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2.10 DAGMan Applications

A directed acyclic graph (DAG) can be used to represent a set of computations where the input, output, or execution of one or more computations is dependent on one or more other computations. The computations are nodes (vertices) in the graph, and the edges (arcs) identify the dependencies. HTCondor finds machines for the execution of programs, but it does not schedule programs based on dependencies. The Directed Acyclic Graph Manager (DAGMan) is a meta-scheduler for the execution of programs (computations). DAGMan submits the programs to HTCondor in an order represented by a DAG and processes the results. A DAG input file describes the DAG.

DAGMan is itself executed as a scheduler universe job within HTCondor. It submits the HTCondor jobs within nodes in such a way as to enforce the DAG's dependencies. DAGMan also handles recovery and reporting on the HTCondor jobs.

2.10.1 DAGMan Terminology

A node within a DAG may encompass more than a single program submitted to run under HTCondor. Figure 2.1 illustrates the elements of a node.

Figure 2.1: One Node within a DAG

More than one HTCondor job may belong to a single node. All HTCondor jobs within a node must be within a single cluster, as given by the job ClassAd attribute ClusterId.

DAGMan enforces the dependencies within a DAG using the events recorded in a separate file that is specified by the default configuration. If the exact same DAG were to be submitted more than once, such that these DAGs were running at the same time, expected them to fail in unpredictable and unexpected ways. They would all be using the same single file to enforce dependencies.

As DAGMan schedules and submits jobs within nodes to HTCondor, these jobs are defined to succeed or fail based on their return values. This success or failure is propagated in well-defined ways to the level of a node within a DAG. Further progression of computation (towards completing the DAG) is based upon the success or failure of nodes.

The failure of a single job within a cluster of multiple jobs (within a single node) causes the entire cluster of jobs to fail. Any other jobs within the failed cluster of jobs are immediately removed. Each node within a DAG may be further constrained to succeed or fail based upon the return values of a PRE script and/or a POST script.

2.10.2 The DAG Input File: the JOB, DATA, PARENT...CHILD, and SCRIPT Key Words

The input file used by DAGMan is called a DAG input file. It specifies the nodes of the DAG as well as the dependencies that order the DAG. All items are optional, except that there must be at least one JOB or DATA item.

Comments may be placed in the DAG input file. The pound character (#) as the first character on a line identifies the line as a comment. Comments do not span lines.

A simple diamond-shaped DAG, as shown in Figure 2.2 is presented as a starting point for examples. This DAG contains 4 nodes.

Figure 2.2: Diamond DAG

A very simple DAG input file for this diamond-shaped DAG is

    # File name: diamond.dag
    JOB  A  A.condor 
    JOB  B  B.condor 
    JOB  C  C.condor	
    JOB  D  D.condor

A set of basic keywords appearing in a DAG input file is described below.

2.10.3 Submit Description File Contents

Each node in a DAG may use a unique submit description file. A key limitation is that each HTCondor submit description file must submit jobs described by a single cluster number; DAGMan cannot deal with a submit description file producing multiple job clusters.

Consider again the diamond-shaped DAG example, where each node job uses the same submit description file.

    # File name: diamond.dag
    JOB  A  diamond_job.condor 
    JOB  B  diamond_job.condor 
    JOB  C  diamond_job.condor	
    JOB  D  diamond_job.condor

Here is a sample HTCondor submit description file for this DAG:

    # File name: diamond_job.condor
    executable   = /path/diamond.exe
    output       = diamond.out.$(cluster)
    error        = diamond.err.$(cluster)
    log          = diamond_condor.log
    universe     = vanilla

Since each node uses the same HTCondor submit description file, this implies that each node within the DAG runs the same job. The $(Cluster) macro produces unique file names for each job's output.

The job ClassAd attribute DAGParentNodeNames is also available for use within the submit description file. It defines a comma separated list of each JobName which is a parent node of this job's node. This attribute may be used in the arguments command for all but scheduler universe jobs. For example, if the job has two parents, with JobNames B and C, the submit description file command

arguments = $$([DAGParentNodeNames])
will pass the string "B,C" as the command line argument when invoking the job.

2.10.4 DAG Submission

A DAG is submitted using the tool condor_submit_dag. The manual page [*] details the command. The simplest of DAG submissions has the syntax

condor_submit_dag DAGInputFileName

and the current working directory contains the DAG input file.

The diamond-shaped DAG example may be submitted with

condor_submit_dag diamond.dag

Do not submit the same DAG, with same DAG input file, from within the same directory, such that more than one of this same DAG is running at the same time. It will fail in an unpredictable manner, as each instance of this same DAG will attempt to use the same file to enforce dependencies.

To increase robustness and guarantee recoverability, the condor_dagman process is run as an HTCondor job. As such, it needs a submit description file. condor_submit_dag generates this needed submit description file, naming it by appending .condor.sub to the name of the DAG input file. This submit description file may be edited if the DAG is submitted with

condor_submit_dag -no_submit diamond.dag
causing condor_submit_dag to create the submit description file, but not submit condor_dagman to HTCondor. To submit the DAG, once the submit description file is edited, use

condor_submit diamond.dag.condor.sub

Submit machines with limited resources are supported by command line options that place limits on the submission and handling of HTCondor jobs and PRE and POST scripts. Presented here are descriptions of the command line options to condor_submit_dag. These same limits can be set in configuration. Each limit is applied within a single DAG.

The -maxjobs option specifies the maximum number of job clusters that condor_dagman may submit at one time. Since each node may represent a single cluster of jobs, this limit restricts the number of nodes that can be submitted at a time. It is commonly used when there is a limited amount of input file staging capacity. As a specific example, consider a case where each node represents a single HTCondor job that requires 4 MB of input files, and the jobs will run in a directory with a volume of 100 MB of free space. Using the argument -maxjobs 25 guarantees that a maximum of 25 jobs, using a maximum of 100 MB of space, will be submitted to HTCondor at one time.

