Since late last year, Microsoft has made the Attunity connectors to Oracle databases available to Enterprise Edition customers. We all recognized right away that these connectors were faster than the pre-existing options from either Microsoft or Oracle, when moving data into or out of an Oracle database. It wasn’t immediately obvious what speeds we could expect from the connectors, so I did some experimenting to see. This posting summarizes some findings from those experiments done earlier this year, but rather than report all the details I will then zero in on the key lessons and one big surprise that came out of the work.

Before getting in to my findings, let me give a little anecdote heard from a colleague: There is at least one SSIS customer that now uses SSIS and the Attunity connectors to move data from an Oracle database to an Oracle database, because SSIS with Attunity outperforms any of the Oracle options! While I can’t corroborate that, the information to follow is from my own measurements.

To do this work, I used two machines each with 24 cores (64-bit, 2.4 GHz), one for SSIS and one for Oracle. The machines were practically an embarrassment of riches for this simple benchmark. The SSIS machine had flat files to read (when loading data into Oracle) or write (when extracting data from Oracle). The SSIS packages were super simple, just a flat file source and an Oracle destination, or an Oracle source and a flat file destination. The data was 75 million rows, 133 bytes per row, of the LINEITEM table from the TPC-H benchmark, as generated by the DBGEN utility.

Some basic findings (remember, you mileage will vary):

  • Putting data into an Oracle database using the Attunity connectors clocked 30,000 rows per second, on the order of 20 times faster than using the OLE DB connectors to Oracle.
  • Extracting data from Oracle moved 36,000 rows per second, about 20% faster than using the OLE DB connectors.
  • The above measurements were taken using “mixed” data types: Numbers were put in NUMBER fields, dates were put in DATE fields, etc. A funny thing happened though when all the data was put in string fields (VARCHAR2 use used for everything). Now we could hit 42,000 rows per second loading data into Oracle, and 76,000 rows per second extracting from Oracle!
  • The Fast Load option is supposed get higher performance through the use of the DirectPath API. In my experiments, I didn’t see a consistent advantage of Fast Load over non-Fast Load. The thing that Fast Load did seem to do was shift more of the CPU time from the Oracle process to the SSIS process. This could mean that if you have multiple concurrent SSIS packages sending data to Oracle, using Fast Load might let Oracle receive the data faster. Given my experience with Fast Load, I can only recommend that you check its performance in your own situation. Note: I’ve been told that Fast Load will be fixed in a maintenance release later this calendar year. So while I’m not promising anything, it’s likely that this will change.
  • The default batch size for the Oracle destination connector is 100 rows. Setting the batch size to 10,000 rows gave a boost of 10% to 50%, depending on other elements of the configuration. (When using Fast Load, you specify the buffer size instead of the row count. So estimate the buffer size needed to hold the number of rows you want, and use that number.)
  • When using the Oracle source, setting batch size to 10,000 rows gave a boost of around 10%, depending on other elements of the configuration.
  • I wanted to know how important it was for SSIS to be on a separate machine from the Oracle database. There was a good network connecting the source and destination servers, and also plenty of CPUs and memory on the servers. What I saw was a negligible difference between the case where SSIS and Oracle were on the same server and the case where SSIS and Oracle were on separate systems. My recommendation: Look at what resource is the most loaded in your environment, and configure to lighten the load on that resource.

The idea that performance with string data would be so different from performance with natural data types was a big surprise. The difference was especially pronounced when extracting data from Oracle. Now let’s face it, we would prefer to see data extracted from Oracle and placed in SQL Server databases! Given the big speed disparity and the fact that most real-world data needs to be in natural data types, I wondered if the same thing would happen if data was cast to string types in the query that SSIS issues against Oracle. So instead of having SSIS simply read the table, I gave it this query to run:

select
    TO_CHAR(L_SHIPDATE),
    TO_CHAR(L_ORDERKEY),
    TO_CHAR(L_DISCOUNT),
    TO_CHAR(L_EXTENDEDPRICE),
    TO_CHAR(L_SUPPKEY),
    TO_CHAR(L_QUANTITY),
    L_RETURNFLAG,
    TO_CHAR(L_PARTKEY),
    L_LINESTATUS,
    TO_CHAR(L_TAX),
    TO_CHAR(L_COMMITDATE),
    TO_CHAR(L_RECEIPTDATE),
    L_SHIPMODE,
    TO_CHAR(L_LINENUMBER),
    L_SHIPINSTRUCT,
    L_COMMENT
from ATTUSER.LINEITEM

Then before inserting the data into SQL Server using the SQL Server destination, I put in a data conversion task to get all the data into the correct types.

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At this point you must be thinking, “Surely converting the data twice can’t be the fastest way!” Well, here are the results: The first run below read the mixed data types using the Attunity Oracle source with default settings, converted to SQL Server types, then wrote to the SQL Server destination. The second run was like the first, with the addition of setting the batch size larger. The third run was like the first, but on reading from Oracle all the columns were converted to text as discussed above. The last test was like the third, with the addition of setting the batch size larger. Using the string conversion and larger batches, the run was over two times faster than the obvious out-of-the-box configuration.

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Overall, the Attunity connectors for Oracle really were fast, as expected. In doing this work a few lessons turned up that hopefully help you get optimal performance.

- Len Wyatt