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Memory Use in DataFlex Web Applications

July 15,2020
By Stephen W. Meeley, Development


A recent Forum thread about the size of the memory footprint for WebApp processes pointed out that under certain circumstances the memory used seemed significant (in the hundreds of megabytes per process). As the conversation continued it was apparent that the topic of memory use needed clarification. The purpose of this document is to focus on how the number, type, and structure of tables in a web application impact memory use, which is by far the most significant factor in the total memory necessary per process.

Is the information in this document pertinent to your application?

Significant memory consumption does not impact all, or even many, web applications. There are a few simple aspects to consider in determining if you need to be sensitive to memory consumption and explore ways to reduce it.

  • The embedded database has limited row lengths and only uses one buffer per open table, so it is extremely unlikely you will experience significant memory consumption unless you are using an SQL backend.
  • If your application uses a relatively small set of tables and relatively small row sizes, the chances that your application will consume significant amounts of memory is also very low even with an SQL server. You may still find the information below about what consumes memory interesting, and surprisingly useful.
  • Even if your web applications consume significant amounts of memory per process, it may not be an issue that needs to be addressed if high memory use is not causing problems.

So, the bottom line is that significant memory consumption per process requires analysis in your environment to move from “that’s just how it works” to “we have a problem to deal with”. That threshold may be crossed when using SQL, having tables with very large row lengths, having many tables open in the application and loading a large enough number of processes that would all come together and exceed the memory capacity of a reasonably configured server.

To aid you in this analysis, we’ve created the AnalyzeTableMemoryUsage basic project that you can compile in any workspace. We’ll discuss this in more detail below.

So, if we’ve caught your attention, proceed...

What uses memory, and when?

The largest consumer of memory in an SQL environment is opening a table. For each table opened, memory is allocated for the data buffer (equal to the row length and statically allocated), the find cache for performance tuning (equal to the row length x the block size and statically allocated) and each data dictionary instance in the application (equal to the size of the data in the DD buffer at any given time, because it is dynamically allocated).

There are some other factors involved, like argument size and how that impacts the amount of data that moves between the data buffer and the DD buffers, but we’ll dive into that a bit later. For now, let’s stay focused on the basics:

  • Each table has one data buffer = row length
  • Each table has a configurable find cache = block size X row length
    • Note that BLOCK_SIZE has a default value of 10 (this can be set in the file) and the find cache can also be controlled at runtime via the DF_FILE_BLOCK_SIZE attribute.
  • Each DD instance has a buffer that is based on the actual data in memory, with the maximum being the row length (but also limited by argument size).

Let’s take an example of the Customer table in the examples installed with the Studio. The total row length is only 1,222 bytes. With the default block size of 10 rows, the expected memory footprint for opening this table in your application would be 13,422 bytes (row length x 11). If we add in three instances of data dictionaries for the customer table, that could use as much as 3,666 bytes (row length x number of DD instances), but that would only peak when rows that contained the maximum data were in memory. At that level, we’re still only using a total of 17,008 bytes of memory in this application for the Customer table. Even if we had 100 such tables, the projected memory use per process (for tables) would only be 100 x 17,088 bytes, which gives us a total of approximately 1.7MB.

Of course, this is not a representative table example. We ran some statistics against an application workspace provided by a developer and found that across the 74 tables used in the application, it was common to have tables with record lengths in the 17KB range (there were 7 of them) and the default memory use for each of them would be 191KB (row length x 11) and when totaled would be 1.3MB. Add in our basic assumption of three DD instances for each of those tables and you could possibly have an additional 365KB peak (but again, only if the rows in memory were of maximum defined length). In this particular workspace, the total combined row length for all 74 tables was 140KB with a projected total memory footprint of 1.5MB for table buffers and perhaps another 420KB for DD buffers (assuming 3 DDs per table and maximum length data in all) – totaling just under 2MB. Make the application even 4 times larger and we’re still not even in the 10MB range of memory needed for all the tables and data.

So how can there be reports of memory consumption in the hundreds of megabytes per process?

Enter large columns.

While every application is different and, as developers, we each have our own habits, some basics often come into play:

  • Text columns always allocate a minimum of 16KB
  • (max) columns always allocate a minimum of 16KB
    • Note that these are listed separately because, even though Text columns default to (max) data types, (max) data types can be mapped to ASCII columns as well
  • The data column in WebAppServerProps is set to 1MB when converted to Microsoft SQL Server
  • Developers, including those of us on the Data Access development team, tend to size columns on the large side "just in case" (e.g. did the data area for WebAppServerProps really need to be 1MB?)

So, it turns out the behaviors associated with the combination of large columns and block sizes, and then further multiplied by the number of processes in the web application is where some developers can start to see significant memory use in web applications.

Let’s take the example above and change each of the 7 tables that had row lengths of 17KB and assume that, upon moving to SQL, the text columns were changed to 1MB (because we can – it’s that "just in case" mentality). We now have added at least 77MB to the memory footprint (per process) and that’s just for the file and find cache buffers. Still not hundreds of megabytes per process, but you can see how very large columns can start to add up.