Since PRE and POST scripts run on the submit machine, it may be desirable to limit the number of PRE or POST scripts running at one time. The optional -maxpre command line argument limits the number of PRE scripts that may be running at one time, and the optional -maxpost command line argument limits the number of POST scripts that may be running at one time.

The number of idle jobs within a given DAG may be limited with the optional command line argument -maxidle. condor_dagman will not submit further clusters of jobs until the number of idle node jobs in the DAG goes below this specified value, even if there are ready nodes in the DAG. Note that while the -maxjobs option limits the number of clusters of jobs submitted, there can be more than one job per cluster. This -maxidle option is applied using counts of jobs, not counts of clusters. Therefore, it can prevent further clusters from being submitted.

2.10.5 File Paths in DAGs

condor_dagman assumes that all relative paths in a DAG input file and the associated HTCondor submit description files are relative to the current working directory when condor_submit_dag is run. This works well for submitting a single DAG. It presents problems when multiple independent DAGs are submitted with a single invocation of condor_submit_dag. Each of these independent DAGs would logically be in its own directory, such that it could be run or tested independent of other DAGs. Thus, all references to files will be designed to be relative to the DAG's own directory.

Consider an example DAG within a directory named dag1. There would be a DAG input file, named one.dag for this example. Assume the contents of this DAG input file specify a node job with

  JOB A  A.submit
Further assume that partial contents of submit description file A.submit specify
  executable = programA
  input      = A.input

Directory contents are

    dag1 (directory)

All file paths are correct relative to the dag1 directory. Submission of this example DAG sets the current working directory to dag1 and invokes condor_submit_dag:

  cd dag1
  condor_submit_dag one.dag

Expand this example such that there are now two independent DAGs, and each is contained within its own directory. For simplicity, assume that the DAG in dag2 has remarkably similar files and file naming as the DAG in dag1. Assume that the directory contents are

    parent (directory)
         dag1 (directory)
         dag2 (directory)

The goal is to use a single invocation of condor_submit_dag to run both dag1 and dag2. The invocation

  cd parent
  condor_submit_dag dag1/one.dag dag2/two.dag
does not work. Path names are now relative to parent, which is not the desired behavior.

The solution is the -usedagdir command line argument to condor_submit_dag. This feature runs each DAG as if condor_submit_dag had been run in the directory in which the relevant DAG file exists. A working invocation is

  cd parent
  condor_submit_dag -usedagdir dag1/one.dag dag2/two.dag

Output files will be placed in the correct directory, and the .dagman.out file will also be in the correct directory. A Rescue DAG file will be written to the current working directory, which is the directory when condor_submit_dag is invoked. The Rescue DAG should be run from that same current working directory. The Rescue DAG includes all the path information necessary to run each node job in the proper directory.

Use of -usedagdir does not work in conjunction with a JOB node specification within the DAG input file using the DIR keyword. Using both will be detected and generate an error.

2.10.6 Job Monitoring, Job Failure, and Job Removal

After submission, the progress of the DAG can be monitored by looking at the job event log file(s), observing the e-mail that job submission to HTCondor causes, or by using condor_q -dag.

There is also a large amount of information logged in an extra file. The name of this extra file is produced by appending .dagman.out to the name of the DAG input file; for example, if the DAG input file is diamond.dag, this extra file is named diamond.dag.dagman.out. If this extra file grows too large, limit its size with the configuration variable MAX_DAGMAN_LOG, as defined in section 3.3.4. The dagman.out file is an important resource for debugging; save this file if a problem occurs. The dagman.out is appended to, rather than overwritten, with each new DAGMan run.

condor_submit_dag attempts to check the DAG input file for various types of errors, such as a cycle in the DAG. If a problem is detected, condor_submit_dag prints out an error message and aborts.

To remove an entire DAG, consisting of the condor_dagman job, plus any jobs submitted to HTCondor or Stork, remove the condor_dagman job by running condor_rm. For example,

% condor_q
-- Submitter: : <> :
  9.0   taylor         10/12 11:47   0+00:01:32 R  0   8.7  condor_dagman -f -
 11.0   taylor         10/12 11:48   0+00:00:00 I  0   3.6  B.out
 12.0   taylor         10/12 11:48   0+00:00:00 I  0   3.6  C.out

    3 jobs; 2 idle, 1 running, 0 held

% condor_rm 9.0

The condor_dagman job uses condor_rm to remove any jobs within the DAG that are running.

In the case where a machine is scheduled to go down, DAGMan will clean up memory and exit. However, it will leave any submitted jobs in the HTCondor queue.

2.10.7 Suspending a Running DAG

It may be desired to temporarily suspend a running DAG. For example, the load may be high on the submit machine, and therefore it is desired to prevent DAGMan from submitting any more jobs until the load goes down. There are two ways to suspend (and resume) a running DAG.

2.10.8 Advanced Features of DAGMan Retrying Failed Nodes or Stopping the Entire DAG

DAGMan can retry any failed node in a DAG by specifying the node in the DAG input file with the RETRY keyword. The use of retry is optional. The syntax for retry is

RETRY JobName NumberOfRetries [UNLESS-EXIT value]

where JobName identifies the node. NumberOfRetries is an integer number of times to retry the node after failure. The implied number of retries for any node is 0, the same as not having a retry line in the file. Retry is implemented on nodes, not parts of a node.

