Oracle DBA

1. What is Exadata?

Exadata is a high-performance engineered system designed and produced by Oracle Corporation. It combines software and hardware components to provide a complete, optimized solution for running Oracle Database workloads. Exadata is specifically designed to enhance the performance and scalability of Oracle Database by leveraging a massively parallel architecture.

The hardware components of an Exadata system include database servers, storage servers, and InfiniBand networking. The database servers are powerful machines that run Oracle Database software and handle the processing of database operations. The storage servers provide high-capacity and high-performance storage for the database. InfiniBand networking enables fast communication and data transfer between the servers.

Exadata also incorporates specialized software, such as the Oracle Exadata Database Machine software and the Oracle Exadata Storage Server software. These software components are tightly integrated with Oracle Database and provide additional optimizations for performance, availability, and manageability.

2. What are key benefits of Exadata?

1. Improved Performance: Exadata leverages its specialized hardware and software components to deliver high-performance and low-latency database operations. It utilizes smart algorithms, flash caching, and advanced compression techniques to accelerate data processing and minimize response times.
2. Scalability: Exadata is designed to scale both vertically and horizontally. It can be expanded by adding more database and storage servers to meet the increasing demands of data-intensive workloads.
3. High Availability: Exadata incorporates redundant components and fault-tolerant features to ensure high availability of the database. It includes features like Automatic Storage Management (ASM) mirroring and Oracle Real Application Clusters (RAC) to minimize downtime and provide continuous access to data.
4. Consolidation: Exadata enables database consolidation by allowing multiple databases to be hosted on a single system. This consolidation can lead to better resource utilization, cost savings, and simplified management.
5. Advanced Analytics: Exadata includes support for advanced analytics and machine learning workloads through integration with Oracle’s software stack. It enables efficient execution of complex queries and analytics operations on large datasets.

3. What are the key component of Exadata?

The key components of an Oracle Exadata system include:

1. Database Servers: Exadata systems include multiple database servers, which are powerful machines running Oracle Database software. These servers handle the processing of database operations, including query execution, data storage, and transaction management.
2. Storage Servers: Exadata systems incorporate storage servers that provide high-capacity and high-performance storage for the database. These servers are equipped with both hard disk drives (HDDs) and solid-state drives (SSDs) to accommodate a combination of large data storage and fast access to frequently accessed data.
3. InfiniBand Networking: Exadata systems utilize high-speed InfiniBand networking technology to facilitate fast communication and data transfer between the database servers and storage servers. InfiniBand enables low-latency and high-bandwidth connectivity, allowing efficient data processing and synchronization.
4. Exadata Storage Software: The Oracle Exadata Storage Server software is a key component of Exadata. It is installed on the storage servers and provides advanced storage management and optimization capabilities. The storage software includes features such as Hybrid Columnar Compression, Smart Flash Cache, and Smart Scan, which enhance performance and reduce storage costs.
5. Exadata Database Machine Software: The Oracle Exadata Database Machine software is a specialized software stack designed to work seamlessly with Exadata hardware. It includes components like Oracle Linux, Oracle Clusterware, and Oracle Exadata-specific optimizations. This software layer integrates with Oracle Database to provide additional performance, availability, and manageability features.
6. Redundancy and High Availability Features: Exadata incorporates redundancy and high availability features to ensure continuous operation and data protection. It includes redundant components such as power supplies, disks, and network connections. Features like Automatic Storage Management (ASM) mirroring, Oracle Real Application Clusters (RAC), and Oracle Data Guard are commonly used to provide high availability and disaster recovery capabilities.
7. Exadata Storage Expansion Racks: Exadata systems can be expanded by adding additional storage servers and networking components using Exadata Storage Expansion Racks. These expansion racks allow for increased storage capacity and performance as the data requirements grow.
8. Management Tools: Oracle provides management tools, such as Oracle Enterprise Manager, specifically designed to monitor, configure, and administer Exadata systems. These tools offer comprehensive visibility into the Exadata infrastructure, performance monitoring, and automated management capabilities.

