UniDAC vs FireDAC: Performance and Memory Consumption Comparison

August 29th, 2022

We have been receiving many questions about how UniDAC compares to FireDAC. To answer your questions, we took a data-backed approach and created a series of tests that aim to examine the performance and memory consumption of the two products.

Test Environment

The testing was conducted on UniDAC 9.2.1 and FireDAC in RAD Studio 11.1 (the version that comes with RAD Studio)  with default settings for both products.

A 1Gbps network was used to access the remote databases to minimize the impact of network bandwidth on test results. A computer with an SSD drive will minimize the impact of the drive’s read/write speed on tests for local databases such as MS Access and SQLite. Other characteristics of the computer on which the testing was carried out are less important since the vast majority of modern computers have multi-core processors and more than 4Gb of RAM.

Performance Test Cases

Fetch speed tests of 10,000 records in chunks of various sizes.

Operation type Description
Fetch with FetchRows=25 Measures the read speed in small blocks of 25 records
Fetch with FetchRows=1000Measures the read speed in small blocks of 1000 records
Fetch All RecordsMeasures the read speed of all the records from the table at once

Fetch speed tests for 1,000 records, one at a time.

Operation typeDescription
Fetch by 1 record without ParamsMeasures the performance of SELECT queries without parameters that return a single record
Fetch by 1 record with ParamsMeasures the performance of SELECT queries with parameters that return the same record but from different tables
Fetch by 1 record with Params (prepared)Measures the speed of multiple reads of one record from the same table. For this, a pre-prepared SELECT query with parameters is used

Update speed tests for 1,000 records.

Operation type Description
Insert/Post
Edit/Post
DML insert without Params
DML update without Params
Measure the rate of data modification in a classical way using the Insert/Post or Append/Post or Update/Post methods of TDataSet
Measure the speed of execution of INSERT and UPDATE queries without parameters
DML insert with Params
DML update with Params
Measure the speed of execution of INSERT and UPDATE queries with parameters for modifying data in different tables
DML insert with Params (prepared)
DML update with Params (prepared)
Measure the speed of execution of INSERT and UPDATE queries (with parameters) that were previously prepared

Batch update speed tests for 10,000 records.

Operation type Description
Batch Insert with BatchSize=25
Batch Update with BatchSize=25
Measure the speed of inserting and changing data in blocks of 25 records
Batch Insert with BatchSize=1000
Batch Update with BatchSize=1000
Measure the speed of inserting and changing data in blocks of 1000 records

Read/write speed tests for Blobs.

Operation type Description
Read Blob Measures the Blob read speed
Write BlobMeasures the Blob write speed

Performance test for calling stored procedures 1,000 times:

Operation type Description
StoredProc with params Measures the execution speed of stored procedures

Memory Consumption Test Cases

It’s important to know the amount of memory consumed when reading a large number of records from the database or when reading Blob fields. Therefore, we also measured memory consumption in the following test cases:

  • Fetch with FetchRows=25
  • Fetch with FetchRows=1000
  • Fetch All Records
  • Read Blob
  • Write Blob

The structure of the test database

The test database had two tables–READ_PERF and WRITE_PERF, with the following fields.

Argument Description
ID number field
F_DATETIMEdate and time
F_STR100string field 100 characters long
F_STR800string field 800 characters long
F_STR1600string field 1600 characters long
F_TEXTa large string field where you can write several megabytes of text

The choice of data types was dictated by the requirement to have them supported in all databases under test.

We added several string fields with different lengths in case one of the products would demonstrate higher performance for only long or short text fields.

Separate tables were used for reading and writing operations so we didn’t have to populate the table with test data for reading operations after performing write tests on the table.

Databases under test

We used major databases in our tests:

Disclaimer

You should not view the results of this testing as performance indicators of the database servers themselves (for example, Oracle vs SQL Server) because databases were deployed on different physical or virtual machines, with a different number of CPU cores and amount of RAM, and so on. 