The diamond-shaped DAG example may be modified to retry node C:

    # File name: diamond.dag
    JOB  A  A.condor 
    JOB  B  B.condor 
    JOB  C  C.condor	
    JOB  D  D.condor
    Retry  C 3

If node C is marked as failed for any reason, then it is started over as a first retry. The node will be tried a second and third time, if it continues to fail. If the node is marked as successful, then further retries do not occur.

Retry of a node may be short circuited using the optional keyword UNLESS-EXIT, followed by an integer exit value. If the node exits with the specified integer exit value, then no further processing will be done on the node.

The macro $RETRY evaluates to an integer value, set to 0 first time a node is run, and is incremented each time for each time the node is retried. The macro $MAX_RETRIES is the value set for NumberOfRetries. These macros may be used as arguments passed to a PRE or POST script.

The ABORT-DAG-ON keyword provides a way to abort the entire DAG if a given node returns a specific exit code. The syntax for ABORT-DAG-ON is

ABORT-DAG-ON JobName AbortExitValue [RETURN DAGReturnValue]

If the return value of the node specified by JobName matches AbortExitValue, the DAG is immediately aborted. A DAG abort differs from a node failure, in that a DAG abort causes all nodes within the DAG to be stopped immediately. This includes removing the jobs in nodes that are currently running. A node failure differs, as it would allow the DAG to continue running, until no more progress can be made due to dependencies.

The behavior differs based on the existence of PRE and/or POST scripts. If a PRE script returns the AbortExitValue value, the DAG is immediately aborted. If the HTCondor job within a node returns the AbortExitValue value, the DAG is aborted if the node has no POST script. If the POST script returns the AbortExitValue value, the DAG is aborted.

An abort overrides node retries. If a node returns the abort exit value, the DAG is aborted, even if the node has retry specified.

When a DAG aborts, by default it exits with the node return value that caused the abort. This can be changed by using the optional RETURN keyword along with specifying the desired DAGReturnValue. The DAG abort return value can be used for DAGs within DAGs, allowing an inner DAG to cause an abort of an outer DAG.

A DAG return value other than 0, 1, or 2 will cause the condor_dagman job to stay in the queue after it exits and get retried, unless the on_exit_remove expression in the .condor.sub file is manually modified.

Adding ABORT-DAG-ON for node C in the diamond-shaped DAG

    # File name: diamond.dag
    JOB  A  A.condor 
    JOB  B  B.condor 
    JOB  C  C.condor	
    JOB  D  D.condor
    Retry  C 3

causes the DAG to be aborted, if node C exits with a return value of 10. Any other currently running nodes, of which only node B is a possibility for this particular example, are stopped and removed. If this abort occurs, the return value for the DAG is 1. Variable Values Associated with Nodes

Macros defined for DAG nodes can be used within the submit description file of the node job. The VARS keyword provides a method for defining a macro. Macros are defined on a per-node basis, using the syntax

VARS JobName macroname="string" [macroname="string"... ]

The macro may be used within the submit description file of the relevant node. A macroname may contain alphanumeric characters (a-z, A-Z, and 0-9) and the underscore character. The space character delimits macros, such that there may be more than one macro defined on a single line. Multiple lines defining macros for the same node are permitted.

Correct syntax requires that the string must be enclosed in double quotes. To use a double quote mark within a string, escape the double quote mark with the backslash character (\). To add the backslash character itself, use two backslashes (\\).

A restriction is that the macroname itself cannot begin with the string queue, in any combination of upper or lower case letters.


If the DAG input file contains

    # File name: diamond.dag
    JOB  A  A.submit 
    JOB  B  B.submit 
    JOB  C  C.submit	
    JOB  D  D.submit
    VARS A state="Wisconsin"

then the submit description file A.submit may use the macro state. Consider this submit description file A.submit:

    # file name: A.submit
    executable = A.exe
    log        = A.log
    arguments  = "$(state)"
The macro value expands to become a command-line argument in the invocation of the job. The job is invoked with
A.exe Wisconsin

The use of macros may allow a reduction in the number of distinct submit description files. A separate example shows this intended use of VARS. In the case where the submit description file for each node varies only in file naming, macros reduce the number of submit description files to one.

The example uses a single submit description file in the DAG input file, and uses the VARS entry to name files used by each job.

The relevant portion of the DAG input file appears as

    JOB A theonefile.sub
    JOB B theonefile.sub
    JOB C theonefile.sub

    VARS A filename="A"
    VARS B filename="B"
    VARS C filename="C"

The submit description file appears as

    # submit description file called:  theonefile.sub
    executable   = progX
    output       = $(filename)
    error        = error.$(filename)
    log          = $(filename).log

For a DAG such as this one, but with thousands of nodes, the ability to write and maintain a single submit description file together with a single, yet more complex, DAG input file is worthwhile.

Multiple macroname definitions

If a macro name for a specific node in a DAG is defined more than once, as it would be with the partial file contents

  JOB job1 job1.submit
  VARS job1 a="foo"
  VARS job1 a="bar"
a warning is written to the log, of the format
Warning: VAR <macroname> is already defined in job <JobName>
Discovered at file "<DAG input file name>", line <line number>

The behavior of DAGMan is such that all definitions for the macro exist, but only the last one defined is used as the variable's value. Using this example, if the job1.submit submit description file contains

  arguments = "$(a)"
then the argument will be bar.

Special characters within VARS string definitions

The value defined for a macro may contain spaces and tabs. It is also possible to have double quote marks and backslashes within a value. It is not possible to have single quote marks within a value. In order to have spaces or tabs within a value, use the New Syntax format for the arguments submit command, as described in section 11. Escapes for double quote marks depend on whether the New Syntax or Old Syntax format is used for the arguments submit command. Note that in both syntaxes, double quote marks require two levels of escaping: one level is for the parsing of the DAG input file, and the other level is for passing the resulting value through condor_submit.