4. What are the features of Exadata?

Oracle Exadata offers several key features that enhance the performance, scalability, availability, and manageability of Oracle Database workloads. Here are some of the notable features of Exadata:

1. Smart Scan: Exadata utilizes Smart Scan technology to offload data-intensive processing to the storage layer. When executing queries, Exadata storage servers perform filtering and aggregation operations, reducing the amount of data transferred to the database servers. This minimizes network traffic and improves query performance.
2. Hybrid Columnar Compression (HCC): Exadata incorporates HCC, a compression technology optimized for data warehouse and archive workloads. HCC achieves high compression ratios by storing data in a columnar format, reducing storage requirements and improving query performance on compressed data.
3. Smart Flash Cache: Exadata leverages high-performance flash storage (SSDs) as a cache layer. The Smart Flash Cache intelligently caches frequently accessed data, allowing for faster retrieval and reducing the need to retrieve data from slower disk storage.
4. Exadata Smart Flash Logging: This feature improves database write performance by utilizing flash storage for database log writes. It accelerates log writes, reduces I/O contention, and improves overall database performance.
5. Oracle Exadata Hybrid Columnar Compression for OLTP: In addition to its benefits for data warehousing, Exadata also supports Hybrid Columnar Compression for Online Transaction Processing (OLTP) workloads. It reduces storage requirements and improves performance for transactional databases.
6. Automatic Storage Management (ASM): Exadata includes Oracle ASM, a disk management and file system solution. ASM simplifies storage administration, provides redundancy, and enables dynamic rebalancing of data across disks to maximize performance.
7. Oracle Real Application Clusters (RAC): Exadata supports Oracle RAC, which enables high availability and scalability by allowing multiple database servers to work together as a cluster. RAC provides workload balancing, failover protection, and increased database performance.
8. Smart Flash Cache Write-Back: This feature improves database write performance by caching write operations to flash storage. It enables faster write response times, reducing latency and improving overall system performance.
9. Exadata Live Migration: Exadata systems support live migration, allowing for seamless movement of database workloads between different Exadata systems or within the same system. This feature enables workload balancing, maintenance, and system upgrades without disrupting database operations.
10. Oracle Exadata Cloud Service: Oracle also offers Exadata as a cloud service, providing the benefits of Exadata’s performance and scalability in a cloud environment. Customers can leverage Exadata Cloud Service to run their Oracle Database workloads in the cloud with high performance and simplified management.

These features, along with the tight integration between hardware and software components, contribute to the overall performance, scalability, availability, and manageability advantages of Oracle Exadata.

5. What are Exadata sizing available?

Oracle offers different models and configurations of Exadata systems to cater to various workload requirements. The available Exadata sizing options have typically included:

Quarter Rack: This is the smallest configuration, consisting of a quarter rack with a specific number of database servers and storage servers. It provides a starting point for smaller workloads or for organizations looking to scale up gradually.

Half Rack: The half rack configuration includes twice the compute and storage capacity of the quarter rack. It offers higher performance and storage capacity, suitable for medium-sized workloads.

Full Rack: The full rack is the largest configuration and provides the maximum compute and storage capacity available in a single Exadata system. It is designed for large-scale and mission-critical workloads with high performance, scalability, and availability requirements.

It’s important to note that Exadata configurations can vary over time as Oracle releases new models and updates. It’s recommended to consult with Oracle sales or a certified Oracle Exadata partner to get the most up-to-date information on available Exadata sizing options, configurations, and pricing specific to your requirements.

6. What is a storage index and how it works?

The Oracle Exadata Storage Index is a unique feature of Oracle Exadata that improves query performance by eliminating the need to read unnecessary data during query execution. It works by storing summary information about the data blocks on storage servers, allowing for more efficient query processing.

