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How fast can you process SMF data from 1 billion CICS transactions?

May 20, 2026 by Andrew

I was recently asked whether Java performance on a PC was adequate for processing SMF data. More generally, people have been sceptical about Java performance. So, I thought why not borrow the idea from the Java 1 Billion Row Challenge, and write a Java program to see how fast you can produce a report on 1 billion CICS transactions using the EasySMF Java API?

The test is to read CICS monitoring facility SMF data and produce a report showing:

  • transaction counts,
  • maximum, minimum and average elapsed times
  • average and total USRCPUT

grouped by APPLID and transaction ID, for 1 billion CICS transactions.

  • The CICS SMF records are compressed using CICS compression.
  • The SMF records are not sorted or pre-processed for the report.

Watch the video, or read more detail below…

Input Data

The first problem was I didn’t have enough sample data for 1 billion transactions, so I wrote a small program to build a file containing 1 billion transactions. It reads an input file and writes the data to a new file repeatedly, until the new file contains records for the required number of transactions.

That means the input contains repeated data, but that shouldn’t influence the run time as long as the initial file is reasonably large.

The resulting file with 1 billion transactions was 586 GB.

The program to generate the data is available here: GenerateInput.java

PC Hardware

The PC used to run the test was a Windows PC, with an i5-14400 processor and 1TB Crucial P310 M.2 SSD. This is a reasonable midrange PC, nothing extreme or expensive.

The Program

Entries in the original 1 billion row challenge used some pretty advanced techniques to get the winning time down to around 1.5 seconds.

However, with this program I’m trying to keep it simple and use standard techniques that should be familiar to any Java programmer, and at least readable to people familiar with similarly structured languages like C/C++.

The program uses a class to accumulate data about each applid/transaction combination. The entries are stored in a HashMap with a Java record containing the applid and transaction name as the key. This provides very fast access as the data is processed.

The CICS records require a dictionary to access locate the fields in the SMF record. A file containing the CICS dictionary SMF records is read before the main data file.

View the program source on Github (only 134 lines):

Cics1B.java

Results

The program produced the report from 1 billion CICS transactions in 20 minutes 17 seconds. Some statistics from the run:

  • Read SMF data: 492 MB/s
  • Decompress the compressed portion of the record: 420 MB/s
  • Decompressed data size: 3.1 TB
  • Process decompressed data: 2.6 GB/s

Most of the CPU time was spent doing the CICS SMF data decompression.

How to make it faster?

On a PC, the simple answer is multithreading. This PC has 10 cores (6 performance cores, 4 efficiency cores) and can run 16 threads simultaneously. The CICS data decompression in particular will benefit from multiple threads.

The program can be converted to use Java Streams, specifying parallel() to divide the work across multiple threads.

Note that this might not be a good idea on z/OS. Java by default will attempt to create enough threads to use all the processors on the system – which is probably not desirable on the mainframe. On the other hand, it could work OK in a discretionary service class and will probably be limited by I/O speed anyway so YMMV.

Here’s the parallel version of the program on Github:

Cics1BParallel.java

Java Streams provides methods to do the accumulation independently and merge the results from each thread at the end. That avoids the problem of synchronizing updates between threads.

However, for this test, I elected to minimize the changes from the original version and just moved the processing to a forEach block. That means that any updates to shared data in the forEach block need to be synchronized between threads. Change required were:

  • Transaction counts were changed to use the LongAdder class.
  • The HashMap was changed to a ConcurrentHashMap
  • The add method in the TransactionData class was made synchronized.

In theory, accumulating independently and merging at the end should be faster because it avoids having to synchronize threads. However, most of the time here is spent decompressing data so any gain is probably insignificant.

Results

The parallel run finished in 7 minutes 18 seconds. Statistics:

  • Read SMF data: 1.3 GB/s
  • Decompress the compressed portion of the record: 1.1 GB/s
  • Process decompressed data: 7.2 GB/s

Reporting on 1 billion transactions in 7 minutes 18 seconds seems pretty good. That’s over 2 million transactions per second. The times are pretty linear with transaction count, so e.g. if you have only 100 million transactions it should run in less than a minute.

Even faster?

According to the Windows performance monitor, the parallel run didn’t max out the disk or the CPU. In theory the SSD is capable of more than 1.3 GB/s so there might be more performance to be found.

At some point the I/O is being processed sequentially, so I suspect that is the bottleneck. Perhaps it would be faster if there were multiple files e.g. if the SMF data was divided by CICS region or system and I/O could be processed to multiple files in parallel.

A subject for future investigation, perhaps.