As an example, here are only the relevant parts of a DAG input file. Note that the NodeA value for macro second contains a tab.

    VARS NodeA first="Alberto Contador"
    VARS NodeA second="\"\"Andy	Schleck\"\""
    VARS NodeA third="Lance\\ Armstrong"
    VARS NodeA misc="!@#$%^&*()_-=+=[]{}?/"
    VARS NodeB first="Lance_Armstrong"
    VARS NodeB second="\\\"Andreas_Kloden\\\""
    VARS NodeB third="Ivan\\_Basso"
    VARS NodeB misc="!@#$%^&*()_-=+=[]{}?/"

The New Syntax arguments line of the submit description file for NodeA is

  arguments = "'$(first)' '$(second)' '$(third)' '$(misc)'"
The single quotes around each variable reference are only necessary if the variable value may contain spaces or tabs. The resulting values passed to the NodeA executable are
  Alberto Contador
  "Andy	Schleck"
  Lance\ Armstrong

The Old Syntax arguments line of the submit description file for NodeB is

  arguments = $(first) $(second) $(third) $(misc)

The resulting values passed to the NodeB executable are


Using special macros within a definition

The $(JOB) and $(RETRY) macros may be used within a definition of the string that defines a variable. This usage requires parentheses, such that proper macro substitution may take place when the macro's value is only a portion of the string.

Using VARS to define ClassAd attributes

The macroname may also begin with a + character, in which case it names a ClassAd attribute. For example, the VARS specification

  VARS NodeF +A="\"bob\""
results in the job ClassAd attribute
  A = "bob"
Note that ClassAd string values must be quoted, hence there are escaped quotes in the example above. The outer quotes are consumed in the parsing of the DAG input file, so the escaped inner quotes remain in the definition of the attribute value.

Continuing this example, it allows the HTCondor submit description file for NodeF to use the following line:

  arguments = "$$([A])"

The special macros may also be used. For example

  VARS NodeG +B="$(RETRY)"
places the numerical attribute
  B = 1
into the ClassAd when the NodeG job is run for a second time, which is the first retry and the value 1. Setting Priorities for Nodes

The PRIORITY keyword assigns a priority to a DAG node. The syntax for PRIORITY is

PRIORITY JobName PriorityValue

The node priority affects the order in which nodes that are ready at the same time will be submitted. Note that node priority does not override the DAG dependencies.

Node priority is mainly relevant if node submission is throttled via the -maxjobs or -maxidle command-line arguments or the DAGMAN_MAX_JOBS_SUBMITTED or DAGMAN_MAX_JOBS_IDLE configuration variables. Note that PRE scripts can affect the order in which jobs run, so DAGs containing PRE scripts may not run the nodes in exact priority order, even if doing so would satisfy the DAG dependencies.

The priority value is an integer (which can be negative). A larger numerical priority is better (will be run before a smaller numerical value). The default priority is 0.

Adding PRIORITY for node C in the diamond-shaped DAG

    # File name: diamond.dag
    JOB  A  A.condor 
    JOB  B  B.condor 
    JOB  C  C.condor	
    JOB  D  D.condor
    Retry  C 3

This will cause node C to be submitted before node B. Without this priority setting for node C, node B would be submitted first.

Priorities are propagated to children, to SUBDAGs, and to the HTCondor job itself, via the JobPrio attribute in the job's ClassAd. The priority is defined to be the maximum of the DAG PRIORITY directive for the job itself and the PRIORITYs of all its parents. Here is an example to clarify:

    # File name: priorities.dag
JOB A A.condor
JOB B B.condor
JOB C C.condor

In this example, node B is a child of nodes A and C. Node B's priority is initially set to 0, but its priority becomes 60, because that is the maximum of its initial priority of 0, and the priorities of its parents A with priority 60 and C with priority 5. Node D has only parent node C. Since the priority of node D will become at least as big as that of its parent node C, node D is assigned a priority of 100. And, all nodes in the D SUBDAG will have priority at least 100. This priority is assigned by DAGMan. There is no way to change the priority in the submit description file for a job, as DAGMan will override any priority command placed in a submit description file. The implication of this priority propagation is that for DAGs with a large number of edges (representing dependencies), the priorities of child nodes far from the root nodes will tend to be the same. The priorities of the leaf nodes of a tree-shaped DAG, or of DAGs with a relatively small number of dependencies, will not tend to be the same. Limiting the Number of Submitted Job Clusters within a DAG

In order to limit the number of submitted job clusters within a DAG, the nodes may be placed into categories by assignment of a name. Then, a maximum number of submitted clusters may be specified for each category.

The CATEGORY keyword assigns a category name to a DAG node. The syntax for CATEGORY is

CATEGORY JobName CategoryName

Category names cannot contain white space.

The MAXJOBS keyword limits the number of submitted job clusters on a per category basis. The syntax for MAXJOBS is

MAXJOBS CategoryName MaxJobsValue

If the number of submitted job clusters for a given category reaches the limit, no further job clusters in that category will be submitted until other job clusters within the category terminate. If MAXJOBS is not set for a defined category, then there is no limit placed on the number of submissions within that category.

Note that a single invocation of condor_submit results in one job cluster. The number of HTCondor jobs within a cluster may be greater than 1.

The configuration variable DAGMAN_MAX_JOBS_SUBMITTED and the condor_submit_dag -maxjobs command-line option are still enforced if these CATEGORY and MAXJOBS throttles are used.

Please see the end of section 2.10.8 on DAG Splicing for a description of the interaction between categories and splices. Configuration Specific to a DAG

All configuration variables and their definitions that relate to DAGMan may be found in section 3.3.25.

