CRACK Table Optimizer
Christian Erdmann
The optimizer supports any combination of floating-point and fixed-point data types. The original input and output data types can be kept or changed as desired. To minimize memory used, the optimizer selects the data types of breakpoints and table data as well as the number and spacing of breakpoints.
The process of generating a lookup table approximation is faster for a function handle than for a subsystem. If a subsystem can be represented by a function handle, approximating the function handle is faster.
Specify the word lengths, in bits, that can be used in the lookup table approximation based on your intended hardware. For example, if you intend to target an embedded processor, you can restrict the data types in your lookup table to native types. The word lengths must be between 1 and 128.
Whether to generate an HDL-optimized approximation. When this option is set to True, the generated approximation is a subsystem consisting of a prelookup step followed by interpolation that functions as a lookup table with explicit pipelining to generate efficient HDL code.
The Simulink block and MATLAB function lookup table approximations generated by the Lookup Table Optimizer may not be exactly numerically equivalent. However, both solution forms are guaranteed to meet all constraints specified in the optimization problem.
Whether to constrain table values to the quantized output of the function being approximated. When you set this option to False and allow off-curve table values, you may be able to reduce the memory of the lookup table while maintaining the same error tolerances or maintain the same memory while reducing the error tolerances.
When object to approximate specifies infinite input ranges and the input type is non-floating-point, the software infers upper and lower ranges during the approximation based on the range of the input data type. The resulting lookup table solution specifies the bounds that the algorithm used during the approximation, not the originally specified infinite bounds.
The Lookup Table Optimizer has an improved algorithm for lookup table value and breakpoint optimization for one-dimensional functions with flat interpolation. This enhancement can enable improved memory reduction of the optimized lookup table and faster completion of the lookup table optimization process.
Use the Lookup Table Optimizer app to generate a subsystem consisting of a prelookup step followed by interpolation that functions as a lookup table with explicit pipelining to generate efficient HDL code.
Previously, the Lookup Table Optimizer required that functions and function handles to approximate were vectorized, meaning that for each input, there is exactly one output. Starting in R2021a, lookup table optimization fully supports approximation of Simulink blocks and subsystems that only allow scalar inputs.
The Lookup Table Optimizer has an improved algorithm for lookup table value optimization for the Flat and Nearest interpolation methods when off-curve table values are allowed. This enhancement can enable faster completion of the lookup table optimization process and improved memory reduction of the optimized lookup table.
The Lookup Table Optimizer now replaces blocks being approximated by a lookup table with a variant subsystem containing the function approximation. The variant subsystem enables you to return to the original function and perform the optimization again using different optimization settings and constraints.
In previous releases, the optimization required table values to match the quantized output values of the original function being approximated. By allowing off-curve table values, you may be able to reduce the memory of the lookup table while maintaining the same error tolerances, or maintain the same memory while reducing the error tolerances.
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Depending on the requirements of a table program, it might be necessary to adjustcertain parameters for optimization. For example, unbounded streaming programs may need to ensurethat the required state size is capped (see streaming concepts).
The new lakehouse architectural design pattern provides many technical benefits like ACID support, time travel for machine learning, and better query performance. Apache Iceberg implements this pattern and provides the flexibility to further enhance based on real-world needs. Using Apache Iceberg table format requires special vacuuming like snapshot expire and orphan removal for data governance and metadata and data compaction for efficient and fast access of data.
This talk will discuss how to handle these table operations at very large scale by keeping cost in mind without compromising on data engineering, ML, analytical, and BI use cases. Automating these operations makes life easier for engineers leveraging the platforms without having to worry about how Iceberg internals work. We will share lessons learned for optimizing the streaming and batch data sets in a cost-effective and efficient way.
Another problem we have had in the past is how do we track our execution histories? We started small, we can just search off our Spark application log to figure out the history executions. But as we are getting more and more tables under the management, we decided to persist all of this optimization input and output into a table format so that we can query these results later. Below, you can see the example for the all firm removals. We can see the Spark actions on the right hand side, which probably needs a table identifier and older than timestamp to execute. On the left hand side, we model this input and output into the corresponding tables so that we can write these number of orphan files removed, Spark application IDs, into our own tables using Iceberg to start our own internal data sets.
So a patch Iceberg offers a way to stream the data into the table with a high parallelism, but oftentimes this implies that we are also running into the small files problems by compacting those small files together with sorting will improve our performance. Reducing the number of files means we spend less time on open and cost files when we are trying to do the read. Currently, the data compaction allows us to optimize on protection levels except compact of the data files we found in our table. However, the data compaction will only create larger files in a new snapshot. It does not really clean up the previously small data files. In order to handle this destructive part, we actually need to look at another Iceberg operation called the expired snapshots.
Nice. Yeah, it makes sense. Yeah, definitely excited to see the work when you contribute it. Do you have any preference on, I think, more general like normalized versus normalized as foreign vice? But I think they mean normalized more of a, the schema design. How do you guys go about the normalized versus denormalized kind of table design and decisions there?
Cool. Great. Thanks. It looks like we have time for maybe one more question here online. I think you already addressed this. Is there anything else you want to add? Your expert tables can contain deletes or updates, compaction could fail, do do a delete, you know, winning the commit race and conflicts. Anything to add on that, but you guys kind of already addressed this.
Per this Oracle doc Online Statistics Gathering for Bulk Loads, bulk loads only gather online statistics automatically when the object is empty. My process will not benefit from it as the table is not empty when I load data.
OPTIMIZE TABLE works for InnoDB (before MariaDB 10.1.1, only if the innodb_file_per_table server system variable is set), Aria, MyISAM and ARCHIVE tables, and should be used if you have deleted a large part of a table or if you have made many changes to a table with variable-lengthrows (tables that have VARCHAR, VARBINARY, BLOB, or TEXT columns). Deleted rows are maintained in alinked list and subsequent INSERT operations reuse old row positions.
You can use OPTIMIZE TABLE to reclaim the unusedspace and to defragment the data file. With other storage engines, OPTIMIZE TABLE does nothing by default, and returns this message: " The storage engine for the table doesn't support optimize". However, if the server has been started with the --skip-new option, OPTIMIZE TABLE is linked to ALTER TABLE, and recreates the table. This operation frees the unused space and updates index statistics.
If a MyISAM table is fragmented, concurrent inserts will not be performed until an OPTIMIZE TABLE statement is executed on that table, unless the concurrent_insert server system variable is set to ALWAYS.
By default, an OPTIMIZE TABLE will defragment a table. In order to use it to update fulltext index statistics, the innodb_optimize_fulltext_only system variable must be set to 1. This is intended to be a temporary setting, and should be reset to 0 once the fulltext index has been re-organized.
MariaDB 10.1.1 merged the Facebook/Kakao defragmentation patch, allowing one to use OPTIMIZE TABLE to defragment InnoDB tablespaces. For this functionality to be enabled, the innodb_defragment system variable must be enabled. No new tables are created and there is no need to copy data from old tables to new tables. Instead, this feature loads n pages (determined by innodb-defragment-n-pages) and tries to move records so that pages would be full of records and then frees pages that are fully empty after the operation. Note that tablespace files (including ibdata1) will not shrink as the result of defragmentation, but one will get better memory utilization in the InnoDB buffer pool as there are fewer data pages in use.
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