Binary load limitations
This section describes current limits within Amazon DynamoDB or no limit, in some cases. Each limit listed below applies on a per-region basis unless otherwise specified. For any table or global secondary index, the minimum settings for provisioned throughput are 1 read capacity unit and 1 write capacity unit. AWS places some default binary load limitations on the throughput you can provision. These are the limits unless you request a higher amount. To request a service binary load limitations increase see https: Per table — 40, read capacity units and 40, write capacity units.
Binary load limitations account — 80, read capacity units and 80, write capacity units. Per table — 10, read capacity units and 10, write capacity units.
Per account — 20, read capacity units and 20, write capacity units. All the account's available throughput can be applied to a binary load limitations table or across multiple tables. The binary load limitations throughput limit includes the sum of the capacity of binary load limitations table together with the capacity of all of its global secondary indexes. In the AWS Management Console, you can see what your current provisioned capacity is in a given region and make sure you are not too close to the limits.
If you increased your default limits, you can use the DescribeLimits operation to see the current limit values. In a single call, you can increase the provisioned throughput for a table, for any global secondary indexes on that table, or for any combination of these. The new settings do not take effect until the UpdateTable operation is complete.
You cannot exceed your per-account limits when you add provisioned capacity, and DynamoDB will not permit you to increase provisioned capacity extremely rapidly. Aside from these restrictions, you can increase the provisioned capacity for your tables as high as you need.
For more information about per-account limits, see the preceding section Provisioned Throughput Default Limits. A decrease is allowed up to binary load limitations times any time per day. A day is defined according to the GMT time zone. Additionally, if there was no decrease in the past hour, an additional decrease is allowed, effectively bringing the maximum number of decreases in a day to 27 times 4 decreases in the first hour, and 1 decrease for each of the subsequent 1-hour windows in a day.
However, if a single request decreases the throughput for a table and a GSI, it will be rejected if either exceeds the current limits. A request will not be partially processed. A table with a GSI, in the first 4 hours binary load limitations a day, can be modified as follows:.
At the end of that same day the table and the GSI's throughput can potentially be decreased a total of 9 times each. There is no practical limit on a table's size. Tables are unconstrained in terms of the number of items or the number of binary load limitations. You can define a maximum of 5 local secondary indexes and 5 global secondary indexes per table. You can project a total of up to 20 attributes into all of a table's local and global secondary indexes.
This only applies to user-specified projected attributes. Binary load limitations you project the same attribute name into two different indexes, this counts binary load limitations two distinct attributes when determining the total.
The minimum length of a partition key value is 1 byte. The maximum length is bytes. There is no practical limit on the number of distinct partition key values, for tables or for secondary indexes.
The minimum length of a sort key value is 1 byte. In general, there is no practical limit on the number of distinct sort key values per partition key value. The exception is for tables with local secondary indexes. With a local secondary index, there is a limit on item collection sizes: For every distinct partition key value, the total sizes of all table and index items cannot exceed 10 GB.
This might constrain the number of sort keys per partition key value. For more information, see Item Collection Size Binary load limitations. Names for tables and secondary indexes must be at least 3 characters long, but no greater than characters binary load limitations. In general, an attribute name must be at least 1 character long, but no greater than 64 KB long.
The exceptions are listed below. These attribute names must be no greater than characters long:. The names of any user-specified projected attributes applicable only to local secondary indexes. These attribute names must be encoded using UTF-8, and the total size of each name after encoding cannot exceed bytes. The length of a String is constrained by the maximum item size of KB.
Strings are Unicode with UTF-8 binary encoding. A Number can have up to 38 digits of precision, and can be positive, negative, or zero. If number precision is important, binary load limitations should pass numbers to DynamoDB using strings that binary load limitations convert from a number type. The length of a Binary is constrained by the maximum item size binary load limitations KB. Applications that work with Binary attributes must encode the data in Base64 format before sending it to DynamoDB.
Upon receipt of the data, DynamoDB decodes it into an unsigned byte array and uses that as binary load limitations length of the attribute. The maximum item size in DynamoDB is KB, which includes both attribute name binary length UTF-8 length and attribute value lengths again binary length. The attribute name counts towards the size limit. For example, consider an item with two attributes: The total size of that item is 23 bytes. For each local secondary index binary load limitations a table, there binary load limitations a KB limit on the total of the following:.
The size of the local secondary index entry corresponding to that item, including its key values and projected attributes. There is no limit on the number of values in a List, a Map, or a Set, as long as the item containing the values fits within the Binary load limitations item size limit. However, empty Lists and Maps are allowed. The maximum length of any expression string is 4 KB.