Configuration variables for condor_dagman can be specified in several ways, as given within the ordered list:

  1. In an HTCondor configuration file.
  2. With an environment variable. Prepend the string _CONDOR_ to the configuration variable's name.
  3. With a line in the DAG input file using the keyword CONFIG, such that there is a configuration file specified that is specific to an instance of condor_dagman. The configuration file specification may instead be specified on the condor_submit_dag command line using the -config option.
  4. For some configuration variables, condor_submit_dag command line argument specifies a configuration variable. For example, the configuration variable DAGMAN_MAX_JOBS_SUBMITTED has the corresponding command line argument -maxjobs.

For this ordered list, configuration values specified or parsed later in the list override ones specified earlier. For example, a value specified on the condor_submit_dag command line overrides corresponding values in any configuration file. And, a value specified in a DAGMan-specific configuration file overrides values specified in a general HTCondor configuration file.

The CONFIG keyword within the DAG input file specifies a configuration file to be used to set configuration variables related to condor_dagman when running this DAG. The syntax for CONFIG is

CONFIG ConfigFileName

As an example, if the DAG input file contains:

  CONFIG dagman.config
then the configuration values in file dagman.config will be used for this DAG. If the contents of file dagman.config is
then this configuration is defined for this DAG.

Only a single configuration file can be specified for a given condor_dagman run. For example, if one file is specified within a DAG input file, and a different file is specified on the condor_submit_dag command line, this is a fatal error at submit time. The same is true if different configuration files are specified in multiple DAG input files and referenced in a single condor_submit_dag command.

If multiple DAGs are run in a single condor_dagman run, the configuration options specified in the condor_dagman configuration file, if any, apply to all DAGs, even if some of the DAGs specify no configuration file.

Configuration variables that are not for condor_dagman and not utilized by DaemonCore, yet are specified in a condor_dagman-specific configuration file are ignored. Optimization of Submission Time

condor_dagman works by watching log files for events, such as submission, termination, and going on hold. When a new job is ready to be run, it is submitted to the condor_schedd, which needs to acquire a computing resource. Acquisition requires the condor_schedd to contact the central manager and get a claim on a machine, and this claim cycle can take many minutes.

Configuration variable DAGMAN_HOLD_CLAIM_TIME avoids the wait for a negotiation cycle. When set to a non zero value, the condor_schedd keeps a claim idle, such that the condor_startd delays in shifting from the Claimed to the Preempting state (see Figure 3.1). Thus, if another job appears that is suitable for the claimed resource, then the condor_schedd will submit the job directly to the condor_startd, avoiding the wait and overhead of a negotiation cycle. This results in a speed up of job completion, especially for linear DAGs in pools that have lengthy negotiation cycle times.

By default, DAGMAN_HOLD_CLAIM_TIME is 20, causing a claim to remain idle for 20 seconds, during which time a new job can be submitted directly to the already-claimed condor_startd. A value of 0 means that claims are not held idle for a running DAG. If a DAG node has no children, the value of DAGMAN_HOLD_CLAIM_TIME will be ignored; the KeepClaimIdle attribute will not be defined in the job ClassAd of the node job, unless the job requests it using the submit command keep_claim_idle. Single Submission of Multiple, Independent DAGs

A single use of condor_submit_dag may execute multiple, independent DAGs. Each independent DAG has its own, distinct DAG input file. These DAG input files are command-line arguments to condor_submit_dag.

Internally, all of the independent DAGs are combined into a single, larger DAG, with no dependencies between the original independent DAGs. As a result, any generated Rescue DAG file represents all of the original independent DAGs with a single DAG. The file name of this Rescue DAG is based on the DAG input file listed first within the command-line arguments. For example, assume that three independent DAGs are submitted with

  condor_submit_dag A.dag B.dag C.dag
The first listed is A.dag. The remainder of the specialized file name adds a suffix onto this first DAG input file name, A.dag. The suffix is _multi.rescue<XXX>, where <XXX> is substituted by the 3-digit number of the Rescue DAG created as defined in section 2.10.9. The first time a Rescue DAG is created for the example, it will have the file name A.dag_multi.rescue001.

Other files such as dagman.out and the lock file also have names based on this first DAG input file.

The success or failure of the independent DAGs is well defined. When multiple, independent DAGs are submitted with a single command, the success of the composite DAG is defined as the logical AND of the success of each independent DAG. This implies that failure is defined as the logical OR of the failure of any of the independent DAGs.

By default, DAGMan internally renames the nodes to avoid node name collisions. If all node names are unique, the renaming of nodes may be disabled by setting the configuration variable DAGMAN_MUNGE_NODE_NAMES to False (see 3.3.25). A DAG Within a DAG Is a SUBDAG

The organization and dependencies of the jobs within a DAG are the keys to its utility. Some DAGs are naturally constructed hierarchically, such that a node within a DAG is also a DAG. HTCondor DAGMan handles this situation easily. DAGs can be nested to any depth.

One of the highlights of using the SUBDAG feature is that portions of a DAG may be constructed and modified during the execution of the DAG. A drawback may be that each SUBDAG causes its own distinct job submission of condor_dagman, leading to a larger number of jobs, together with their potential need of carefully constructed policy configuration to throttle node submission or execution.

Since more than one DAG is being discussed, here is terminology introduced to clarify which DAG is which. Reuse the example diamond-shaped DAG as given in Figure 2.2. Assume that node B of this diamond-shaped DAG will itself be a DAG. The DAG of node B is called a SUBDAG, inner DAG, or lower-level DAG. The diamond-shaped DAG is called the outer or top-level DAG.