MS Access

The MS Access database files were located on an SSD driver. We used Microsoft’s native ODBC driver (Microsoft Access Driver) to connect to the database. This test benchmarked the performance of accessing data in an MS Access database and how well the two products could work with ODBC drivers.

Performance test results:

Test name FireDAC,
sec
UniDAC,
sec
UniDAC is faster
than FireDAC, %
Fetch with FetchRows=250,062 0,038 63,16
Fetch with FetchRows=10000,061 0,037 64,86
Fetch All Records 0,056 0,037 51,35
Fetch by 1 record without Params 0,516 0,384 34,38
Fetch by 1 record with Params 0,543 0,414 31,16
Fetch by 1 record with Params (prepared) 0,126 0,046 173,91
Insert/Post 0,477 0,454 5,07
Edit/Post 0,515 0,501 2,79
DML insert without Params0,436 0,373 16,89
DML insert with Params0,476 0,457 4,16
DML insert with Params (prepared) 0,205 0,201 1,99
DML update without Params0,484 0,422 14,69
DML update with Params0,532 0,511 4,11
DML update with Params (prepared) 0,263 0,263 0,00
Batch Insert with BatchSize=25 1,962 1,914 2,51
Batch Insert with BatchSize=1000 1,925 1,910 0,79
Batch Update with BatchSize=25 2,564 2,542 0,87
Batch Update with BatchSize=1000 2,518 2,500 0,72
Read Blob0,0740,025196,00
Write Blob0,6050,5715,95

Memory consumption test results:

Test name FireDAC,
MB
UniDAC,
MB
UniDAC is better
than FireDAC, %
Fetch with FetchRows=25 11,227 8,512 31,90
Fetch with FetchRows=1000 12,281 8,664 41,75
Fetch All Records 11,230 7,465 50,44
Read Blob 36,738 30,668 19,79
Write Blob 31,301 31,855 -1,74

Diagrams:

UniDAC fetched data 1.5 times faster than FireDAC, whereas in Blob read tests, UniDAC was 3 times faster than FireDAC. The tests also revealed that UniDAC consumed much less memory.

For inserting and updating data, the difference between the products was less evident, but still, UniDAC was slightly faster. 

The only test where FireDAC beat UniDAC and consumed 1.74% less memory was when writing Blobs, though FireDAC was still slower than UniDAC. 

Adaptive Server Enterprise

This test compared how the products worked with SAP Adaptive Server Enterprise 16 using the native ODBC driver and the Direct mode. The Direct mode is only available in UniDAC–we used it in this test case to assess its efficiency.

We connected to the remote server through a 1 GB/s network.

Performance test results:

Test name FireDAC,
sec
UniDAC via ODBC, sec UniDAC is faster than FireDAC via ODBC, % UniDAC in Direct, sec UniDAC is faster than FireDAC in Direct, %
Fetch with FetchRows=25 0,777 0,704 10,37 0,646 20,28
Fetch with FetchRows=10000,664 0,536 23,88 0,501 32,53
Fetch All Records0,643 0,599 7,35 0,578 11,25
Fetch by 1 record without Params 2,236 1,768 26,47 1,616 38,37
Fetch by 1 record with Params 2,458 1,704 44,25 1,618 51,92
Fetch by 1 record with Params (prepared) 1,760 1,606 9,59 0,779 125,93
Insert/Post 2,404 2,223 8,14 2,099 14,53
Edit/Post 3,581 3,234 10,73 3,057 17,14
DML insert without Params 2,367 2,085 13,53 1,938 22,14
DML insert with Params2,289 2,004 14,22 1,893 20,92
DML insert with Params (prepared) 2,124 1,849 14,87 1,406 51,07
DML update without Params3,849 3,542 8,67 3,455 11,40
DML update with Params3,360 3,275 2,60 3,114 7,90
DML update with Params (prepared) 3,107 2,994 3,77 1,588 95,65
Batch Insert with BatchSize=25 18,731 18,327 2,20 5,172 262,16
Batch Insert with BatchSize=1000 20,086 19,644 2,25 3,191 529,46
Batch Update with BatchSize=25 37,222 32,629 14,08 7,728 381,65
Batch Update with BatchSize=1000 33,472 30,635 9,26 7,689 335,32
Read Blob 2,031 1,104 83,97 1,022 98,73
Write Blob 5,256 4,729 11,14 2,023 159,81
StoredProc with params
1,098
0,818