Here’s how the Oracle Exadata Storage Index works:

1. Summary Information: The Exadata Storage Index stores summary information about the minimum and maximum values of selected columns within each data block on the storage servers. This summary information is stored in memory on the storage servers.
2. Query Filtering: When a query is executed, the Exadata Storage Index is consulted to determine if any data blocks can be skipped entirely based on the query’s filtering conditions. The Storage Index compares the query filter predicates with the summary information it has stored.
3. Data Block Elimination: If the summary information indicates that a data block does not contain any data that satisfies the query’s filtering conditions, that block is skipped, and the storage server does not need to read it. This eliminates the need to transfer unnecessary data across the network to the database servers, reducing I/O and improving query performance.
4. Intelligent Storage Offloading: The Exadata Storage Index works in conjunction with other Exadata features, such as Smart Scan and Predicate Offloading. When a query is processed, the Storage Index is used to offload filtering operations to the storage servers. This reduces the amount of data that needs to be transferred to the database servers for further processing.
5. Adaptive Sampling: The Exadata Storage Index adapts dynamically to changes in data distribution. It uses adaptive sampling techniques to ensure that the summary information remains accurate and up to date, even as the data changes over time.

The Exadata Storage Index can significantly improve the performance of queries that involve large tables and require scanning a substantial amount of data. By eliminating the need to read unnecessary data blocks, it reduces I/O and network overhead, leading to faster query execution times.

It’s important to note that the Exadata Storage Index is automatically managed and maintained by the Exadata storage servers. There is no need for manual configuration or intervention. The Storage Index is an integral part of the Exadata architecture, designed to optimize query performance and improve overall system efficiency.

7. What is ASR?

Auto Service Request (ASR) is a proactive service automation feature provided by Oracle for Exadata systems. It helps to streamline the process of diagnosing and resolving hardware faults or issues by automatically opening service requests with Oracle Support when certain predefined conditions or events occur.

Here’s how Auto Service Request works in Oracle Exadata:

1. Monitoring and Detection: The Exadata system continuously monitors various hardware components, including servers, storage, networking, and power distribution units. It collects diagnostic information and detects any anomalies or potential issues.
2. Event Notification: When a hardware fault or issue is detected, the Exadata system generates an event notification. These events could include hardware failures, critical system alerts, environmental issues, or performance-related conditions.
3. Service Request Generation: The event notification triggers the Auto Service Request feature, which automatically creates a service request (SR) with Oracle Support. The SR includes details about the detected event, system configuration, diagnostic information, and any relevant logs.
4. Service Request Submission: The generated service request is submitted to Oracle Support, providing them with the necessary information to initiate the troubleshooting and resolution process.
5. Oracle Support Engagement: Oracle Support receives the service request and begins working on diagnosing and resolving the reported issue. They can access the collected diagnostic information, review logs, and communicate with the customer to resolve the problem efficiently.
6. Proactive Support: Auto Service Request helps Oracle Support to proactively engage with customers by initiating the service request before the customer might even be aware of the issue. This proactive approach aims to reduce downtime, speed up problem resolution, and improve overall system availability.

Auto Service Request simplifies and expedites the support process for Exadata systems, enabling quicker identification and resolution of hardware-related problems. By automating the service request generation and leveraging real-time diagnostics, it enhances the overall support experience and reduces the time to resolve hardware issues.

It’s important to note that the Auto Service Request feature requires appropriate configuration and integration with Oracle Support. Customers need to have the necessary service agreements and system setup to utilize this feature effectively.

8. What is flash cache in and how it works?

In Oracle Exadata, the Flash Cache is a key component that contributes to the high performance and low latency of the system. It is a layer of solid-state drives (SSDs) used as a cache between the storage servers and the database servers. The Flash Cache in Exadata works in conjunction with other Exadata features to optimize data access and improve overall query performance.

