Skip to main content

Decision Criteria for db_file_multiblock_readcount

DB_FILE_MULTIBLOCK_READ_COUNT is one of the parameters you can use to minimize I/O during table scans. It specifies the maximum number of blocks read in one I/O operation during a sequential scan. The total number of I/Os needed to perform a full table scan depends on such factors as the size of the table, the multiblock read count, and whether parallel execution is being utilized for the operation.

The maximum value is the operating system's maximum I/O size expressed as Oracle blocks
((max I/O size)/DB_BLOCK_SIZE).
According to Oracle, this is the formula for setting db_file_multiblock_read_count:
                                        max I/O chunk size
DB_FILE_MULTIBLOCK_READ_COUNT  =  ------------------------------------
                                        db_block_size
If you set this parameter to a value greater than the maximum, Oracle uses the maximum.
The setting of db_file_multiblock_read_count dictates how many I/O calls will be required to complete a table scan.  For example, if db_file_multiblock_read_count is set to 32, and the Oracle  block size = 8k, then a sequential scan of a 256k table can be read in one pass.  This improves the speed of the table scan and overall query performance.

The cost of setting db_file_multiblock_read_count too high is that the server will consume additional memory and may cause full table scans to be chosen by the Cost-Based Optimizer more frequently.

It does NOT "dictate how many I/O calls will be required to complete a table scan.”

 
Labels:

Comments

Post a Comment

Popular posts from this blog

Data Wrangling vs EDA

  Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more

Infrastructure limitations ESX Server 4 vs ESXI 5

Some limitations in ESX Server 4 may constrain the design of data centers:[28][29] • Guest system maximum RAM: 255 GB • Host system maximum RAM: 1 TB[28] • Number of hosts in a high availability cluster: 32 • Number of Primary Nodes in ESX Cluster high availability: 5 • Number of hosts in a Distributed Resource Scheduler cluster: 32 • Maximum number of processors per virtual machine: 8 • Maximum number of processors per host: 160 • Maximum number of cores per processor: 12 • Maximum number of virtual machines per host: 320 • VMFS-3 limits files to 262,144 (218) blocks, which translates to 256 GB for 1 MB block sizes (the default) or up to 2 TB for 8 MB block sizes.[30] However, on a VMFS Boot drive, it is usually very difficult to use anything other than 1 MB Block size [31]. With ESXI 5 there has been some changes to these limits[32] • Guest system maximum RAM: 1 TB • Host system maximum RAM: 2 TB • Number of hosts in a high availability cluster: 32 • Maximum number ...
Post a Comment
Read more