Memory consumption test results:

Test name FireDAC, MB UniDAC via ODBC, MB UniDAC is better than FireDAC via ODBC, % UniDAC  in Direct, MB UniDAC is better than FireDAC in Direct , %
Fetch with FetchRows=25 23,621 20,352 16,06 11,230 110,34
Fetch with FetchRows=1000 24,293 20,340 19,43 11,262 115,71
Fetch All Records 23,848 20,418 16,80 11,180 113,31
Read Blob 34,652 31,012 11,74 23,199 49,37
Write Blob 31,555 31,824 -0,85 23,254 35,70

Diagrams:

UniDAC demonstrated exceptional performance in fetch tests both in the Direct mode (where it surpassed by far the speed of FireDAC) and when connecting through an ODBC driver. In memory consumption tests, UniDAC consumed half as much memory as FireDAC in the Direct mode and slightly less through an ODBC connection.  

UniDAC also showed better performance for update statements in both modes. In particular, UniDAC inserted and updated data in batch operations in the Direct mode six times faster than FireDAC through an ODBC connection.     

The read and write tests for Blob objects also proved UniDAC superiority over FireDAC. The only time FireDAC exceeded UniDAC was during a memory consumption test for writing Blobs through an ODBC connection (by 0.85%), while in the Direct mode, UniDAC still beat FireDAC.

We couldn’t perform tests on stored procedures because, for some reason, FireDAC threw an error during this test whenever we connected through the ODBC driver.

The test results show that UniDAC can work with different ODBC drivers more efficiently than FireDAC. In the Direct mode, UniDAC achieves an even higher level of performance, unattainable through an ODBC connection, while also consuming less memory.

Firebird

The testing was conducted on Firebird 3 because FireDAC didn’t officially support FireDAC 4 at that time. 

We ran tests on a local server to reduce the impact of the network environment on test results. The database was located on an SSD driver.

Performance test results:

Test name FireDAC,
sec
UniDAC,
sec
UniDAC is faster
than FireDAC, %
Fetch with FetchRows=25 0,174 0,157 10,83
Fetch with FetchRows=1000 0,167 0,158 5,70
Fetch All Records 0,165 0,158 4,43
Fetch by 1 record without Params 0,602 0,869 -30,72
Fetch by 1 record with Params 0,620 0,897 -30,88
Fetch by 1 record with Params (prepared) 0,6230,171 264,33
Insert/Post 0,856 0,713 20,06
Edit/Post 1,073 0,762 40,81
DML insert without Params 0,755 0,711 6,19
DML insert with Params 0,744 0,743 0,13
DML insert with Params (prepared) 0,728 0,516 41,09
DML update without Params 0,993 0,880 12,84
DML update with Params 0,941 0,900 4,56
DML update with Params (prepared) 0,946 0,538 75,84
Batch Insert with BatchSize=25 1,416 0,926 52,92
Batch Insert with BatchSize=1000 0,689 0,530 30,00
Batch Update with BatchSize=25 1,619 0,957 69,17
Batch Update with BatchSize=1000 0,759 0,576 31,77
Read Blob 0,279 0,273 2,20
Write Blob 0,491 0,452 8,63
StoredProc with params 0,565 0,147 284,35

Memory consumption test results:

Test name FireDAC,
MB
UniDAC,
MB
UniDAC is better
than FireDAC
, %
Fetch with FetchRows=25 15,035 9,719 54,70
Fetch with FetchRows=1000 14,934 9,762 52,98
Fetch All Records 14,840 9,895 49,97
Read Blob 21,211 18,379 15,41
Write Blob 21,191 21,191 0,00

Diagrams:

UniDAC fetched small recordsets faster and consumed 1.5 times less memory than UniDAC. 