Here’s how the Flash Cache works in Oracle Exadata:

1. Data Caching: The Flash Cache in Exadata acts as a read and write cache for frequently accessed data. It stores both recently accessed data and data predicted to be accessed in the near future based on intelligent algorithms.
2. Smart Flash Cache: Exadata incorporates a feature called Smart Flash Cache, which dynamically manages the Flash Cache. It leverages the Exadata Storage Server software to determine which data to cache based on the workload characteristics and access patterns.
3. Read Acceleration: When a read request is issued, Exadata checks if the requested data is present in the Flash Cache. If the data is found in the cache, it can be served directly from the high-speed SSDs, bypassing the slower disk storage. This significantly reduces read latency and improves overall query performance.
4. Write Acceleration: Exadata Flash Cache also accelerates write operations. When data is written to the storage servers, it can be temporarily cached in the Flash Cache before being written to the disk-based storage. This allows for faster write response times and reduces the I/O load on the disk storage, improving system performance.
5. Intelligent Caching: Exadata’s Smart Flash Cache is designed to adapt dynamically to changing workloads. It analyzes the access patterns and identifies frequently accessed data blocks or objects, maximizing cache utilization and performance.
6. Integration with Smart Scan: The Flash Cache works seamlessly with Exadata’s Smart Scan feature, which offloads filtering and processing tasks to the storage servers. The combination of Smart Scan and Flash Cache allows for efficient data retrieval, minimizing I/O and network traffic.

By leveraging the Flash Cache in Exadata, organizations can experience significant performance improvements for both read and write operations. The intelligent caching and integration with other Exadata features make the Flash Cache an integral part of the overall performance optimization strategy in Oracle Exadata systems.

9. What is EHCC?

In Oracle Exadata, EHCC stands for Exadata Hybrid Columnar Compression. these compression options are associated with different compression levels and are used to achieve varying degrees of data compression. Here’s an overview of each compression option:

Query Low Compression (QLC): QLC is a compression option in Exadata that provides a moderate level of compression while still prioritizing query performance. It offers a balance between storage savings and query response times by employing a compression algorithm that allows for faster decompression during query execution. QLC is suitable for workloads where data access speed is important, but some storage savings are still desired.

Query High Compression (QHC): QHC is a compression option that delivers high levels of data compression for Exadata systems. It achieves significant storage savings by compressing data at a high granularity, down to individual columns. QHC is particularly useful for data warehousing workloads with large data sets, as it reduces storage requirements, improves query performance by reducing I/O, and maximizes cache utilization.

Archive High Compression (AHC): AHC is a compression option optimized for archiving data in data warehousing environments. It provides even higher compression ratios than QHC, making it ideal for long-term data retention and archiving purposes. AHC enables organizations to reduce storage costs by efficiently compressing and storing historical or infrequently accessed data while still maintaining access when needed.

Archive Low Compression (ALC): Archive Low Compression is another compression option that provides a lower level of compression compared to AHC. It offers a compromise between storage savings and query performance, similar to QLC but with a focus on archiving and infrequently accessed data. ALC may be suitable for workloads where storage optimization is desirable but query response times are not as critical.

The choice of compression option depends on the specific requirements of your workload, including the balance between storage savings and query performance that you need to achieve. It’s recommended to test and evaluate different compression options to determine the most suitable one for your specific use case.

10. What is offloading and how it works?

Offloading, in the context of Oracle Exadata, refers to the process of pushing certain database operations down to the Exadata storage servers for execution. Offloading takes advantage of the high-performance capabilities of the Exadata storage servers to perform data-intensive processing tasks closer to the data, reducing the amount of data transferred across the network and improving overall query performance.

