JSS2015 - Keynote jour 1
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Pour cette première keynote des Journées SQL Server, Isabelle Van Campenhoudt et Jean-Pierre Riehl parlent de la communauté Data & BI Microsoft et invitent sur scène différents speakers pour faire un tour des nouveautés Azure et SQL Server 2016
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JSS2015 - Keynote jour 1
- 1. #JSS2015 Les journées SQL Server 2015 Un événement organisé par GUSS @GUSS_FRANCE Isabelle Van Campenhoudt, Membre du Board GUSS Jean-Pierre Riehl, Membre du Board GUSS
- 3. #JSS2015 5 ans de JSS
- 4. #JSS2015 10 ans de La communauté Data & BI Microsoft
- 5. #JSS2015 Une communauté 2000 contacts 700 lecteurs 1000 participants
- 6. #JSS2015 Une communauté Worldwide Vidéo "What is PASS ?" (cliquez)
- 8. #JSS2015
- 9. #JSS2015 Agenda 2016 Janvier : DBA chez Criteo Février : TechDays Mars-Avril : Support MS Avril-Mai : 24 hours of PASS French Juin : SQLSaturday Paris 2016
- 11. #JSS2015 Merci à nos sponsors
- 12. #JSS2015 Concours Suivez @GUSS_FRANCE Répondez aux questions avec #JSS2015 Remplissez les évaluations des sessions Merci à
- 13. #JSS2015 Soirée à l’Explorer 1 place à gagner à chaque session
- 14. #JSS2015
- 15. #JSS2015 C’est quoi un DBA en 2016 ?
- 18. #JSS2015 …ou juste un plus gros bocal
- 20. #JSS2015
- 23. #JSS2015 SkillInsight Et vous, comment faites vous pour valider les compétences techniques de vos candidats ? Self-Assessment Test de connaissance simple limité à 2 ou 3 thèmes, avec comme résultat échoué ou validé. Depth-Assessment Test plus poussé sur différents scénarios avec une note comme résultat de chacun des thèmes. Réception du CV Self- Assessment Entretien d’embauche Depth- Assessment A vous de décider
- 24. #JSS2015 SQL Server, la meilleure base de données
- 26. No change in programming model New Insights INSERT / BULK INSERT UPDATE DELETE MERGE DML SELECT * FROM temporal Querying Temporal tables CREATE temporal TABLE PERIOD FOR SYSTEM_TIME… ALTER regular_table TABLE ADD PERIOD… DDL FOR SYSTEM_TIME AS OF FROM..TO BETWEEN..AND CONTAINED IN Temporal Querying
- 27. Temporal table (actual data) Insert / Bulk Insert * Old versions Update */ Delete * Temporal Tables : Comment ça marche? History Table
- 28. Stretch Tables
- 29. Durability latency controlled by DB option DATA_FLUSH_INTERNAL_SECONDS Compile Execute Plan Store Runtime Stats Query Store Schema Query data store Stocke le code SQL (+ propriétés) Stocke les choix de plan et les métriques de performance Support les redémarrages, updates, recompilations Performances, Troubleshooting Nouvelles DMVs Forçage de plans d’exécution Interface de comparaison
- 30. Haute-Disponibilité Moins d’adhérence à Active Directory Load-balancing sur les Readable-Secondaries AlwaysOn disponible en Edition Standard Performances Plus d’hybride …
- 31. #JSS2015 Base de données la moins vulnérable depuis 6 ans (source NIST 2015) Sécurité
- 33. dbo.Patients Jane Doe Name 243-24-9812 SSN USA Country Jim Gray 198-33-0987 USA John Smith 123-82-1095 USA dbo.Patients Jane Doe Name 1x7fg655se2e SSN USA Jim Gray 0x7ff654ae6d USA John Smith 0y8fj754ea2c USA Country Result Set Jim Gray Name Jane Doe Name 1x7fg655se2e SSN USA Country Jim Gray 0x7ff654ae6d USA John Smith 0y8fj754ea2c USA dbo.Patients SQL Server Query TrustedApps SELECT Name FROM Patients WHERE SSN=@SSN @SSN='198-33-0987' Result Set Jim Gray Name SELECT Name FROM Patients WHERE SSN=@SSN @SSN=0x7ff654ae6d Column Encryption Key Enhanced ADO.NET Library Column Master Key Client side Always Encrypted Help protect data at rest and in motion, on-premises & cloud ciphertext
- 34. -- user defined function CREATE FUNCTION Security.fn_securitypredicate (@Manager AS sysname) RETURNS TABLE WITH SCHEMABINDING AS RETURN SELECT 1 AS fn_securitypredicate_result WHERE @Manager = USER_NAME() OR USER_NAME() = 'BigBoss'; -- security policy CREATE SECURITY POLICY ManagerFilter ADD FILTER PREDICATE Security.fn_securitypredicate(ManagerName) ON dbo.Salaries WITH (STATE = ON); RLS : Row Level Security * Merci Boris Hristov – SQLSaturday Paris 2015