FireDAC was faster than UniDAC by 30%  in two tests involving multiple unprepared SELECT queries to fetch one record. However, UniDAC was 3.5 times faster than FireDAC in tests with prepared statements.

When updating data with the Append/Insert/Edit/Post methods or SQL statements, UniDAC was faster than FireDAC, especially in tests with prepared statements.

In batch operations, UniDAC was 1.5 times faster than FireDAC and was slightly faster in tests with Blobs.

In tests with stored procedures, UniDAC was almost 4 times faster than UniDAC.

The only test case where FireDAC outperformed UniDAC was fetching by one record with unprepared statements. In all other tests, UniDAC showed significantly better results than FireDAC.

MySQL

The testing was performed on a remote MySQL 5.7 server in a 1 GB/s network. We used the Direct mode in UniDAC and the native client library in FireDAC to access MySQL.

Performance test results:

Test name FireDAC,
sec
UniDAC,
sec
UniDAC faster
than FireDAC, %
Fetch with FetchRows=25 0,232 0,168 38,10
Fetch with FetchRows=1000 0,238 0,155 53,55
Fetch All Records 0,215 0,118 82,20
Fetch by 1 record without Params 1,488 0,726 104,96
Fetch by 1 record with Params 1,727 0,804 114,80
Fetch by 1 record with Params (prepared) 1,402 0,674 108,01
Insert/Post 2,466 0,825 198,91
Edit/Post 1,230 1,082 13,68
DML insert without Params 1,488 0,757 96,57
DML insert with Params 1,954 0,833 134,57
DML insert with Params (prepared) 1,217 0,655 85,80
DML update without Params 1,684 0,850 98,12
DML update with Params 2,283 0,941 142,61
DML update with Params (prepared) 1,548 1,119 38,34
Batch Insert with BatchSize=25 1,258 1,083 16,16
Batch Insert with BatchSize=1000 0,604 0,482 25,31
Batch Update with BatchSize=25 17,195 5,318 223,34
Batch Update with BatchSize=1000 16,199 5,034 221,79
Read Blob 1,756 0,198 786,87
Write Blob 6,952 1,185 486,67
StoredProc with params
0,992

Memory consumption test results:

Test name FireDAC,
MB
UniDAC,
MB
UniDAC is better
than FireDAC, %
Fetch with FetchRows=25 26,121 9,719 168,76
Fetch with FetchRows=1000 26,133 9,648 170,86
Fetch All Records 26,379 9,730 171,11
Read Blob 44,512 24,211 83,85
Write Blob 23,500 21,258 10,55

Diagrams:

In read tests, UniDAC was almost 2 times faster and consumed 2.5 times less memory than FireDAC.

In update tests, UniDAC was 2.5 times faster, and in batch update operations, 3 times faster than FireDAC.

UniDAC read Blobs 9 times faster and consumed 2 times less memory. In Blob write tests, UniDAC was 6 times faster than FireDAC.

We couldn’t run tests with stored procedures because we were getting an error in FireDAC.

Oracle

The testing was conducted on a remote Oracle 18c server in a 1 GB/s network. We connected to Oracle both in the Direct mode and through Oracle Client in UniDAC, and only through Oracle Client in FireDAC because FireDAC can only work through a client library.

Performance test results:

Test name FireDAC,
sec
UniDAC in OCI, sec UniDAC faster than FireDAC in OCI, % UniDAC  in Direct, sec UniDAC faster than FireDAC in Direct, %
Fetch with FetchRows=25 0,327 0,223 46,64 0,222 47,30
Fetch with FetchRows=1000 0,536 0,127 322,05 0,125 328,80
Fetch All Records 0,299 0,129 131,78 0,130 130,00
Fetch by 1 record without Params 1,262 0,889 41,96 1,230 2,60
Fetch by 1 record with Params 1,325 0,881 50,40 1,242 6,68
Fetch by 1 record with Params (prepared) 0,919 0,669 37,37 0,678 35,55
Insert/Post 4,450 4,088 8,86 4,043 10,07
Edit/Post 4,401 3,831 14,88 3,714 18,50
DML insert without Params 4,672 4,276 9,26 4,181 11,74
DML insert with Params 3,733 3,614 3,29 3,519 6,08
DML insert with Params (prepared) 3,714 3,528 5,27 3,346 11,00
DML update without Params 5,940 4,961 19,73 4,893 21,40
DML update with Params 3,626 3,581 1,26 3,579 1,31
DML update with Params (prepared) 3,452 3,423 0,85 3,427 0,73
Batch Insert with BatchSize=25 1,959 1,785 9,75 1,795 9,14
Batch Insert with BatchSize=1000 0,473 0,375 26,13 0,363 30,30
Batch Update with BatchSize=25 3,018 2,399 25,80 2,374 27,13
Batch Update with BatchSize=1000 1,016 0,799 27,16 0,806 26,05
Read Blob 0,502 0,425 18,12 0,401 5,19
Write Blob 0,994 0,867 14,65 0,831 19,61
StoredProc with params 0,736 0,686 7,29 0,690 6,67

Memory consumption test results:

Test name FireDAC, MB UniDAC in OCI, MB UniDAC is better than FireDAC in OCI, % UniDAC in Direct, MB UniDAC is better than FireDAC in Direct, %
Fetch with FetchRows=25 16,164 10,086 60,26 11,160 44,84
Fetch with FetchRows=1000 19,371 10,070 92,36 10,355 87,07
Fetch All Records 16,340 10,113 61,57 10,457 56,26
Read Blob 23,293 21,191 9,92 21,191 9,92
Write Blob 25,785 21,336 20,85 21,254 21,32

Diagrams:

When reading large volumes of data, UniDAC was 4 times faster and consumed 1.5 times less memory than FireDAC.

UniDAC also outperformed its competitor in update tests when using the Append/Insert/Edit/Post methods and INSERT/UPDATE statements. We saw similar results in batch operations where UniDAC exceeded FireDAC.

In operations with Blobs, UniDAC was better both in terms of performance and memory consumption. 

In tests with stored procedures, the difference was not so evident, but still, UniDAC was slightly better.

It’s safe to say that you won’t have to make a trade-off between performance/memory consumption and ease of deployment if you choose the Direct mode which doesn’t require any external client libraries. 

PostgreSQL

The testing was performed on a remote PostgreSQL14 server in a 1 GB/s network. We used UniDAC in the Direct mode and FireDAC with the native client library.

Performance test results:

Test name FireDAC,
sec
UniDAC,
sec
UniDAC faster
than FireDAC, %
Fetch with FetchRows=25 0,560 0,394 42,13
Fetch with FetchRows=1000 0,259 0,239 8,37
Fetch All Records 0,182 0,134 35,82
Fetch by 1 record without Params 4,505 4,128 9,13
Fetch by 1 record with Params 4,813 4,342 10,85
Fetch by 1 record with Params (prepared) 2,450 1,261 94,29
Insert/Post 1,568 1,465 7,03
Edit/Post 1,732 1,688 2,61
DML insert without Params 2,971 1,834 62,00
DML insert with Params 3,217 1,727 86,28
DML insert with Params (prepared) 1,573 1,432 9,85
DML update without Params 2,990 1,752 70,66
DML update with Params 3,617 1,552 133,05
DML update with Params (prepared) 1,580 1,521 3,88
Batch Insert with BatchSize=25 2,252 1,753 28,47
Batch Insert with BatchSize=1000 1,119 1,004 11,45
Batch Update with BatchSize=25 19,242 3,310 481,33
Batch Update with BatchSize=1000 19,283 1,889 920,80
Read Blob 0,638 0,248 157,26
Write Blob 1,094 1,026 6,63
StoredProc with params 1,973 1,358 45,29

Memory consumption test results:

Test name FireDAC,
MB
UniDAC,
MB
UniDAC is better
than FireDAC, %
Fetch with FetchRows=25 32,559 16,113 102,07
Fetch with FetchRows=1000 32,266 13,227 143,94
Fetch All Records 32,809 10,402 215,41
Read Blob 35,324 21,191 66,69
Write Blob 34,387 23,465 46,55

Diagrams:

In fetch tests, UniDAC showed significantly higher performance and consumed 3 times less memory than UniDAC.