Work on the inner DAG first. Here is a very simple linear DAG input file used as an example of the inner DAG.

    # File name: inner.dag
    JOB  X  X.submit
    JOB  Y  Y.submit
    JOB  Z  Z.submit

The HTCondor submit description file, used by condor_dagman, corresponding to inner.dag will be named inner.dag.condor.sub. The DAGMan submit description file is always named <DAG file name>.condor.sub. Each DAG or SUBDAG results in the submission of condor_dagman as an HTCondor job, and condor_submit_dag creates this submit description file.

The preferred presentation of the DAG input file for the outer DAG is

# File name: diamond.dag
    JOB  A  A.submit 
    SUBDAG EXTERNAL  B  inner.dag
    JOB  C  C.submit	
    JOB  D  D.submit

The preferred presentation is equivalent to

# File name: diamond.dag
    JOB  A  A.submit 
    JOB  B  inner.dag.condor.sub
    JOB  C  C.submit	
    JOB  D  D.submit

Within the outer DAG's input file, the SUBDAG keyword specifies a special case of a JOB node, where the job is itself a DAG.

The syntax for each SUBDAG entry is

SUBDAG EXTERNAL JobName DagFileName [DIR directory] [NOOP] [DONE]

The optional specifications of DIR, NOOP, and DONE, if used, must appear in this order within the entry.

A SUBDAG node is essentially the same as any other node, except that the DAG input file for the inner DAG is specified, instead of the HTCondor submit file. The keyword EXTERNAL means that the SUBDAG is run within its own instance of condor_dagman.

NOOP and DONE for SUBDAG nodes have the same effect that they do for JOB nodes.

Here are details that affect SUBDAGs: DAG Splicing

A weakness in scalability exists when submitting a DAG within a DAG, which is called a SUBDAG. Each executing independent DAG requires its own invocation of condor_dagman to be running. The outer DAG has an instance of condor_dagman, and each named SUBDAG has an instance of condor_dagman. The scaling issue presents itself when the same semantic DAG is reused hundreds or thousands of times in a larger DAG. Further, there may be many Rescue DAGs created if a problem occurs. To alleviate these concerns, the DAGMan language introduces the concept of graph splicing.

A splice is a named instance of a subgraph which is specified in a separate DAG file. The splice is treated as a whole entity during dependency specification in the including DAG. The same DAG file may be reused as differently named splices, each one incorporating a copy of the dependency graph (and nodes therein) into the including DAG. Any splice in an including DAG may have dependencies between the sets of initial and final nodes. A splice may be incorporated into an including DAG without any dependencies; it is considered a disjoint DAG within the including DAG. The nodes within a splice are scoped according to a hierarchy of names associated with the splices, as the splices are parsed from the top level DAG file. The scoping character to describe the inclusion hierarchy of nodes into the top level dag is '+'. This character is chosen due to a restriction in the allowable characters which may be in a file name across the variety of platforms that HTCondor supports. In any DAG input file, all splices must have unique names, but the same splice name may be reused in different DAG input files.

HTCondor does not detect nor support splices that form a cycle within the DAG. A DAGMan job that causes a cyclic inclusion of splices will eventually exhaust available memory and crash.

The SPLICE keyword in a DAG input file creates a named instance of a DAG as specified in another file as an entity which may have PARENT and CHILD dependencies associated with other splice names or node names in the including DAG file. The syntax for SPLICE is

SPLICE SpliceName DagFileName [DIR directory]

After parsing incorporates a splice, all nodes within the spice become nodes within the including DAG.

The following series of examples illustrate potential uses of splicing. To simplify the examples, presume that each and every job uses the same, simple HTCondor submit description file:

  # BEGIN SUBMIT FILE submit.condor
  executable   = /bin/echo
  arguments    = OK
  universe     = vanilla
  output       = $(jobname).out
  error        = $(jobname).err
  log          = submit.log
  notification = NEVER
  # END SUBMIT FILE submit.condor

This first simple example splices a diamond-shaped DAG in between the two nodes of a top level DAG. Here is the DAG input file for the diamond-shaped DAG:

  # BEGIN DAG FILE diamond.dag
  JOB A submit.condor
  VARS A jobname="$(JOB)"

  JOB B submit.condor
  VARS B jobname="$(JOB)"

  JOB C submit.condor
  VARS C jobname="$(JOB)"

  JOB D submit.condor
  VARS D jobname="$(JOB)"

  # END DAG FILE diamond.dag

The top level DAG incorporates the diamond-shaped splice:

  # BEGIN DAG FILE toplevel.dag
  JOB X submit.condor
  VARS X jobname="$(JOB)"

  JOB Y submit.condor
  VARS Y jobname="$(JOB)"

  # This is an instance of diamond.dag, given the symbolic name DIAMOND
  SPLICE DIAMOND diamond.dag

  # Set up a relationship between the nodes in this dag and the splice


  # END DAG FILE toplevel.dag

Figure 2.3 illustrates the resulting top level DAG and the dependencies produced. Notice the naming of nodes scoped with the splice name. This hierarchy of splice names assures unique names associated with all nodes.

Figure 2.3: The diamond-shaped DAG spliced between two nodes.

Figure 2.4 illustrates the starting point for a more complex example. The DAG input file X.dag describes this X-shaped DAG. The completed example displays more of the spatial constructs provided by splices. Pay particular attention to the notion that each named splice creates a new graph, even when the same DAG input file is specified.