- 36. Operational Analytics Bénéfices Pas de latence / temps réel Pas d’ETL Pas de Datawarehouse Challenges Eviter les Locks et Blocks Minimiser l’impact sur l’OLTP Efficacité des requêtes analytiques sur un schéma OLTP IIS Server
- 37. #JSS2015 Théorie de la « crêpe au sucre » Vous avez Hekaton ? Vous avez CSI ? Eh bien avec Hekaton vous faites une table in-memory et vous mettez CSI dessus
- 39. #JSS2015 Autres fonctionnalités Live Query Statistics Dynamic Data Masking R in SQL Server Hybrid Polybase Single SSDT …
- 40. #JSS2015 Sessions connexes Nouveautés SQL 2016 : Sécurité, Temporal & Stretch Tables David Barbarin et Sébastien Haby AlwaysOn 2016 Nicolas Soukoff In-Memory 2016 : Operational Analytics Benjamin Vesan et Guillaume Nocent Query Store, le (nouveau) meilleur ami du DBA Benjamin Vesan et Guillaume Nocent
- 41. #JSS2015 De Nouvelles Architectures Architecture Lambdas, Big Data, Cortana Analytics, etc.
- 42. #JSS2015#JSS2015 Plus d’outils, plus de complexité ?
- 46. #JSS2015 Azure SQL Data Warehouse PolyBase Scale out compute SQL DW Instance Hadoop VMs / Azure Storage Any data, any size, anywhere
- 47. #JSS2015 Data Warehouse Unit (DWU) Simply buy the query performance you need, not just hardware Quantified by workload objectives: how fast rows are scanned, loaded, copied Measure of Power Transparency First DW service to offer compute power on demand, independent of storageOn Demand Scan Rate 3.36M row/sec Loading Rate 130K row/sec Table Copy Rate 350K row/sec * * 100 DWU = 297 sec 400 DWU = 74 sec 800 DWU = 37 sec 1,600 DWU = 19 sec *
- 48. #JSS2015 Analytics Storage HDInsight (“managed clusters”) Azure Data Lake Analytics Azure Data Lake Storage Azure Data Lake U-SQL
- 50. #JSS2015 • Hadoop : Framework d’agrégation des données. • HDInsight : Implémentation d’Hadoop sur Azure par Hortonworks HDInsight
- 51. #JSS2015 Comment ça marche (version simplifiée ) Jobs Map/Reduce Données en entrée Données en sortie aggrégées - Temps de traitement proportionnel au nombre de machines dans le cluster - Capacité de stockage proportionnel au nombre de machines dans le cluster csv csv csv csv csv csv csv csv csv csv csv csv csv csv csv
- 52. #JSS2015 Comment ça marche (version simplifiée) HDFS ou WASB Map Map Map Reduce Job Map / Reduce
- 53. #JSS2015 Création du cluster : Choix du nb de nœuds et types de machines • Choix du nombre de nœuds • Choix du type de machines • Estimation du prix à l’heure
- 55. #JSS2015 Azure Data Factory Azure Data Factory
- 57. #JSS2015 57 Business Scenarios Recommendations, customer churn, forecasting, etc. Perceptual Intelligence Face, vision Speech, text Personal Digital Assistant Cortana Dashboards and Visualizations Power BI Machine Learning and Analytics Azure Machine Learning Azure Stream Analytics DATA Business apps Custom apps Sensors and devices INTELLIGENCE ACTION People Automated Systems Big Data Stores Azure SQL Data Warehouse Information Management Azure Data Factory Azure Data Catalog Azure Event Hub Azure Data Lake Store Azure HDInsight (Hadoop) Azure Data Lake Analytics Cortana Analytics Suite
- 59. #JSS2015
Editor's Notes
- Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx How Does Temporal Work? System-versioning for a table is implemented as a pair of tables, a current table and a history table. Within each of these tables, two additional datetime (datetime2 datatype) columns are used to define the period of validity for each record – a system start time (SysStartTime) column and a system end time (SysEndTime) column. The current table contains the current value for each record. The history table contains the each previous value for each record, if any, and the start time and end time for the period for which it was valid. INSERTS: On an INSERT, the system sets the value