Here’s how offloading works in Oracle Exadata:

1. Smart Scan: The key component of offloading is the Smart Scan feature. When a SQL query is executed, Exadata storage servers are capable of intelligently scanning and filtering the data at the storage layer before sending it back to the database servers. This eliminates the need to transfer large volumes of data over the network.
2. Column Projection: During a Smart Scan, Exadata storage servers only retrieve the necessary columns specified in the SQL query. This minimizes the amount of data transferred and processed, reducing network overhead and improving performance.
3. Predicate Filtering: Exadata storage servers can perform predicate filtering at the storage layer, removing rows that do not satisfy the query’s filtering conditions. This further reduces the amount of data transferred to the database servers, resulting in faster query execution.
4. Compression Offloading: Exadata storage servers can also offload the decompression process when retrieving compressed data. Instead of transferring compressed data to the database servers, the storage servers can perform decompression on-the-fly, reducing network traffic and improving query performance.
5. Join Processing: In addition to scan and filtering operations, Exadata storage servers can offload join processing tasks. This involves performing join operations between tables at the storage layer, reducing the data transferred between storage and database servers.

By offloading these data-intensive operations to the Exadata storage servers, the overall query processing is accelerated, resulting in improved performance. Offloading minimizes network traffic, reduces I/O bottlenecks, and leverages the parallel processing capabilities of the Exadata storage servers, making it a key feature for achieving high performance in Exadata systems.

It’s important to note that not all database operations can be offloaded. Offloading is most effective for operations involving large data sets and full table scans, where the benefits of minimizing data transfer and utilizing the Exadata storage server capabilities are most significant.

11. What is the difference between cellcli and dcli?

cellcli (Cell Command Line Interface):cellcli is the primary command-line tool used to manage individual Exadata storage cells. It provides a comprehensive set of commands for performing storage-related operations, such as managing disks, creating and managing grid disks, configuring cell network interfaces, monitoring performance, and more. CellCLI operates on a single cell at a time and requires direct access to the specific storage cell being managed.

dcli (Distributed Command Line Interface):dcli is a distributed command-line tool designed to manage multiple Exadata storage cells simultaneously. It is used for executing commands across a group of storage cells, enabling administrators to perform actions on multiple cells in parallel. DCLI is particularly useful for performing tasks that need to be executed consistently across multiple cells, such as patching, configuration changes, and health checks. It provides a way to automate administrative tasks by scripting commands and executing them across multiple cells.

12. What is IORM and what is its role in Exadata?

IORM stands for I/O Resource Manager, and it plays a crucial role in Oracle Exadata environments. It is a feature designed to manage and prioritize the allocation of I/O (Input/Output) resources within an Exadata storage system. The primary purpose of IORM is to ensure that different workloads running on the Exadata Database Machine get the necessary I/O resources to meet their performance requirements and prevent any single workload from monopolizing the system resources.

Here are the key aspects and functions of IORM in Exadata:

1. Workload Management: IORM allows you to define database resource plans that specify the allocation of I/O resources to different databases or database services running on the Exadata system. This enables you to prioritize and control the I/O resources consumed by various workloads based on their importance and performance requirements.
2. Performance Objectives: With IORM, you can assign performance objectives to different databases or database services, defining the maximum and minimum I/O limits that can be consumed by each workload. This helps ensure that critical workloads receive the necessary I/O resources while preventing resource contention and maintaining system stability.
3. Resource Allocation: IORM monitors the I/O activity of the databases and services on the Exadata system and dynamically allocates the available resources based on the defined resource plans and performance objectives. It uses a sophisticated algorithm to manage I/O requests and prioritize them according to the configured rules.
4. I/O Resource Metrics: IORM collects and maintains statistics about I/O performance and resource consumption for each database and service. These metrics are used to make intelligent decisions regarding resource allocation and to provide visibility into the I/O usage patterns and performance characteristics of the workloads.

By effectively managing I/O resources, IORM helps ensure predictable and consistent performance for different workloads running on an Exadata Database Machine. It enables you to balance the resource allocation, prevent resource contention, and optimize the overall system performance based on the specific needs of your database workloads.

13. Difference between wright-through and write-back flash cache mode?

The difference between write-through and write-back flash cache modes lies in how write operations are handled in the flash cache of an Oracle Exadata system:

Write-Through Flash Cache Mode:

In write-through mode, when a database write operation occurs, the data is written simultaneously to both the database buffer cache in memory and the flash cache.