  JOB A submit.condor
  VARS A jobname="$(JOB)"

  JOB B submit.condor
  VARS B jobname="$(JOB)"

  JOB C submit.condor
  VARS C jobname="$(JOB)"

  JOB D submit.condor
  VARS D jobname="$(JOB)"

  JOB E submit.condor
  VARS E jobname="$(JOB)"

  JOB F submit.condor
  VARS F jobname="$(JOB)"

  JOB G submit.condor
  VARS G jobname="$(JOB)"

  # Make an X-shaped dependency graph

  # END DAG FILE X.dag

Figure 2.4: The X-shaped DAG.

File s1.dag continues the example, presenting the DAG input file that incorporates two separate splices of the X-shaped DAG. Figure 2.5 illustrates the resulting DAG.

  # BEGIN DAG FILE s1.dag

  JOB A submit.condor
  VARS A jobname="$(JOB)"

  JOB B submit.condor
  VARS B jobname="$(JOB)"

  # name two individual splices of the X-shaped DAG
  SPLICE X1 X.dag
  SPLICE X2 X.dag

  # Define dependencies
  # A must complete before the initial nodes in X1 can start
  # All final nodes in X1 must finish before 
  # the initial nodes in X2 can begin
  # All final nodes in X2 must finish before B may begin.

  # END DAG FILE s1.dag

Figure 2.5: The DAG described by s1.dag.

The top level DAG in the hierarchy of this complex example is described by the DAG input file toplevel.dag. Figure 2.6 illustrates the final DAG. Notice that the DAG has two disjoint graphs in it as a result of splice S3 not having any dependencies associated with it in this top level DAG.

  # BEGIN DAG FILE toplevel.dag

  JOB A submit.condor
  VARS A jobname="$(JOB)"

  JOB B submit.condor
  VARS B jobname="$(JOB)"

  JOB C submit.condor
  VARS C jobname="$(JOB)"

  JOB D submit.condor
  VARS D jobname="$(JOB)"

  # a diamond-shaped DAG

  # This splice of the X-shaped DAG can only run after
  # the diamond dag finishes
  SPLICE S2 X.dag

  # Since there are no dependencies for S3,
  # the following splice is disjoint 
  SPLICE S3 s1.dag

  # END DAG FILE toplevel.dag

Figure 2.6: The complex splice example DAG.

The DIR option specifies a working directory for a splice, from which the splice will be parsed and the containing jobs submitted. The directory associated with the splices' DIR specification will be propagated as a prefix to all nodes in the splice and any included splices. If a node already has a DIR specification, then the splice's DIR specification will be a prefix to the nodes and separated by a directory separator character. Jobs in included splices with an absolute path for their DIR specification will have their DIR specification untouched. Note that a DAG containing DIR specifications cannot be run in conjunction with the -usedagdir command-line argument to condor_submit_dag. A Rescue DAG generated by a DAG run with the -usedagdir argument will contain DIR specifications, so the Rescue DAG must be run without the -usedagdir argument.

Limitation: Splices and PRE or POST Scripts

A splice may not specify a PRE or POST script. The nodes of a splice are incorporated into a top level DAG; these nodes are scoped and named. Therefore, after incorporation, the node name given to the splice does not exist as a distinct entity.

To achieve the desired effect of having a PRE script associated with a splice, introduce a new NOOP node into the DAG with the splice as a dependency. Attach the PRE script to the NOOP node.

  # BEGIN DAG FILE example1.dag

  # Names a node with no associated node job, a NOOP node
  # Note that the file noop.submit must exist, but will not be used.
  JOB OnlyPreNode noop.submit NOOP

  # Attach a PRE script to the NOOP node
  SCRIPT PRE OnlyPreNode

  # Define the splice
  SPLICE TheSplice thenode.dag
  # Define the dependency
  PARENT OnlyPreNode CHILD TheSplice

  # END DAG FILE example1.dag

The same technique is used to achieve the effect of having a POST script associated with a splice. Introduce a new NOOP node into the DAG as a child of the splice, and attach the POST script to the NOOP node.

  # BEGIN DAG FILE example2.dag

  # Names a node with no associated node job, a NOOP node
  # Note that the file noop.submit must exist, but will not be used.
  JOB OnlyPostNode noop.submit NOOP

  # Attach a POST script to the NOOP node
  SCRIPT POST OnlyPostNode

  # Define the splice
  SPLICE TheSplice thenode.dag
  # Define the dependency
  PARENT TheSplice CHILD OnlyPostNode

  # END DAG FILE example2.dag

Limitation: Splices and the RETRY of a Node, use of VARS, or use of PRIORITY

The nodes of a splice are incorporated into a top level DAG; these nodes are scoped and named. Once incorporated in this way, the splice name cannot be used to cause RETRY of what would be the entire splice. RETRY is applied on a node basis, not on a splice basis.

The scoping and renaming of nodes within a splice also prevent the specification of VARS or PRIORITY either for nodes within a splice or for the splice as a whole. The node names will have changed.

Here is an example showing a RETRY that cannot work.

  # top level DAG input file
  JOB    A a.sub
  SPLICE B b.dag

  # cannot work, as B is not a node in the DAG once
  # splice B is incorporated

To effect RETRY on a specific node within a splice, the scoped name may be used. However, this subverts the intent of using a splice. Here is a similar example, assuming that RETRY is desired on just node X within the subgraph described by splice B.

  # top level DAG input file
  JOB    A a.sub
  SPLICE B b.dag

  # RETRY just node X within splice B;  assumes that
  # this top level DAG knows the node name within B

An alternative implementation when RETRY is desired on an entire subgraph of a DAG is to create and use a SUBDAG instead of a splice. This has the potential drawback of all SUBDAGs, in that the SUBDAG is its own distinct job, with its own instance of condor_dagman. Here is the same example, now defining job B as a SUBDAG, and effecting RETRY on that SUBDAG.