for the SysStartTime column to the UTC time of the current transaction based on the system clock and assigns the value for the SysEndTime column to the maximum value of 9999-12-31 – this marks the record as open. UPDATES: On an UPDATE, the system stores the previous value of the record in the history table and sets the value for the SysEndTime column to the UTC time of the current transaction based on the system clock. This marks the record as closed, with a period recorded for which the record was valid. In the current table, the record is updated with its new value and the system sets the value for the SysStartTime column to the UTC time for the transaction based on the system clock. The value for the updated record in the current table for the SysEndTime column remains the maximum value of 9999-12-31. DELETES: On a DELETE, the system stores the previous value of the record in the history table and sets the value for the SysEndTime column to the UTC time of the current transaction based on the system clock. This marks this record as closed, with a period recorded for which the previous record was valid. In the current table, the record is removed. Queries of the current table will not return this value. Only queries that deal with history data return data for which a record is closed. MERGE: On a MERGE, MERGE behaves as an INSERT, an UPDATE, or a DELETE based on the condition for each record.
- Source: https://msdn.microsoft.com/en-us/library/dn935015(v=sql.130).aspx The SYSTEM_TIME period columns used to record the SysStartTime and SysEndTime values must be defined with a datatype of datetime2.
- Query store is a new feature in that provides DBAs with insight on query plan choice and performance. It simplifies performance troubleshooting by enabling you to quickly find performance differences caused by changes in query plans. The feature automatically captures a history of queries, plans, and runtime statistics, and retains these for your review. It separates data by time windows, allowing you to see database usage patterns and understand when query plan changes happened on the server. The query store presents information by using a Management Studio dialog box, and lets you force the query to one of the selected query plans. For more information, see Monitoring Performance By Using the Query Store.
- Source: https://msdn.microsoft.com/en-us/library/mt163865(v=sql.130).aspx Always Encrypted is a feature designed to protect sensitive data, such as credit card numbers or national identification numbers (e.g. U.S. social security numbers), stored in SQL Server databases. Always Encrypted allows clients to encrypt sensitive data inside client applications and never reveal the encryption keys to SQL Server. As a result, Always Encrypted provides a separation between those who own the data (and can view it) and those who manage the data (but should have no access). By ensuring on-premises database administrators, cloud database operators, or other high-privileged, but unauthorized users, cannot access the encrypted data, Always Encrypted enables customers to confidently store sensitive data outside of their direct control. This allows organizations to encrypt data at rest and in use for storage in Azure, to enable delegation of on-premises database administration to third parties, or to reduce security clearance requirements for their own DBA staff. Always Encrypted makes encryption transparent to applications. An Always Encrypted-enabled driver installed on the client computer achieves this by automatically encrypting and decrypting sensitive data in the SQL Server client application. The driver encrypts the data in sensitive columns before passing the data to SQL Server, and automatically rewrites queries so that the semantics to the application are preserved. Similarly, the driver transparently decrypts data, stored in encrypted database columns, contained in query results.