The maximum length of any single expression attribute name or expression attribute value is bytes. For example, name is five bytes;: The maximum length of all substitution variables in an expression is 2 MB. The maximum number of operators or functions allowed in an UpdateExpression is The binary load limitations number of operands for the IN comparator is DynamoDB does not prevent you from using names that conflict with reserved words.
However, if you use a reserved word in an expression parameter, you must also specify ExpressionAttributeNames. For more information, see Expression Attribute Names. Do not allow more than two processes to read from the same DynamoDB Streams shard at the same time.
Exceeding this limit can result in request throttling. The provisioned throughput limits also apply for DynamoDB tables with Streams enabled. For more information, see Provisioned Throughput Default Limits. A DAX cluster consists of exactly 1 primary node, and between 0 and 9 read replica nodes.
In general, you binary load limitations have up to 10 CreateTableUpdateTableand DeleteTable requests running simultaneously in any combination. The only exception is when you are creating a table with one or more secondary indexes. You can have up binary load limitations 5 such requests running at a time; however, if the table or index specifications are complex, DynamoDB might temporarily reduce the number of concurrent requests below 5.
A single BatchGetItem operation can retrieve a maximum of items. The total size of all the items retrieved cannot exceed 16 MB. The total size of all the items written cannot exceed 16 MB. You can expect throttling errors if you call it more than once in a minute. The result set from a Query is limited to 1 MB per call. You can use the LastEvaluatedKey from the query response to retrieve more results. The result set from a Scan is limited to 1 MB per call.
You can use the LastEvaluatedKey from the scan response to retrieve more results. Binary load limitations is disabled or is unavailable in your browser. Please refer to your browser's Help pages for instructions. Sign In to the Console. Create a Table Step 2: Load Sample Data Step 3: Query and Scan the Data Step 5: Delete the Table Summary Node.
Delete the Table Summary. Batch Write Operations Example: Document Model Working with Items. Batch Write Operation Example:
There are many different data formats that fall under the umbrella of "numerical data" and they need not be entirely numerical in nature. The main distinction between numerical data and generic data is that the desired Wolfram Language form of the data will be represented by reals, integers, rationals, or complex numbers, rather than strings or symbols.
A typical example of numerical data might be a two-column array of floating-point numbers stored as Comma Separated Values CSV. Another common form of numerical data is time series data, which has a time stamp associated with measurements of some kind temperature, speed, pressure, etc. Here is a sample of binary load limitations time series data that is included with the Wolfram Binary load limitations. There are a number of different functions available in the Binary load limitations Language that are used to load data.
The following chart summarizes these functions and some of the advantages and disadvantages of each. For small files, and when working with new data for the first time, it is often easiest to use Import to load data. Import offers the most user-friendly interface for loading data, and in these cases the extra overhead generally is not a major concern. However, when deploying an application or working with particularly large datasets, working with a function like ReadList or BinaryReadList can greatly enhance the performance of your data loading.
As a general rule, ReadList will outperform a comparable Import operation in terms of speed and memory overhead. This is due in part to the type restriction used as a second argument in ReadListwhich allows the Wolfram Language to skip the various parsing operations used in Import which make sure that entries that should be strings are interpreted as stringsand reals and integers are used appropriately for numerical values.
This generally leads to Import being more user-friendly, but at the cost of requiring additional time and memory to do the same operation as ReadList. ReadList is simple to use in cases where a large data file has a single type such as rows and columns of reals or integers but can be more work when multiple types are used such binary load limitations when strings and reals are intermixed, or where a data file with real values has strings in the first row of each column.
FullForm indicates the source of the error being issued by ReadList. To get the desired types in the Wolfram Language, an additional processing step is required. For small datasets, it generally is more efficient to use Import for time series data, but in cases where a large amount of information is being read, the processing overhead associated with Import may make it worth the extra effort so that your data binary load limitations be handled using less binary load limitations, and binary load limitations quickly.
Not all Import operations are handled by the Wolfram Language directly, but instead rely on external converter binaries to handle the import operation. In many cases, these binaries have their own limitations on file size or may have a certain amount of overhead binary load limitations with them. This process requires additional memory, since not only the Wolfram Language but also the converter needs a copy of the file in memory to process, and then must pass the data over the Wolfram Symbolic Transfer Protocol WSTP to the Wolfram Language.
It is not uncommon to see very large amounts of memory overhead associated with importing these file formats. The following is a table of Big Data-friendly file formats those formats that involve little formatting, can be directly loaded by the Wolfram Language, and do not require much memory to load.