Try it on your data

Contact us at info@blackhillsoftware.com for a free trial. Let us know what reports you would like and we can add them to the samples.

Notes

  • This runs fast enough that I/O is potentially the bottleneck. On z/OS, I have found that zEDC compression for SMF data can significantly improve I/O speed. Using zEDC and CICS SMF record compression might be better than zEDC alone. See some tests here.
  • This is 100% Java so it is zIIP eligible on z/OS. This is a particular advantage if the general purpose CPs run at less than full speed.
  • For CICS records, most of the time is spent decompressing the records. It is reasonably easy to modify the program to produce multiple different reports from the same pass of the data, which means that additional reports cost almost zero CPU or elapsed time.
  • EasySMF can read data using the z/OSMF Dataset and Files REST API. You can run a Java program on the PC and access the mainframe SMF data using a https connection.
  • AI is pretty good at writing Java, and Java type safety in the EasySMF API helps keep it on track with the SMF data so it’s very feasible to use AI to help write SMF reports.

Filed Under: Java, Java SMF

Using zEDC compression for SMF data

April 29, 2024 by Andrew

Should you use zEDC to compress SMF data? Should you use both zEDC and CICS software compression?

There was a recent discussion on the IBM-MAIN email list about exploiting zEDC. One of the topics was SMF data, including whether to turn off the software compression for CICS SMF records.

I ran some experiments to compare the processing of SMF records with and without the various compression options. The tests were run using a dataset containing 6.6 GB of CICS SMF data compressed with CICS software compression. For the tests without software compression, the data was uncompressed and copied to another dataset compressed with zEDC. The uncompressed data size was about 39 GB.

Disclaimer: As always, your mileage may vary. This isn’t intended as a benchmark – it’s just one set of tests run against one dataset. This is a very lightly loaded system running under VM. It might bear no resemblance to a busy production system. CPU times etc. might not be accurately reported.

Data size

CICS software compression124000 tracks
CICS software compression + zEDC18500 tracks
zEDC compression35000 tracks

The data is very compressible. The software compressed data compresses further using zEDC and the final size is smaller than compression by zEDC alone.

Reading data

To test the overhead of zEDC when reading SMF data, I used the IFASMFDP program with the OUTDD pointing to DUMMY. This reads the data and produces a report on the numbers of each SMF record type, showing that individual records were read.

Elapsed (seconds)CPU (seconds)
CICS software compression
(read 6.6GB)
30.10.86
CICS software compression + zEDC
(read 6.6GB, ~1GB compressed)
2.21.2
zEDC compression only
(read 39GB, ~1.9GB compressed)
6.55.6
Chart showing comparative times to read data

This program doesn’t do anything with the CICS data so there is no software decompression. It’s just a indication of the cost of reading SMF records with and without zEDC compression.

I was surprised how slow it was to read the records without zEDC. Is this a result of e.g. running under VM, or is it typical? zEDC was very fast, relative to the uncompressed data – better than 10x speed-up.

Copying Data

I ran the IFASMFDP jobs but this time with OUTDD specifying a real dataset. The difference between the read jobs and the copy jobs should give an indication of the cost of writing the data using zEDC.

Elapsed (seconds)CPU (seconds)
CICS software compression
(read/write 6.6GB)
621.58
CICS software compression + zEDC
(read/write 6.6GB, ~1GB compressed)
8.32.31
zEDC compression only
(read/write 39GB, ~1.9GB compressed)
19.411.88
Chart showing comparative times to copy data

Again zEDC was much faster but unsurprisingly there was a CPU cost (less than 0.2s/GB).

SMF Reporting using Java

I ran the CICS Transaction Summary report from the EasySMF Java samples:

https://github.com/BlackHillSoftware/easysmf-samples/blob/main/sample-reports/src/main/java/com/smfreports/cics/CicsTransactionSummary.java

This report produces a basic summary of CICS transactions grouped by APPLID and transaction name.

Software decompression is performed in Java by EasySMF as required. The program is processing 39GB of data after decompression.

This gives some idea of the overhead of compression on reporting, however this is very dependent on the reporting software you use and your mileage will definitely vary.

Elapsed (seconds)CP (seconds)zIIP (seconds)
CICS software compression
(6.6GB compressed)
64.51.4940.6
CICS software compression + zEDC
(read 6.6GB, ~1GB compressed)
39.51.2545.4
zEDC compression only
(read 39GB, ~1.9GB compressed)
35.02.0238.6
Chart showing comparative times to run a report

One interesting thing here is that zEDC with Java seems to allow more work to move from the CP to the zIIP. The time on CP reduces from 1.49s to 1.25s processing the 6.6GB input. The CP time processing the 39GB compressed with zEDC is only 2 seconds. Reading the same data with IFASMFDP took 5.6 seconds of CPU time. Presumably under Java the rest is included in the zIIP time.