When updating data with the Append/Insert/Edit/Post methods, UniDAC was slightly better than FireDAC, but the real advantage could be seen in update tests with SQL statements where UniDAC was 2 times faster than its competitor.

In batch insert operations, UniDAC was still ahead of FireDAC and showed its real power in batch update tests with 10 times better performance than FireDAC.

UniDAC read Blobs 2.5 times faster and consumed 1.5 times less memory than FireDAC, and also showed better results than the competitor when writing blobs.

In operations with stored procedures, UniDAC was 1.5 times faster than UniDAC.

SQL Server

The testing was performed on a remote MS SQL Server 2019  in a 1 GB/s network.

This test is a bit different from previous tests because FireDAC can work with SQL Server only through an ODBC driver while UniDAC supports ODBC, OLEDB, and the Direct mode.

Performance test results:

Test name FireDAC, sec UniDAC via Native Client, sec UniDAC faster than FireDAC via Native Client, % UniDAC  in Direct, sec UniDAC faster than FireDAC in Direct, %
Fetch with FetchRows=25 0,607 0,113 437,17 0,111 446,85
Fetch with FetchRows=1000 0,605 0,124 387,90 0,107 465,42
Fetch All Records 0,560 0,111 404,50 0,111 404,50
Fetch by 1 record without Params 1,493 0,848 76,06 0,698 113,90
Fetch by 1 record with Params 2,062 0,817 152,39 0,755 173,11
Fetch by 1 record with Params (prepared) 0,754 0,661 14,07 0,649 16,18
Insert/Post 1,171 1,040 12,60 1,028 13,91
Edit/Post 1,152 1,077 6,96 1,039 10,88
DML insert without Params 2,308 1,456 58,52 1,294 78,36
DML insert with Params 3,459 2,371 45,89 2,233 54,90
DML insert with Params (prepared) 1,313 1,302 0,84 1,130 16,19
DML update without Params 1,743 0,961 81,37 0,961 81,37
DML update with Params 2,225 1,028 116,44 1,026 116,86
DML update with Params (prepared) 0,914 0,831 9,99 0,844 8,29
Batch Insert with BatchSize=25 3,740 1,219 206,81 1,281 191,96
Batch Insert with BatchSize=1000 3,588 1,059 238,81 1,156 210,38
Batch Update with BatchSize=25 3,862 3,491 10,633,378 14,33
Batch Update with BatchSize=1000 3,957 1,895 108,81 1,897 108,59
Read Blob 1,229 0,254 383,86 0,200 514,50
Write Blob 1,637 0,738 121,82 0,782 109,34
StoredProc with params 0,892 0,824 8,25 0,791 12,77

Memory consumption test results:

Test name FireDAC, MB UniDAC via Native Client, MB UniDAC is better than FireDAC via Native Client, % UniDAC  in Direct, MB UniDAC is better than FireDAC in Direct , %
Fetch with FetchRows=25 14,184 9,457 49,98 11,047 28,40
Fetch with FetchRows=1000 14,168 8,477 67,13 11,309 25,28
Fetch All Records 15,168 9,941 52,58 12,500 21,34
Read Blob 23,340 17,660 32,16 23,191 0,64
Write Blob 21,313 21,320 -0,03 23,262 -8,38

Diagrams:

In read tests, UniDAC substantially outperformed FireDAC–both in the Direct mode and through an OLEDB driver, UniDAC was 5 times faster and consumed less memory. 

UniDAC updated data slightly faster than FireDAC with the Append/Insert/Edit/Post methods and almost 2 times faster with SQL statements in some test cases.

In batch update operations, UniDAC was 3 times faster than FireDAC.

When reading Blobs, UniDAC was up to 5 times faster than UniDAC. The only time when FireDAC outperformed UniDAC was in a memory consumption test for writing Blobs–FireDAC consumed several percent less memory.

The compared products were almost equal in stored procedures tests, though UniDAC was a little faster.