Accurate measures of load time and memory overhead binary load limitations important when optimizing a data-loading operation. The Wolfram Language has a number of functions available for both of these tasks, which can help you determine the best way to load binary load limitations process your binary load limitations.
There are two functions in the Wolfram Language that measure the time taken to perform a kernel operation note that these values do not include the amount of time taken to render in the front end: The biggest difference between Timing and AbsoluteTiming is that Timing measures calculation time binary load limitations the kernel, whereas AbsoluteTiming measures elapsed time in the binary load limitations.
This distinction is easiest to see in the following example. Timing measures the amount of time spent computing in the kernel, and does not count time waiting. Timing also accounts for multithreading, so if an operation only takes two seconds in elapsed time but runs on four threads, then Timing will return a calculation time of eight seconds. When reading data in, it can be useful to look at the results of Timingbut in most cases elapsed time and therefore AbsoluteTiming is a more valuable measure of speed, since the actual time taken is more likely to impact the application.
The following examples illustrate the differences in Timing and AbsoluteTiming on an example dataset. Another way of measuring timing is to manually create time stamps before and after the data loading operation, which you can see gives the same value as AbsoluteTiming which actually has higher resolution than DateList in this case. When measuring timing, it is often a good idea to put a time limit on a specific operation. TimeConstrained can be wrapped around the target function, and a time limit in seconds is specified.
Note that TimeConstrained uses the same type of elapsed time measure as AbsoluteTimingso the time limit is not based on computation time. There are two functions in the Wolfram Binary load limitations that measure memory overhead in the Wolfram Language kernel note that these values do not include front end overhead normally: As the function names suggest, MemoryInUse and MaxMemoryUsed measure how much memory binary load limitations currently being used by the kernel, and what the peak memory use for the current kernel session has been.
This means that even if a variable is cleared, there is still a copy of the data contained in that variable in the memory. When initially working with a large dataset, it can be binary load limitations to run evaluations that are taxing on system resources.
When first prototyping your application, it can be helpful to wrap evaluations in MemoryConstrained binary load limitations limit the amount of resources that are available to a particular evaluation. ByteCount results can vary from those returned by MemoryInUseas ByteCount counts each expression and binary load limitations as though it were unique; but in fact, subexpressions can be identical and therefore are shared.
The following example binary load limitations this. These results vary because a 1,element expression like x takes 8, bytes, independent of what the elements take. This is a bit machine the author is using, where a pointer is 8 bytes, so an array of 1 million pointers is 8 million bytes. All those pointers are pointing to the same expression, the String whose contents are "Sample long string ByteCount uses a simple method for computing the size of an expression: The 8, bytes returned, plus 1 million copies of the String binary load limitations, result in an estimated byte count of million.
In Wolfram System Version 9 and higher, the only real restriction on how much data you can import is how much memory is available on binary load limitations system, or more specifically, how much memory your operating system will allow any single process to utilize. That said, there are a number of factors that you should be aware of when loading data, binary load limitations how they might limit your ability to load larger datasets.
It is important to note that memory is affected by not only what is held within an binary load limitations, but also by the dimensionality of that data. For example, the following two datasets were loaded using both ReadList and Import on the same machine, each with a fresh kernel, and have the same number of elements, but require very different amounts of memory to load and store the expressions.
You will notice that the packed expressions are all the same size as are the Import and ReadList unpacked sizes for each data filebut there are large differences between the amount of memory required binary load limitations load and store the expressions based on the dimensionality of the file expressions that are long in just one dimension will generally require more binary load limitations. The previous section outlines some of the general limits when loading data into the Wolfram Language.
In this section, some of the techniques that can be used to push those limits are discussed, and work is done with particularly large datasets. The largest performance difference between ReadList and Import is the memory overhead required by the loading process, though speed can also vary widely, even on smaller datasets.
Import takes more than twice as long to load the data as ReadList in the previous example, and binary load limitations requires significantly more memory. BinaryReadList is one of the most memory- and time-efficient functions available to load data, running at what is essentially a 1-to-1 file-size-to-memory footprint. As the previous section illustrates, BinaryReadList is generally going to run significantly faster than a comparable ReadList operation when working with data that is natively in a binary format.
The first loading of the data is to read in the file as "Byte" 8-bit characters and segment the data at the end of each line using Split the ASCII character for end-of-line is These steps can be combined into a helper function to interpret data, such as the time series data used previously. When creating an application or package that will load data files, performance can often be improved by creating customized helper functions to handle or assist in data loading.