How can we reduce the memory footprint?

Note: The fundamentals we’ll be discussing in this section are somewhat generalized so that we don’t get caught in the minutia of different backends and data types. Everything needs to be taken into the specific context of individual instances. For instance, these factors may limit the options available in some environments:

  • Data types available (these can change even within revisions of a particular backend)
  • Data type sizes in memory (especially when Unicode types come into play)
  • Row length limitations (these are often tied to the data types used)
  • Who owns the data (converted from DataFlex or “native” and used by other applications)?
  • How much of a difference would a change make?

So, keep in mind that some of the techniques discussed below cannot be used at all, or cannot be used to the same extent or, even if used, do not result in a significant memory use reduction.

Block Size

We’ll start with block_size, because it multiplies the memory use of the underlying data structure for a very specific purpose: the find cache for each table. It is also the one aspect that can be tuned without any changes to the database itself. The only potential impact is application performance, and that can be easily tested. Remember that the validity of the find cache is temporary (only within the scope of the find_cache_timeout, which defaults to 10 milliseconds), so it usually comes into play in tight finding loops (reports, batch processes, filling selection lists, etc.).

The first question to ask about every table is "how often, and under what circumstances, do sequential finds take place?" While rare, there are some tables that never have sequential finds performed, so allocating a significant find cache is simply wasted memory. WebAppServerProps is one example of such a table – the only finds done on this table are FIND EQ. Of course, the minimum block size is 2, so we can’t reclaim all the memory allocated for the find cache for such tables, but since the default block size is 10, we can still cut down the use by 80%.

For the embedded database, WebAppServerProps is only 16KB, so the default memory use is only 176KB. But with the column change to 1MB when converted to Microsoft SQL Server, that expands to over 11MB just for that one table. We can reclaim 8MB of that by adjusting the block size to its minimum of 2.

Tables defined as "extensions", using the technique of one-to-one relationships, would be another example of a table that would probably only use FIND EQs; once a row in the "main" table is found, it uses an index value to FIND EQ for the secondary table and the extended data.

Tables that only have FIND EQs done are rare, but there can still be advantages to tuning the block size of other tables to determine if there is any significant performance lost if the find cache is reduced. You can look for tables that have large row sizes and reduce the block size and see if users would actually feel the difference (remember, only tight loops where the finds happen within the find cache timeout would be aided by the find cache) – so the best candidates for change would be lookup tables and others that are not used as much or rarely have reports or batch processes associated with their use.

The key when examining potential block size changes is not to invest time unless a block size reduction will result in significant memory use reduction. Also remember that for tables with very large row sizes, even a minor reduction in block size, say going from the default of 10 down to 6 or 7, may save significant memory per process without significantly changing the performance profile. For testing, we recommend setting the block size to the minimum value (2) and then work back up as needed. Setting it to 2 will give the earliest indication of the sensitivity of change associated with any particular table.

While block size adjustments can result in the largest reductions in memory use when applied to tables with large row sizes, they are also the only change that can have an associated performance impact.

One last technique that can be used for tables that have very large row sizes but need higher block sizes to aid the find cache for reports, batch processes, grids and lists, is to keep the block size set low in the file but set it higher dynamically when needed. You can set DF_FILE_BLOCK_SIZE outside of a Structure_StartStructure_End operation and it will immediately change the memory allocation for the find cache and then reduce it when set back to the original value, something like this…

Integer iBaseBlockSize iTempBlockSize

Move 10 to iTempBlockSize // this can be whatever value you want for desired performance

Get_Attribute DF_FILE_BLOCK_SIZE of {tableNumber} to iBaseBlockSize
// {tableNumber} is likely going to be the main DD for the operation
// all the related files will be found with Find EQs and won’t benefit from a block size adjustment.

Set_Attribute DF_FILE_BLOCK_SIZE of {tableNumber} to iTempBlockSize
// keep in mind that whatever process is responding to this request will immediately
// allocate more memory once set

// do stuff

Set_Attribute DF_FILE_BLOCK_SIZE of {tableNumber} to iBaseBlockSize
// once the operation is complete we still need to set the find cache back to its starting point
// or the memory allocated for the responding process will stay at the higher level
// the value in the file is only read upon opening the table.

Note: The above technique can be very useful when performance for a particular process would benefit from a significantly increased find cache, regardless of the memory use impact.

Data Types

Note that usually only text and binary columns come into play during this data type discussion because, by default, they are mapped to the backend data types that store large data, for instance, the (max) data types in Microsoft SQL Server. There are cases where developers may have mapped what usually would have been ASCII columns to the larger backend data types, so they can come into play as well.

We’ll use Micorsoft SQL Server data types in this section, but all backend databases have similar behaviors and we are not concerned with numeric or date data types.