UniDAC exceeded FireDAC in practically all tests for SQL Server. The use of the Direct mode probably won’t give you a substantial performance gain on Windows compared to the preinstalled Microsoft OLE DB Driver for SQL Server on this system. The developers who create software for non-Windows systems would benefit from the Direct mode as it ensures performance and memory consumption similar to those of the native OLE DB Driver. 

SQLite

The testing was performed on an SQLite database located on an SSD driver. UniDAC and FireDAC can work with SQLite both through an external client library and a statically linked client library (it’s called the Direct mode in UniDAC). We tested both methods.

Performance test results:

Test name FireDAC via native DLL, sec FireDAC Static Link, sec UniDAC via native DLL, sec UniDAC in Direct, sec UniDAC faster than FireDAC, %
Fetch with FetchRows=25 0,066 0,066 0,051 0,053 26,92
Fetch with FetchRows=1000 0,064 0,063 0,051 0,052 23,30
Fetch All Records 0,062 0,061 0,050 0,053 19,42
Fetch by 1 record without Params 0,154 0,154 0,090 0,092 69,23
Fetch by 1 record with Params 0,160 0,159 0,094 0,097 67,02
Fetch by 1 record with Params (prepared) 0,080 0,0800,022 0,022 263,64
Insert/Post 0,147 0,147 0,089 0,090 64,25
Edit/Post 0,179 0,179 0,115 0,117 54,31
DML insert without Params 0,132 0,129 0,071 0,074 80,00
DML insert with Params 0,101 0,101 0,100 0,101 0,50
DML insert with Params (prepared) 0,038 0,038 0,038 0,038 0,00
DML update without Params 0,154 0,155 0,094 0,095 63,49
DML update with Params 0,132 0,129 0,125 0,125 4,40
DML update with Params (prepared) 0,060 0,060 0,060 0,060 0,00
Batch Insert with BatchSize=25 0,373 0,364 0,121 0,125 199,59
Batch Insert with BatchSize=1000 0,361 0,368 0,081 0,085 339,16
Batch Update with BatchSize=25 0,653 0,651 0,169 0,169 285,80
Batch Update with BatchSize=1000 0,651 0,650 0,113 0,113 475,66
Read Blob 0,052 0,050 0,039 0,039 30,77
Write Blob 0,118 0,119 0,089 0,092 30,94

Memory consumption test results:

Test name FireDAC via native DLL, MB FireDAC Static Link, MB UniDAC via native DLL, MB UniDAC in Direct, MB UniDAC is better than FireDAC, %
Fetch with FetchRows=25 27,168 27,762 10,449 10,840 150,63
Fetch with FetchRows=1000 27,102 27,313 9,781 10,855 149,67
Fetch All Records 27,445 27,320 9,898 10,844 153,09
Read Blob 51,520 51,508 22,426 21,191 143,12
Write Blob 31,254 31,250 21,250 21,250 47,08

Diagrams:

When reading a large number of rows, UniDAC was still faster than FireDAC, and 3.5 times faster when reading records one by one.

In update tests, UniDAC was up to 1.5 times faster than FireDAC both when using the Append/Insert/Edit/Post methods and SQL statements.

UniDAC outperformed FireDAC by a wide margin in batch operations–in some tests, UniDAC was 5 times faster. 

When reading and writing Blobs, UniDAC still exceeded FireDAC both in performance and memory optimization.

The connection method didn’t have a significant impact on the performance of the products–we got almost the same results with external and statically linked client libraries.

Conclusion

The results clearly show that UniDAC delivers a significant performance boost compared to standard data access components in RAD Studio–in some test cases, UniDAC was 10 times faster than FireDAC.

UniDAC also consumes significantly less memory than FireDAC, which is very important for applications that work with large amounts of data.

The results of this test suite prove that UniDAC is a good choice for developers who create data-intensive applications in Delphi or C++ Builder.

The archive with the source code of the application that we created for this test suite:

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To eliminate the effect of the IDE and built-in debugger on test results, enable the Release configuration before compiling the project.

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