Binary load limitations functions can be used to decrease the amount of memory and time required to load a data file; often the process is as simple as replacing Import with ReadList and including appropriate options. During development you may use Import on the file to get the data in, and let the Wolfram Language handle the formatting and data interpretation. You will binary load limitations that using ReadList without any options results in a list of stringsrather than binary load limitations lists with real values and date strings.
While the RecordSeparators give the proper dimensionality for the data, the items are still both being loaded as strings. The addition of ToExpression on the second element of each list will return the desired results. These steps can then be combined into a function to import time series data of this form. This function is more than five times as binary load limitations as Import and uses a fraction of the memory required, because it is custom-tailored to this data format, rather than a generalized function like Import import of plaintext files is essentially just a very robust, generalized ReadList function that handles a wide variety of data binary load limitations, with additional post-processing.
Another benefit of creating your own data-loading function is that additional processing steps can be inserted within the loading operation, rather than having to do post-processing binary load limitations the results. This improves performance versus mapping DateList across the dataset. Large data files, especially those binary load limitations via scripts or logging systems, can sometimes take up so much room that they cannot be loaded into memory on the binary load limitations all at once.
However, this does not mean that the data is inaccessible to the Wolfram Language, and there are a number of methods binary load limitations for reading out portions binary load limitations the data from very large files.
ReadList can be used directly on large files to read in subsets of the data, if that data is at the top of the file. However, if that data is farther into the file, and it is not feasible to load in all the data before binary load limitations desired information, OpenRead and Find can binary load limitations used to locate a particular element within the data, for example, a date.
Note that Find sets the StreamPosition to the entry immediately following the information specified, so ReadList running on the stream will start at the next record, January Some applications may require you to reload data every time it is used, or may require accessing a specific list of data files.
In these cases, data loading can be heavily optimized binary load limitations using the Wolfram Language's DumpSave function. Save and DumpSave can be used to store variable values on binary load limitations file system, allowing them to be reloaded in the future, using Get.
There are a few important distinctions between Save and DumpSave. The biggest difference in terms of data storage is that Save uses a plaintext format to store the information, while DumpSave uses a binary data format which takes binary load limitations less room, and can be read more quickly. DumpSave also preserves packed arrays, which improves both speed and memory overhead.
The main limitation of DumpSave is that its encoding binary load limitations requires that the data be loaded on the same machine architecture on which it was encoded, so the files cannot be transferred between platforms.
Import user-friendlysupports many formats ReadList fastflexible control of typessupports streams BinaryReadList better performance than the two previous functions Get DumpSave most efficient loading binary load limitationspreserves packed arrays.
Import high memory overheadlimited performance ReadList not as user-friendlyworks best with plaintext formats BinaryReadList generally the least user-friendlycan involve a lot of data processing Get DumpSave files are not transferableneed to be loaded by another mechanism first. Import automatically converts the date strings for time series data.
Head verifies that the second element of each list is interpreted as a Real value. Binary load limitations contrast, ReadList returns each row as a Record. The FullForm indicates the entire row is being handled as a String. RecordSeparators only apply to Record types, not numbers. ClearSystemCache is used before each evaluation to ensure that the timing values are not influenced by cached results.
This data is composed of Real numbers between 0 and 10, with 4. This data is composed of Real numbers between 0 and 10, also with 4. ReadList here will use half the memory-constrained value as Import in the previous example. Adding "" to RecordSeparatorsalong with the default separators, gives nested lists. Modifying readTimeSeries from the previous example.
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Included in this article are the basic structures and key concepts for interacting with this file format programmatically. February Provided by: It is the part of a series of articles that introduce the binary file formats used by Microsoft Office products. Understanding Office Binary File Formats. Excel Binary File Format. The format is organized into streams and substreams. Each spreadsheet worksheet is stored in its own substream.
All of the data is contained in records that have headers, which give the record type and length. Cell binary load limitations, which contain the actual cell data as well as formulas and cell properties, reside in the cell table. String values are not stored in the cell record, binary load limitations in a shared strings table, which the cell record references.
Row records contain property information for row and cell locations. Only cells that contain data or individual formatting are stored in the substream. The recommended way to perform most programming tasks in Microsoft Excel is to use the Excel Primary Interop Assemblies. These are a set of. NET classes that provide a complete object model for working with Microsoft Excel. This article series binary load limitations only with advanced scenarios, such as where Microsoft Excel is not installed.
Records may be read or skipped by reading these values, then either reading or skipping the number of bytes specified by cb, depending on the record type specified by rt.
A record cannot exceed bytes. If the data the record applies to is larger than that, the rest is stored in one or more continue records.
For more information, see section 2. Specific byte locations within a record binary load limitations counted from the end of the cb field. The Workbook stream is binary load limitations primary stream in an. The first stream is always the Globals substream, and the rest are sheet substreams.