DataFlex has default mappings for the data types used in the embedded database to the SQL backend:

  • ASCII = char
    • These can also be mapped to varchar, nchar and nvarchar
    • In DataFlex 20, the default is nchar to support Unicode data
  • Text = varchar(max)
    • These can also be text, ntext and nvarchar(max)
    • In DataFlex 20, the default is nvarchar(max) to support Unicode data
  • Binary = varbinary(max)
    • These can also be image and binary
    • No change in DataFlex 20 from prior revisions

As mentioned earlier, the (max) data types will always allocate a minimum of 16KB, even if they are really only for much smaller data. So, the first condition to examine are tables that use a large number of text columns set to sizes that would have fit within the data types used for ASCII data.

For instance, we examined a table that had a large number of columns (over 230) defined as nVarChar(max), but all with a DataFlex length set to 40. The resulting buffer size was over 3.5MB, and when combined with the default block size of 10, resulted in memory use of almost 40MB per process. If possible, changing all those columns to nVarChar(40) would reduce the buffer to less than 28KB and the total memory per process to around 305KB. Of course, this was an extreme case and, as mentioned, there could be reasons for the data types used that are outside of your control as the developer.

The bottom line is to build awareness of all the tables and data types used and that can be a tedious task.

We’ve created a basic project (AnalyzeTableMemoryUse.src) that can be dropped into any workspace to provide an immediate overview of every table in filelist and project the memory use impact for each one. It also looks for columns that may be unnecessarily mapped to data types that use minimum allocations and points out any tables that have very large columns in them (where block size may come into play).

Just to give an example, we ran this against a converted WebOrder sample workspace…

Workspace: Order Entry Mobile Application
Data Path: C:\DataFlex Examples\DataFlex 19.1 Examples\WebOrderMobile\Data\
Date Analyzed: 06/02/2020

Table: MSSQLDRV:OrderSystem
Columns: 4
Block Size: 10
Total Buffer Size: 73
Expected Memory Use: 803

Table: MSSQLDRV:Vendor
Columns: 8
Block Size: 10
Total Buffer Size: 137
Expected Memory Use: 1,507

Table: MSSQLDRV:Inventory
Columns: 6
Block Size: 10
Total Buffer Size: 92
Expected Memory Use: 1,012
Table: MSSQLDRV:Customer
Columns: 15
Column ’Comments’ VarChar(max), length used = 1024, recommend change to VarChar
Block Size: 10
Total Buffer Size: 32,964
Expected Memory Use: 362,604
Potential Memory Reduction: 168,949

Table: MSSQLDRV:SalesPerson
Columns: 3
Block Size: 10
Total Buffer Size: 49
Expected Memory Use: 539

Table: MSSQLDRV:OrderHeader
Columns: 9
Block Size: 10
Total Buffer Size: 96
Expected Memory Use: 1,056

Table: MSSQLDRV:OrderDetail
Columns: 6
Block Size: 10
Total Buffer Size: 60
Expected Memory Use: 660

Table: MSSQLDRV:CodeType
Columns: 3
Column ’Comment’ VarChar(max), length used = 1024, recommend change to VarChar
Block Size: 10
Total Buffer Size: 16,423
Expected Memory Use: 180,653
Potential Memory Reduction: 168,949

Table: MSSQLDRV:CodeMast
Columns: 3
Block Size: 10
Total Buffer Size: 50
Expected Memory Use: 550

Table: MSSQLDRV:WebAppSession
Columns: 9
Block Size: 10
Total Buffer Size: 173
Expected Memory Use: 1,903

Table: MSSQLDRV:WebAppUser
Columns: 5
Block Size: 10
Total Buffer Size: 92
Expected Memory Use: 1,012

Table: MSSQLDRV:WebAppServerProps
Columns: 11
Block Size: 10
Total Buffer Size: 1,048,741
Expected Memory Use: 11,536,151

Total Expected Memory Use: 12,088,450
Potential Memory Reduction: 2,027,388

Tables to Examine for Further Optimization:
MSSQLDRV:WebAppServerProps / 11,536,15

Any table with very large columns will be listed for further examination, so you can easily see which tables may benefit most from a reduction in block size. In fact, just setting the block size on WebAppServerProps to the minimum of two changes the profile to this...

Table: MSSQLDRV:WebAppServerProps
Columns: 11
Block Size: 2
Total Buffer Size: 1,048,741
Expected Memory Use: 3,146,223

Total Expected Memory Use: 3,698,522
Potential Memory Reduction: 2,027,388, with one inconsequential change (didn’t even require a recompile), we reduced the per-process memory consumption from 12MB to 3.5MB and by changing the data type used in 2 other columns could reduce it by another 2MB.

What does the future hold?

DataFlex 2020 will introduce changes as a result of Unicode use. The buffer allocations will increase because of the increased size of Unicode data, so we’re making optimizations for some data types so that, where possible, they don’t allocate their full 16KB (see the notes about text and (max) columns above).

The documentation for DataFlex 2020 will cover this in detail.

For revisions after DataFlex 2020, we will look at how the DataFlex database drivers allocate memory for cache buffers and see if we can make it more dynamic.

About AnalyzeTableMemoryUsage.src

The source file AnalyzeTableMemoryUsage.src is a basic project that developers can place in any workspace to help them estimate the memory use for a given workspace.

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