The write is considered complete only when the data is written to both locations. This ensures that the data is durable and consistent, as it is immediately persisted in both the buffer cache and the flash cache.

Write-through mode provides high data integrity and minimizes the risk of data loss or corruption in the event of a system failure or power outage. However, it may introduce additional latency due to the synchronous write operations to both the buffer cache and flash cache.

Write-Back Flash Cache Mode:

In write-back mode, when a database write operation occurs, the data is written to the database buffer cache in memory first, and the acknowledgment is immediately sent back to the database application.

After the acknowledgment, the data is asynchronously written from the buffer cache to the flash cache in the background. This means that the write operation is considered complete as soon as it is written to the buffer cache, providing faster response times to the application.

Write-back mode can improve write performance by reducing the latency introduced by synchronous writes. However, it introduces a slight risk of data loss in the event of a system failure or power outage before the data is written from the buffer cache to the flash cache.

In summary, write-through mode ensures immediate data durability and consistency by synchronously writing data to both the buffer cache and flash cache, while write-back mode improves write performance by asynchronously writing data to the flash cache after acknowledging the write to the buffer cache. The choice between these modes depends on the specific requirements of the application in terms of data integrity, performance, and tolerance for potential data loss.

14. What are the steps to create DBFS?

1. Create Directory
2. Create Tablespace on a database that you are going to use for DBFS
3. Create a user for DBFS
4. Grant required privileges to a created user
5. Now connect to the database with the created user
6. Create dbfs filesystem by invoking dbfs_create_filesystem_advanced
7. Mount file system by starting dbfs_client.

15. What is a Cell and Grid Disk?

In the context of Oracle Exadata, “cell disk” and “grid disk” are terminologies associated with the storage architecture of an Exadata system. Here’s the difference between the two:

1. Cell Disk: A cell disk refers to a physical disk or storage device that is directly attached to an Exadata Storage Server (also known as a “cell”). Each storage server in an Exadata system typically comprises multiple physical disks, which are known as cell disks. These cell disks provide the underlying storage capacity for storing data in the Exadata system.
2. Grid Disk: A grid disk, on the other hand, is an abstraction or logical representation of storage that spans across multiple Exadata Storage Servers. It is created by combining multiple physical cell disks from different storage servers into a single entity. Grid disks are created as part of a disk group, which is a collection of physical disks pooled together to form a logical storage unit.

The key distinction is that a cell disk represents an individual physical disk attached to a single storage server, while a grid disk represents a logical disk formed by combining multiple physical disks across multiple storage servers. Grid disks provide the basis for creating various storage entities in Exadata, such as ASM (Automatic Storage Management) disk groups, which are used for storing data, redo logs, and other database-related files.

Grid disks offer advantages like improved performance and high availability as data can be spread across multiple storage servers and disks. They also enable Exadata’s unique capabilities, such as Oracle’s Automatic Data Optimization (ADO), which allows data to be automatically tiered and moved between different storage tiers based on usage patterns.

In summary, cell disks are individual physical disks attached to storage servers, while grid disks are logical abstractions formed by combining multiple cell disks from different storage servers to provide a higher-level storage entity in Exadata systems.

16. Advantages and disadvantages of rolling upgrade

Advantages of Rolling Patching:

1. Minimal Downtime: Rolling patching allows patching to be performed on individual components or servers without requiring a complete system shutdown. This minimizes the impact on the availability of the Exadata system and reduces downtime for database services.
2. High Availability: Rolling patching ensures that database services remain available during the patching process. The patch is applied to one component at a time, allowing the workload to be shifted to other components without interruption.
3. Continuous Service: Rolling patching allows the Exadata system to maintain its operational state, ensuring uninterrupted service for users and applications.
4. Flexibility: Rolling patching provides flexibility by allowing different components to be patched at different times. This enables administrators to schedule and prioritize patching based on specific requirements and considerations.