CICS software compression increased elapsed and zIIP times by about 10-15% compared to zEDC compression only, but reduced CP time.

Conclusions

Based on these tests, zEDC works very well with SMF data.

  • Elapsed times were greatly reduced in all cases
  • The CPU time required was fractions of a second per GB
  • CICS software compression appears to be still worthwhile. Reducing the amount of data being processed by zEDC significantly reduces the elapsed and CPU times for SMF copy operations e.g. SMF dump, weekly/monthly processing.

Don’t forget to measure and verify the results on your own systems!

Filed Under: Java, Java SMF

Text message alerts using the z/OS SMF Real Time Interface

April 15, 2024 by Andrew

In this post, I’ll show how you can send SMS text messages from z/OS for failed jobs using Twilio and the z/OS SMF Real Time Interface.

Twilio provides a Java API to use their service, and that can be combined with the EasySMF Real Time Interface to send SMS messages based on real time SMF data.

Sending text messages from z/OS

Twilio is a paid service for production messaging, but you can sign up for a free trial account and send a limited number of messages to a verified phone number.

Twilio provides quickstart documentation here:

https://www.twilio.com/docs/messaging/quickstart/java

You can ignore the stuff about the CLI, all we are going to do is send an outbound message which requires a Twilio account, the Java program and dependencies i.e. the fat jar.

This is the Twilio quickstart Java program with a couple of minor tweaks:

import com.twilio.Twilio;
import com.twilio.rest.api.v2010.account.Message;
import com.twilio.type.PhoneNumber;

public class TwilioTest {
    // Find your Account SID and Auth Token at twilio.com/console
    // and set the environment variables. See http://twil.io/secure
    public static final String ACCOUNT_SID = System.getenv("TWILIO_ACCOUNT_SID");
    public static final String AUTH_TOKEN = System.getenv("TWILIO_AUTH_TOKEN");

    public static void main(String[] args) {
        Twilio.init(ACCOUNT_SID, AUTH_TOKEN);
        Message message = Message.creator(
                new com.twilio.type.PhoneNumber("+14159352345"), // to
                new com.twilio.type.PhoneNumber("+14158141829"), // from
                args[0])
            .create();

        System.out.println(message.getSid());
    }
}

Upload the Twilio fat jar twilio-x.x-jar-with-dependencies.jar to z/OS. It is available from e.g. https://repo1.maven.org/maven2/com/twilio/sdk/twilio/10.1.3/

Java 11 will run single file Java programs without a separate compilation step, so we can just run the program under BPXBATCH:

//ANDREWRG JOB CLASS=A,
//             MSGCLASS=H,
//             NOTIFY=&SYSUID
//*
//BPXBATCH EXEC PGM=BPXBATCH,REGION=512M
//STDPARM  DD *
sh /usr/lpp/java/J11.0_64/bin/java
 /home/andrewr/java/src/TwilioTest.java
 "Hello from z/OS"
//STDENV   DD *
CLASSPATH=/home/andrewr/java/lib/twilio-10.1.3-jar-with-dependencies.jar
TWILIO_ACCOUNT_SID=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
TWILIO_AUTH_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
//SYSOUT   DD SYSOUT=*
//STDOUT   DD SYSOUT=*
//STDERR   DD SYSOUT=*

Twilio can also send the messages to WhatsApp using the same functionality.

Sending notifications for failed jobs

Prerequisite: z/OS SMF Real Time Interface

The z/OS SMF Real Time Interface must be active. This requires:

  • SMF running in logstream mode
  • Define an in-memory resource to receive the required records (type 30 in this case)
  • Setup RACF definitions to allow the user running Java program to access the SMF in memory resource

The program

The complete source code for this program can be found here:

https://github.com/BlackHillSoftware/easysmf-samples/tree/main/easysmf-rti/rti-notifications

The following is an overview of the major sections of the program.

