ostatak Weba. Dobra vest je to što, uz malu prevencije i malo razmišljanja, postoji manje šansi da ćete postati žrtva ovih pretnji. Zamislite to kao zaključavanje ulaznih vrata na stanu. Pročitajte ovaj tekst do kraja da biste saznali sve o odlikama virusa, crva i trojanskih konja i o razlikama između njih. Šta je virus?Virus je deo računarskog koda koji se prikači na program ili datoteku tako da može da se prenosi sa računara na računar, šireći pri tom zarazu. Virusi mogu da oštete vaš softver, hardver i vaše datoteke. Virus je kôd napisan sa jasnom namenom da sam sebe umnožava. Virus pokušava da se širi od računara do računara tako što se kači na neki program. Isto kao što opasnost kod ljudskih virusa varira od Ebole do jednodnevnog gripa, tako i među računarskim virusima postoje oni koji su samo mala smetnja pri radu do onih koji su potpuno destruktivni. Dobra vest je to što se pravi virus ne širi bez ljudskih postupaka koji bi ga pokretali, kao što su deljenje datoteke ili slanje e-poruke. Pripremio: Milenko Kusurović

Postgres The Good Parts: Arrays Postgres is packed with great datatypes, one of them that may not get quite as much attention as JSONB is the Array type. This post starts with the basics then digs into some more advanced uses and operators of it to make things more practical. RYAN KELLY Problems with Concurrent Postgres Indexes (and How to Solve Them) If you’re not familiar with concurrent indexing in Postgres, first off you should give them a look. Second, you should know they’re not without their issues which are walked through here. KEN ALEX FASSONE   #TUTORIAL  Frustrating Proprietary Licences Pushing Moves to PostgreSQL Several European institutions have cited complex licences from their traditional proprietary suppliers as an important reason to deploy PostgreSQL instead. ADRIAN OFFERMAN   #NEWS  Find Your Perfect Company Match You're smart, you're efficient. Why job hunt the old way? Try Hired and get your profile in front of thousands of top tech companies. HIRED. ​COM    #SPONSORED  Announcing Citus 5. 2 Citus 5. 2 is out which is an extension which makes it simple to shard your database, allowing you to scale out. The 5. 2 release includes better ORM support, broader SQL coverage, and improved transactional semantics on a single shard. CITUS DATA   #NEWS  Optimising PostgreSQL Database Queries using Indexes An intro to indexes, what they are, how to create, as well as some more customized approaches. All of this with a focus on improving your apps performance. REDPANTHERS   #TUTORIAL  Weighted Random Sampling with Postgres Generating random data isn’t too hard, but generating a realistic sampling based on some weighting can be. This post walks you through how to do this in SQL, perfect for fake datasets. MARCO SANTONI   #TUTORIAL  Walbouncer: A Proxy for Selective PostgreSQL Replication With support for Postgres 9. 5, Walbouncer is a standalone replication proxy, enabling selective replication by forwarding data only for specific databases/tablespaces to the connected replicas. KAAREL MOPPEL   #TOOLS  Send slow queries to Slack, PagerDuty, HipChat, or/and email Make the most of log_min_duration_statement. Get notified instantly or receive a daily summary (free). PAPERTRAIL   #TOOLS  #SPONSORED  JsQuery: JSON Query Language with GIN Indexing Support POSTGRES PROFESSIONAL   #CODE 

R Programming A-Z™: R For Data Science With Real Exercises!Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2 Redis Day London 2018 - Call for PapersRedis Day is a single-track, full-day, free-to-the-public event about anything and everything Redis. Its main purpose is sharing technical knowledge with the local and global Redis communities, by providing a stage for developers and users to tell their stories. This is a call to all speakers who wish to tell theirs. We don’t care whether this is your first time speaking or if you’re a rockstar performer, nor do we mind if you’re native to the city or a tentacled decapod from Alpha Centauri. We’re interested in your story and we’ll do whatever it takes to help you bring it to light. A one size fits all database doesn't fit anyone The days of the one-size-fits-all monolithic database are behind us, and developers are using a multitude of purpose-built databases. How we built a data pipeline with Lambda Architecture using Spark/Spark StreamingWalmart Labs is a data-driven company. Many business and product decisions are based on the insights derived from data analysis. I work in Expo which is the A/B Testing platform for Walmart. As part of the platform we built a data ingestion and reporting pipeline which is used by the experimentation team to identify how the experiments are trending. In this blog I would like to give a little primer on how we built the data ingestion and reporting pipeline with Lamda Architecture using Spark which provides code reusability between the Streaming and the batch layers, key configurations for the deployment and a few troubleshooting tips. Through the Looking Glass: Analyzing the interplay between memory, disk, and read performance. Understanding the relationships between various internal caches and disk performance, and how those relationships affect database and application performance, can be challenging. We’ve used the YCSB benchmark, varying the working set (number of documents used for the test) and disk performance, to better show how these relate. While reviewing the results, we’ll cover some MongoDB internals to improve understanding of common database usage patterns. Rolling the Heroku Redis FleetHow the Heroku Data infrastructure team manages large fleet operations, such as this one required by a recent Redis remote code execution vulnerability. A Performance Cheat Sheet for MongoDBPerformance tuning is not trivial, but you can go a long way with a few basic guidelines. In this post, we will discuss how you analyze the workload of your MongoDB production systems, and then we’ll review a list of important configuration parameters that can help you improve performance. Modelling Cloudant data in TypeScriptUsing TypeScript classes in your code and saving their JSON representation in a Cloudant database. Kafka Python and Google AnalyticsLearn how to use Kafka Python to pull Google Analytics metrics and push them to your Kafka Topic.  This will allow us to analyze this data later using Spark to give us meaningful business data. JWT Authentication with GraphQL, Node. js, and Couchbase NoSQLLearn how to protect specific properties and data elements of your GraphQL powered API that uses the NoSQL database, Couchbase Server, and Node. js using JSON Web Tokens (JWT). Migrating Hulu’s Hadoop Clusters to a New Data Center — Part Two: Creating a Mirrored Hadoop InstanceAs part of a larger migration to a new data center in Las Vegas, we migrated our Hadoop clusters using two approaches. The first approach we discussed in our last post involved extending our instance of Hadoop to migrate our largest and heaviest cluster of HDFS. In this post, we’ll discuss migrating smaller HDFS clusters (100–200 nodes) for special purposes by creating a mirrored Hadoop instanceBuild a Mobile Gaming Events Data Pipeline with Databricks DeltaExploring World Cup 2018 with Neo4j and GraphQLUsing a GraphQL API for Database AdministrationA Journey Through SparkInteracting with Neo4j in NodeJS using the Neode Object MapperAnswering English questions using knowledge graphs and sequence translation Interesting Projects, Tools and Libraries Bitdb Bitdb is a NoSQL database powered by Bitcoin. maniDistributed cron using redis. ml-modelsMachine Learning Procedures and Functions for Neo4j. Copyright © 2018 NoSQL Weekly, All rights reserved.  You are receiving our weekly newsletter because you signed up at http://www. NoSQLWeekly. com.