barcode generator code in c#.net The Typical Life of a Query in Software

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The Typical Life of a Query
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When the Oracle database executes a query on the Oracle Exadata Database Machine, it will use a combination of optimizations traditionally provided by the Oracle database and optimizations that are specific to the Oracle Exadata Database Machine and Oracle Exadata Storage Server Software So, as we illustrate the typical life of a query, we take into account several of the optimizations that we have already covered in this chapter In our example, the Oracle data warehouse contains seven years of customer data and requires 50TB of storage space to contain the data The data in the database columns contains a significant number of repeating values The data is partitioned in composite partitions by geographic region
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Achieving Extreme Performance with Oracle Exadata
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and date A business analyst submits a query to obtain results that will show sales to customers who purchased over $100,000 in products during the first quarter of the year over the past three years in the northeast region Using Hybrid Columnar Compression for warehouse compression, the amount of storage required for user data in the entire data warehouse is reduced from 50TB to 10TB in our example Since the data is partitioned, when the query is executed, only partitions containing data from the first quarter during the past three years is touched This reduces the volume of data that is considered to about 2TB In this stage of solving the query, the Exadata Smart Flash Cache might come into play if any of the partitions were pinned into cache Next, a Smart Scan occurs and storage indexes reduce the volume of data to just that where customers have purchased over $100,000 In this example, only about a few dozen rows of results data is obtained from the Exadata Storage Server cells If some of the data was encrypted, before the Smart Scan is executed, the Oracle Exadata Storage Server Software will first decrypt that data and then it will be expanded as this constant data flow matches the predicates Taken together, the amount of query performance speed-up can greatly exceed ten times or more what might be possible on a non-Exadata system Your performance speed-up will vary due to the number of factors that can come into play, and also will be dependent on how balanced (or unbalanced) the current server and storage platform you are comparing the Oracle Exadata Database Machine to is
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Best Practices for Data Loading
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The approach you should take for data loading will depend on a number of factors, including the cleanliness of the source data, the volume of data to be loaded, and how quickly the data must be available in the data warehouse to business analysts The Oracle database supports loading of data from external tables and via SQL*Loader, using transportable tablespaces to move data from one Oracle database to another, import and export mechanisms such as Oracle Data Pump, and insertion of individual records using database links to other Oracle databases or connectivity to other sources using gateways, ODBC connections, or Java Database Connectivity (JDBC) connections
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Deploying Data Warehouses
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Oracle has tools that can aid in this process, including the ETL tools in the Oracle Data Integrator (ODI) Suite Oracle Data Integrator and Oracle Warehouse Builder Both of these tools push transformation and load processing into the target Oracle database for better performance Where source data is clean and does not need extensive transformations, Oracle s GoldenGate can provide lighter-weight, near-real-time, simple data extraction and loading to the target Oracle database as data in the sources change Where large data volumes are going to be moved, as is typical in database loads where complex data transformations are necessary, a staging area is commonly established to store flat files prior to loading the database The Oracle database features a DBFS that can be used for this purpose and enables the Oracle database to be used as an IEEE POSIX-standards compatible file system Consisting of a PL/SQL package on the server and a Linux DBFS client, the combination enables files to be accessed easily by database applications and file-based tools to access the files stored in the database DBFS is a distributed file system where files are stored as SecureFiles LOBs The DBFS client is used to mount the file system with direct_io set where moving raw files DBFS should reside in its own database, not in the data warehouse database If complex transformations are to occur in the target data warehouse database, an additional staging layer is sometimes set up to avoid impact on business analysts as these transformations are in progress The speed of loading is, of course, directly affected by the number and speed of the CPUs used during the loading process To do this effectively, Oracle must be able to look inside raw data files and determine where rows of data begin and end For that reason, where performance is a key consideration, data is typically loaded as uncompressed, and partitions might later be compressed as they age The use of external tables is now recommended where you are loading from flat files External tables allow transparent parallelization within the Oracle database, applying transformations via SQL and PL/SQL, precluding the need for a staging layer, and providing more efficient space management (especially with partitioned tables) when parallelizing loads External tables are described using the CREATE TABLE syntax Loading data from an external table is via a CREATE TABLE AS SELECT (CTAS) or INSERT AS SELECT (IAS) statement By default, a CTAS statement will use direct path loading, key to good loading performance, since database blocks are created and written directly
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