Reading the in-memory resource

The first thing to do is set up the connection to the in-memory resource.

public class RtiNotifications
{
    public static void main(String[] args) throws IOException
    {
        try (SmfConnection connection = 
                 SmfConnection.forResourceName("IFASMF.MYRECS")
                     .onMissedData(RtiNotifications::handleMissedData)
                     .disconnectOnStop()
                     .connect();

             // Set up SmfrecordReader to read type 30 subtypes 4 and 5    
             SmfRecordReader reader = 
                 SmfRecordReader.fromByteArrays(connection)
                     .include(30,4)
                     .include(30,5))
        {
            // process data here
        }
    }

    private static void handleMissedData(MissedDataEvent e)
    {
        System.out.println("Missed Data!");
        e.throwException(false);    
    }
}

This code:

  • creates a real time connection to resource name IFASMF.MYRECS
  • sets up an action to be taken if data is written faster than the program can read it and the real time buffer wraps (write a message and suppress the exception)
  • sets up a command handler to disconnect from the resource when the STOP command is received
  • creates a SmfRecordReader to read SMF 30 subtypes 4 and 5 (step and job end) records

The connection and reader will be closed automatically when the program exits the try block.

The connection can be simulated in a development environment by setting environment variables:

SIMULATE_RTI=Y

and

IFASMF_MYRECS=/your/smf/filename

The SmfConnection will read SMF data from the file indicated by the environment variable matching the resource name.

Processing the SMF 30 Records

We want to send the SMS message when the job ends, which is indicated by a subtype 5 record. However, if steps are skipped due to e.g. COND processing or an ABEND, the information in the subtype 5 record might be from a step that didn’t run. These steps have the “flushed” indicator set in the Completion Section.

We need to keep the step end information for steps that did run and check the last executed step when we see the job end record. The Java HashMap provides a convenient way to store information for failed steps.

We can create a class to use as a HashMap key to identify specific jobs. The class has fields for the identifying information (system, jobname, job number, read date and time) which are populated in the constructor. Eclipse can generate the hashCode and equals methods which are required for a HashMap key.

Map<JobKey, Smf30Record> failedSteps = new HashMap<>();

...

private static class JobKey
{
    String system;
    String jobname;
    String jobnumber;
    long readtime;
    int readdate;

    JobKey(Smf30Record r30)
    {
        system = r30.system();
        jobname = r30.identificationSection().smf30jbn();
        jobnumber = r30.identificationSection().smf30jnm();
        readtime = r30.identificationSection().smf30rstRawValue();
        readdate = r30.identificationSection().smf30rsdRawValue();
    }

    // hashCode and equals generated using Eclipse
    @Override
    public int hashCode() {
        return Objects.hash(jobname, jobnumber, readdate, readtime, system);
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;
        if (getClass() != obj.getClass())
            return false;
        JobKey other = (JobKey) obj;
        return Objects.equals(jobname, other.jobname) 
                && Objects.equals(jobnumber, other.jobnumber)
                && readdate == other.readdate 
                && readtime == other.readtime 
                && Objects.equals(system, other.system);
    }
}

SMF 30 Subtype 4: Step End

When processing step end records, if the step failed we save the record for later. If it ran successfully, we remove any previous failed step.

if (failed(r30)) 
{  
    // this replaces existing entry if present 
    failedSteps.put(new JobKey(r30), r30);
}
// else it didn't fail, check it wasn't flushed
else if (!r30.completionSection().smf30flh()) 
{
    // If the step wasn't flushed, remove any earlier failed step
    failedSteps.remove(new JobKey(r30));
}

failed is a method which checks whether the step failed:

private static boolean failed(Smf30Record r30) 
{
    return 
            // abended except for S222 (cancelled)
            (r30.completionSection().smf30abd() 
                    && r30.completionSection().smf30scc() != 0x222)
            // or condition code > 8
            || r30.completionSection().smf30scc() > 8
            // or post execution error
            || r30.completionSection().smf30sye();
}

The criteria can be adjusted as required.

SMF 30 Subtype 5: Job End

When the job ends, we check whether we previously saved a failed step.

JobKey key = new JobKey(r30);                        
if (failedSteps.containsKey(key))
{
    // send notification using information from failed step 
    sendNotification(failedSteps.get(key));                            
    failedSteps.remove(key); // finished with this
}
// or if the type 5 record indicates failure
else if(failed(r30))
{
    // send notification using information from job
    sendNotification(r30);                            
}

Sending the SMS message

The job information is extracted from the SMF record, the Twilio information is supplied in environment variables, and sending the SMS message uses the same process as the Twilio quickstart.

private static final String ACCOUNT_SID = System.getenv("TWILIO_ACCOUNT_SID");
private static final String AUTH_TOKEN = System.getenv("TWILIO_AUTH_TOKEN");
private static final String TO_PHONE = System.getenv("TO_PHONE");
private static final String FROM_PHONE = System.getenv("FROM_PHONE");

private static void sendNotification(Smf30Record r30) 
{
    String messagetext = 
            String.format("%s Job failed: %s %s Step: %d %s Program: %s CC: %s",
                    r30.smfDateTime().toString(),
                    r30.identificationSection().smf30jbn(), // job name
                    r30.identificationSection().smf30jnm(), // job number
                    r30.identificationSection().smf30stn(), // step number
                    r30.identificationSection().smf30stm(), // step name
                    r30.identificationSection().smf30pgm(), // program
                    r30.completionSection().completionDescription());
    