Disadvantages of Rolling Patching:

1. Extended Patching Duration: Since rolling patching is performed on individual components sequentially, it may take longer to complete the patching process compared to non-rolling patching where all components are patched simultaneously.
2. Increased Complexity: Rolling patching involves managing the patching process across multiple components, which requires careful coordination and monitoring to ensure that each component is successfully patched without issues or disruptions.
3. Potential for Compatibility Issues: As the rolling patching process involves a mix of patched and non-patched components, there is a possibility of compatibility issues arising between different versions of software and firmware. Careful planning and testing are required to mitigate these risks.

17. Advantages/Disadvantages of Non-Rolling Upgrade

Advantages of Non-Rolling Patching:

1. Simultaneous Patching: Non-rolling patching allows for all components of the Exadata system to be patched simultaneously. This can result in a faster patching process compared to rolling patching.
2. Reduced Complexity: With non-rolling patching, there is no need to manage the patching process across multiple components, reducing the complexity of coordination and monitoring.
3. Immediate Consistency: Non-rolling patching ensures that all components are on the same patch level, eliminating potential compatibility issues that may arise in rolling patching scenarios.

Disadvantages of Non-Rolling Patching:

1. Planned Downtime: Non-rolling patching typically requires a system shutdown during the patching process, resulting in planned downtime for database services and applications.
2. Service Interruption: During the non-rolling patching process, the Exadata system and its associated services will be temporarily unavailable, affecting the availability of the database.

It’s important to consider the specific requirements, constraints, and impact on system availability when deciding between rolling and non-rolling patching methods for an Exadata Database Machine. Careful planning, thorough testing, and adherence to Oracle’s recommended practices are crucial to ensure a successful and smooth patching process.

18. ASM parameters are responsible for Auto disk management?

In an Exadata system, the Automatic Storage Management (ASM) feature provides auto disk management capabilities for efficient storage administration. The following ASM parameters are primarily responsible for auto disk management in Exadata:

AUTO_MANAGE_MAX_ONLINE_TRIES:

1. The AUTO_MANAGE_MAX_ONLINE_TRIES parameter specifies the maximum number of attempts ASM makes to bring a disk online automatically.
2. When a disk in an Exadata system becomes temporarily unavailable or experiences issues, ASM will attempt to bring it back online automatically. This parameter determines the maximum number of retry attempts before ASM considers the disk permanently offline.

AUTO_MANAGE_EXADATA_DISKS:

1. The AUTO_MANAGE_EXADATA_DISKS parameter enables or disables automatic management of Exadata disks by ASM.
2. When set to TRUE, ASM automatically manages the Exadata disks, including their discovery, labeling, and rebalancing.
3. Setting AUTO_MANAGE_EXADATA_DISKS to FALSE disables the automatic management of Exadata disks by ASM, requiring manual intervention for disk management tasks.

AUTO_MANAGE_NUM_TRIES:

1. The AUTO_MANAGE_NUM_TRIES parameter determines the number of parallel discovery attempts ASM makes to discover Exadata disks.
2. When ASM starts, it attempts to discover the Exadata disks based on the disk discovery string specified in the ASM_DISKSTRING parameter.
3. This parameter controls the degree of parallelism for the disk discovery process, allowing ASM to concurrently search for disks across multiple Exadata storage cells.

These parameters are specifically related to auto disk management in Exadata systems, providing control over online disk retries, automatic management of Exadata disks, and parallel disk discovery. They contribute to the efficient and automated management of disks within the Exadata storage infrastructure.

19. What is LIBCELL?

LIBCELL, also known as Oracle Database Kernel Library for Exadata, is a software library that provides an interface between the Oracle Database kernel and the Exadata Storage Server (cell) software. It is a critical component of the Oracle Exadata Database Machine architecture.

LIBCELL is responsible for enabling various Exadata-specific optimizations and features, such as Smart Scans, Storage Indexes, Hybrid Columnar Compression, and Offloading. It serves as the communication layer between the Oracle Database instance running on the compute nodes and the storage cells in an Exadata system.