These include worksheets, macro sheets, chart sheets, dialog sheets, and VBA module sheets. The Globals substream specifies global properties and data in a workbook.
It also includes a BoundSheet8 record for each substream in the Workbook stream. A BoundSheet8 record gives information about a sheet substream. This includes name, location, type, and visibility. The first 4 bytes of the record, the lbPlyPos FilePointer, specifies the position in the Workbook stream where the sheet substream starts. The cell table is the part of a sheet stream where cells are stored.
It contains a series of row blocks, each of which binary load limitations a capacity of 32 rows of cells, and are filled sequentially. Each row block starts with a series of Row records, followed by the cells that go in the rows, and ends with a DBCell record, which gives the starting offset of the binary load limitations cell of each row in the block. A Row record defines a row in a sheet. This is a complex structure, binary load limitations only the first 6 bytes are needed for basic content retrieval.
These give the row index and the columns of the first cells and last cells that contain data or unique formatting in the row. All of the cells in a row block are stored after the last row in the block. There are seven kinds of records that represent actual cells in a worksheet. Most cell records begin with a 6-byte Cell structure.
The first 2 of those bytes specify the row, the next 2 bytes specify the column, and the last 2 bytes specify an XF record in the Globals substream that contains formatting information. The following records represent the different kinds of cells. Unless specified otherwise, the first 6 bytes are taken up by the cell structure, and the remaining bytes contain the value.
A Blank cell record specifies a blank cell that has no formula or value. This record type is used only for cells that contain individual formatting; otherwise, blank cells are stored in MulBlank records or not binary load limitations all.
An RK cell record contains a bit number. Excel automatically converts numbers that binary load limitations be represented in 32 bits or less to this binary load limitations for storage as a way to reduce file size. Instead of a 6-byte cell structure, the first 2 bytes specify the row and the second 2 bytes specify the column. The remaining 6 bytes define the number in an RkRec structure for disk and memory optimization.
A BoolErr cell record contains a 2-byte Bes structure that may be either a Boolean value or an error code. A Formula cell record contains both the formula and the resulting data. The value displayed in the cell is defined in a FormulaValue structure in the 8 bytes that follow the cell structure. The next 6 bytes can be ignored, and the rest of the record is a CellParsedFormula structure that contains the formula itself. The first 2 bytes give the row, and the next 2 bytes give the column that the series of blanks starts at.
Next, a variable length array of cell structures follows to store formatting information, and the last 2 bytes show what column the series of blanks ends on.
These values are referenced in the worksheet by LabelSst cell records. The first 8 bytes of the SST give the number of references to strings binary load limitations the workbook and the number of unique string values in the SST.
The rest is an array of XLUnicodeRichExtendedString structures that contain the strings themselves as arrays of characters. Bit 16 of this structure specifies whether the characters are 1 byte or 2 bytes each.
Although you could load every sheet substream indiscriminately, you gain more control and efficiency by using the BoundSheet8 records to locate just the sheets you want to read. Parsing of formulas and formatting information is beyond the scope of this article. Open the Workbook stream and scan for the first instance of a BOF record. This is the beginning of the Globals substream.
For more details, see Globals. From the BoundSheet8 record that corresponds to the substream you want to open, read binary load limitations first 4 bytes, which binary load limitations the lbPlyPos FilePointer. Go to the offset in the stream specified by the lbPlyPos FilePointer. This is the BOF record for the worksheet. Read the next record in the substream, which is the Index record, and load the array of pointers that starts at byte 16 of the Index record.
Each pointer points to the stream position of a DBCell binary load limitations. Go to the offset specified by the bytes 5—6 binary load limitations the DBCell record and read into memory all of the cell records, starting at that point and ending with binary load limitations last byte before the DBCell.
Copy the cell records to the objects that you defined in your internal data structure by record type. By using the tools that are provided in this article, simple data recovery should be within your reach.
Binary load limitations additional exploration, you can start to recover formulas, formatting information, and other metadata.
Tips for Optimizing Performance Obstructions. Using Custom Data Parts in Excel Working binary load limitations the Compatibility Binary load limitations in Excel and Excel Collapse the table of content. This documentation is archived and is not being maintained.
June 23, Applies to: The following procedure shows how to access all of the data from a worksheet. Note Specific byte locations within a record are counted from the end of the cb field. Create an internal data structure to hold the worksheet content. Define objects to represent each of the eight cell record types in memory. For each pointer in the array: Read the corresponding DBCell record. Parse the cell data. For more information, see the following resources: Is this page helpful?