    // Send a SMS notification through Twilio
    Twilio.init(ACCOUNT_SID, AUTH_TOKEN);
    Message message = Message.creator(
            new com.twilio.type.PhoneNumber(TO_PHONE),
            new com.twilio.type.PhoneNumber(FROM_PHONE),
            messagetext)
            .create();
    System.out.println("Message Sent: " + message.getSid());
}

What Next?

Browse and build the EasySMF:RTI sample code:

https://github.com/BlackHillSoftware/easysmf-samples/tree/main/easysmf-rti/rti-notifications

The Github sample has some additional functionality not covered here. You can limit the notifications to jobs running in specific job classes. You can use the same functionality to include jobs by job name or create custom failure criteria for specific jobs.

For simplicity, the sample doesn’t include error handling. If sending the message fails, the program will disconnect from the SMF in memory resource and end with an exception. A production version should probably catch certain errors and retry.

Filed Under: Java, Uncategorized

DCOLLECT Reports and DCOLLECT to JSON using Java

October 27, 2023 by Andrew

EasySMF:JE version 2.2.1 added support for DCOLLECT records.

DCOLLECT reports using Java are fast and powerful. Programs can run on z/OS on zIIP processors, or DCOLLECT data can be downloaded to another platform for reporting.

Sample reports are provided for several different scenarios:

  • Space by SMS storage group and volume
  • Space by high level qualifier, on primary volumes and HSM ML1 and ML2
  • Space by time since last reference (7 days, 1 month, 6 months, 1 year, 5 years)
  • Aged datasets – datasets on primary volumes with a last reference data older than a cutoff e.g. 5 years
  • Datasets by migration date (7 days, 1 month, 6 months, 1 year, 5 years)
  • Frequently migrated datasets – datasets that are being migrated and recalled frequently
  • zEDC compression statistics by high level qualifier

Samples are also provided to compare 2 different DCOLLECT runs and find what changed:

  • Change in space by storage group
  • Change in space by the top 50 high level qualifiers
  • Change in space for selected dataset names

Convert DCOLLECT Records to JSON format

DCOLLECT records can also be converted to JSON format, for use with Splunk, Excel or other JSON reporting tools.

See the Samples

Sample code is available on Github:

https://github.com/BlackHillSoftware/easysmf-samples/tree/main/dcollect

30 Day Trial

Get a 30 Day Trial, or request further information:

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Filed Under: Uncategorized

Finding UID 0 work on z/OS using SMF Data

February 17, 2022 by Andrew

Finding work running under UID 0 and understanding what it is is important for security on z/OS.

Some UID 0 processes run for hours and are easy to find with tools like SDSF. Other work might only run for a fraction of a second, and is virtually impossible to catch “in the act”.

SMF data can help. SMF type 30 records have Unix Process sections for z/OS unix work. The Unix Process section includes the UID so you can use it to find UID 0 work.

Here is an example of the EasySMF Unix Work report:

EasySMF Unix Work Report, filtered to show UID 0.

The UID field allows you to filter the report to include only work with UID 0.

The report builds a tree using the parent process information so you can see the relationship between different tasks. Information from SMF about the running program is included, which can help to understand what the work is doing.

The Elapsed column shows how long these tasks were running. Most of these tasks were part of system startup and many ran for less than half a second, so it would be very difficult to catch them in real time.

You can download a 30 day trial to see what the data from your system looks like here:

EasySMF 30 Day Trial

Filed Under: EasySMF News

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Information

EasySMF:JE Java API for SMF: Quickstart

Java vs C++ : Drag Racing on z/OS

News

  • How fast can you process SMF data from 1 billion CICS transactions?
  • Using zEDC compression for SMF data
  • Text message alerts using the z/OS SMF Real Time Interface

Black Hill Software

Suite 10b, 28 University Drive, Mt Helen, VIC 3350, Australia
PO Box 2214, Bakery Hill, VIC 3354, Australia
+61 3 5331 8201
+1 (310) 634 9882
info@blackhillsoftware.com

News

  • How fast can you process SMF data from 1 billion CICS transactions?
  • Using zEDC compression for SMF data
  • Text message alerts using the z/OS SMF Real Time Interface

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