Here are some key points about LIBCELL:

1. Smart Scans: LIBCELL plays a vital role in facilitating Smart Scans, which allow the database to offload data-intensive operations to the storage cells. Smart Scans optimize queries by processing and filtering data at the storage layer, significantly reducing the amount of data transferred over the network and improving query performance.
2. Storage Indexes: LIBCELL is involved in the implementation of Storage Indexes, a feature that speeds up data retrieval by maintaining metadata about the minimum and maximum values of storage regions. Storage Indexes help skip unnecessary I/O operations by identifying regions of data that do not satisfy query predicates.
3. Hybrid Columnar Compression (HCC): LIBCELL supports Hybrid Columnar Compression, which is a compression technique specifically designed for Exadata systems. HCC compresses data at the column level, reducing storage requirements and improving query performance by minimizing I/O and maximizing data transfer rates during Smart Scans.
4. Offloading: LIBCELL enables offloading capabilities in Exadata, where computationally intensive operations such as filtering, sorting, and joining are performed on the storage cells rather than the database servers. This offloading mechanism leverages the processing power of the storage cells and reduces the workload on the database servers, improving overall system performance.

In summary, LIBCELL is a key software component in Oracle Exadata systems that facilitates the interaction between the Oracle Database kernel and the Exadata Storage Server software. It enables Exadata-specific optimizations and features, such as Smart Scans, Storage Indexes, Hybrid Columnar Compression, and Offloading, which collectively enhance the performance and efficiency of database operations in an Exadata environment.

20. Which package is used by the compression adviser utility?

The compression adviser utility in Oracle Database uses the DBMS_COMPRESSION package to provide recommendations for table compression. The DBMS_COMPRESSION package contains procedures and functions that analyze the data in a table and suggest the most appropriate compression method for achieving storage savings.

The key procedure in the DBMS_COMPRESSION package used by the compression adviser utility is:

COMPRESS_ADVISE: This procedure analyzes the data distribution and characteristics of a table and generates a report with compression recommendations. It takes input parameters such as the schema name, table name, and compression type (Basic, Advanced, Query Low, or Query High) and returns a result set containing the compression advice.

The COMPRESS_ADVISE procedure considers factors such as data distribution, data type, and compression algorithm capabilities to determine the potential storage savings and the most suitable compression method for a table.

In addition to the DBMS_COMPRESSION package, the compression adviser utility may also use other database views and functions to gather statistical information about the table and perform analysis for compression recommendations.

It’s worth noting that the compression adviser utility and the DBMS_COMPRESSION package are available in Oracle Database versions 11g and later.

21. What is the difference between smart scan and smart flash scan?

In the context of Oracle Exadata, “Smart Scan” and “Smart Flash Scan” are specific terms related to the Exadata storage architecture and optimization features. Here’s the difference between the two:

1. Smart Scan: Smart Scan is a feature in Oracle Exadata that enables the database server to offload parts of the query processing to the storage servers. When a query is executed, the Exadata storage servers can process and filter the data directly on the storage layer, reducing the amount of data sent over the network to the database server. Smart Scan leverages Exadata’s advanced storage capabilities, such as Exadata Storage Indexes, Predicate Filtering, and Column Projection, to perform efficient data retrieval and minimize data movement between storage and compute nodes.
2. Smart Flash Scan: Smart Flash Scan is an extension of the Smart Scan feature that specifically applies to Exadata’s flash storage. In an Exadata system, the storage servers can comprise a combination of both disk-based and flash-based storage. When a query is executed, Smart Flash Scan optimizes the scanning and retrieval of data from the flash-based storage cells. It leverages the high-performance characteristics of flash memory to speed up data access and improve overall query performance.

In summary, while Smart Scan is a general term for offloading query processing to the storage layer in Oracle Exadata, Smart Flash Scan is a specific implementation of Smart Scan that focuses on optimizing data retrieval from flash-based storage cells. Both features contribute to the overall performance and efficiency of Exadata systems by minimizing data movement and processing at the database server level.