- Ability to run analytics queries concurrently with operational workloads using the same schema Not a replacement for: Extreme analytics queries performance possible using schemas customized (Star/Snowflake) and pre-aggregated cubes Data coming from non-relational sources Data coming from multiple relational sources requiring integrated analytics
- Enables query capabilities across common Hadoop distributions (HDP & Cloudera) and Hadoop file formats in Azure storage. Polybase for querying & managing non-relational Hadoop and relational data Allows leveraging existing SQL skills and BI tools Supports multiple non-relational file formats Improved time-to-insights & simplified ETL
- Introducing the concept of a DWU – which is a pivot of a SQL DB DTU. A DTU is a measure of transactions (optimized for OLTP transactions) while a DWU is a measure of query duration (optimized for DW insights). DWUs are a defined measure of performance (tested against the TPC-H dataset) that indicate relative performance to a customer in terms of scan, load, and table copy (shuffle) rate. This presents to a customer as a measure of the time to insight. For example, given the rate of 3.3M rows/sec for 100 DWU, a customer could expect that a scan of a 1B LineItem table in TPC-H would take roughly 297 seconds. An increase of 4x to 400 DWUs would reduce the time of the same query to 74 seconds – providing near linear scale. As a metric of comparison, Redshift offers 4 different SKUs (dense storage large/8xlarge and dense compute large/8xlarge) with varying fixed scan rates. With SQL DW, a customer can chose to balance the cost of DWU (compute) with the time to insight by simply sliding the DWUs up for a duration, executing a query, and then returning to the previous level. This puts the power of computation in the customers hands and away from Amazon choosing only four SKUs that are optimized for their datacenter HW.
- Comme tu l'as dit JP, on a vu énormément de services de données Azure. Je pense à Azure Data Lake, Azure SQL Datawarehouse, Azure Machine Learning. A ça s'ajoute ce qui était déjà existant comme les blob storage ou les tables dans SQL Azure. Et à aucun moment on a parlé d'un outil pour faire communiquer tout ça. Jusqu'à maintenant, on avait le choix entre code C# et code C#, on a maintenant à disposition un nouveau service de données en GA depuis août : Azure Data Factory JP : OK, tu nous en dis un peu plus DJ : Slide avec un beau dessin avec description du produit Alors voila, mes données sources je dois pouvoir les récupérer, faire des transformations, pourquoi pas un peu d’analyse puis les rendre disponibles pour mes outils de reporting. Et bien tout ça je peux le faire directement avec Azure Data Factory, et entièrement dans le cloud. JP : Ça ressemble à Data flow dans SSIS ? DJ : De loin seulement. Déjà pour commencer on a pas d’outil dédié, on travaille sur le portail Azure. Et le produit est simple d’utilisation, on travaille avec 4 objets, le tout en JSON On montre le Pipeline, les objets JP : Alors, comment ça marche, on crée un Job dans un Azure SQL Agent ? DJ : Non c’est encore plus simple au sein des pipelines je configure des plages de fonctionnement. Dès lors, dès que les datasets d’entrée sont prêts le processus se lance. Ce qui est bien c’est qu’on a pas seulement des transformations de base sur la donnée. Ce qui vient à l’esprit là, c’est du batch Azure Machne Learning ou du lancement de requêtes Pig/Hive sur HDInsight. JP : OK, c'est dans le cloud mais tu connais mon intérêt pour l'hybride et les Gateway en général. DJ : et c’est un intérêt partagé. Le grand public n’aurait pas compris qu’on puisse exclusivement récupérer des données dans le Cloud. Comme tu l’as vu en plus, cet outil doit s’intégrer dans un process d’entreprise, je dois donc pouvoir attaquer mes sources de données OnPrem. La réponse est simple, et je sais que tu aimes le produit en plus, c’est la Data Management Gateway. En plus son installation et sa configuration sont implémentées dans Azure Data Factory.