  # top level DAG input file
  JOB    A a.sub


Limitation: The Interaction of Categories and MAXJOBS with Splices

Categories normally refer only to nodes within a given splice. All of the assignments of nodes to a category, and the setting of the category throttle, should be done within a single DAG file. However, it is now possible to have categories include nodes from within more than one splice. To do this, the category name is prefixed with the '+' (plus) character. This tells DAGMan that the category is a cross-splice category. Towards deeper understanding, what this really does is prevent renaming of the category when the splice is incorporated into the upper-level DAG. The MAXJOBS specification for the category can appear in either the upper-level DAG file or one of the splice DAG files. It probably makes the most sense to put it in the upper-level DAG file.

Here is an example which applies a single limitation on submitted jobs, identifying the category with +init.

# relevant portion of file name: upper.dag

    SPLICE A splice1.dag
    SPLICE B splice2.dag

    MAXJOBS +init 2

# relevant portion of file name: splice1.dag

    JOB C C.sub
    CATEGORY C +init
    JOB D D.sub
    CATEGORY D +init

# relevant portion of file name: splice2.dag

    JOB X X.sub
    CATEGORY X +init
    JOB Y Y.sub
    CATEGORY Y +init

For both global and non-global category throttles, settings at a higher level in the DAG override settings at a lower level. In this example:

# relevant portion of file name: upper.dag

    SPLICE A lower.dag

    MAXJOBS A+catX 10
    MAXJOBS +catY 2

# relevant portion of file name: lower.dag

    MAXJOBS catX 5
    MAXJOBS +catY 1

the resulting throttle settings are 2 for the +catY category and 10 for the A+catX category in splice. Note that non-global category names are prefixed with their splice name(s), so to refer to a non-global category at a higher level, the splice name must be included. FINAL node

A FINAL node is a single and special node that is always run at the end of the DAG, even if previous nodes in the DAG have failed. A FINAL node can be used for tasks such as cleaning up intermediate files and checking the output of previous nodes. The FINAL keyword in the DAG input file specifies a node job to be run at the end of the DAG.

The syntax used for the FINAL entry is

FINAL JobName SubmitDescriptionFileName [DIR directory] [NOOP]

The FINAL node within the DAG is identified by JobName, and the HTCondor job is described by the contents of the HTCondor submit description file given by SubmitDescriptionFileName.

The keywords DIR and NOOP are as detailed in section 2.10.2. If both DIR and NOOP are used, they must appear in the order shown within the syntax specification.

There may only be one FINAL node in a DAG. A parse error will be logged by the condor_dagman job in the dagman.out file, if more than one FINAL node is specified.

The FINAL node is virtually always run. It is run if the condor_dagman job is removed with condor_rm. The only case in which a FINAL node is not run is if the configuration variable DAGMAN_STARTUP_CYCLE_DETECT is set to True, and a cycle is detected at start up time. If DAGMAN_STARTUP_CYCLE_DETECT is set to False and a cycle is detected during the course of the run, the FINAL node will be run.

The success or failure of the FINAL node determines the success or failure of the entire DAG, overriding the status of all previous nodes. This includes any status specified by any ABORT-DAG-ON specification that has taken effect. If some nodes of a DAG fail, but the FINAL node succeeds, the DAG will be considered successful. Therefore, it is important to be careful about setting the exit status of the FINAL node.

The $DAG_STATUS and $FAILED_COUNT macros can be used both as PRE and POST script arguments, and in node job submit description files. As an example of this, here are the partial contents of the DAG input file,

    FINAL final_node final_node.sub

and here are the partial contents of the submit description file, final_node.sub

    arguments = "$(DAG_STATUS) $(FAILED_COUNT)"

If there is a FINAL node specified for a DAG, it will be run at the end of the workflow. If this FINAL node must not do anything in certain cases, use the $DAG_STATUS and $FAILED_COUNT macros to take appropriate actions. Here is an example of that behavior. It uses a PRE script that aborts if the DAG has been removed with condor_rm, which, in turn, causes the FINAL node to be considered failed without actually submitting the HTCondor job specified for the node. Partial contents of the DAG input file:

    FINAL final_node final_node.sub
    SCRIPT PRE final_node $DAG_STATUS

and partial contents of the Perl PRE script,

    #! /usr/bin/env perl
    if ($ARGV[0] eq 4) {

There are restrictions on the use of a FINAL node. There is no DONE option for the HTCondor job. And, other nodes may not reference the FINAL node in specifications of

2.10.9 The Rescue DAG

Any time a DAG exits unsuccessfully, DAGMan generates a Rescue DAG. The Rescue DAG records the state of the DAG, with information such as which nodes completed successfully, and the Rescue DAG will be used when the DAG is again submitted. With the Rescue DAG, nodes that have already successfully completed are not re-run.

There are a variety of circumstances under which a Rescue DAG is generated. If a node in the DAG fails, the DAG does not exit immediately; the remainder of the DAG is continued until no more forward progress can be made based on the DAG's dependencies. At this point, DAGMan produces the Rescue DAG and exits. A Rescue DAG is produced on Unix platforms if the condor_dagman job itself is removed with condor_rm. On Windows, a Rescue DAG is not generated in this situation, but re-submitting the original DAG will invoke a lower-level recovery functionality, and it will produce similar behavior to using a Rescue DAG. A Rescue DAG is produced when a node sets and triggers an ABORT-DAG-ON event with a non-zero return value. A zero return value constitutes successful DAG completion, and therefore a Rescue DAG is not generated.

By default, if a Rescue DAG exists, it will be used when the DAG is submitted specifying the original DAG input file. If more than one Rescue DAG exists, the newest one will be used. By using the Rescue DAG, DAGMan will avoid re-running nodes that completed successfully in the previous run. Note that passing the -force option to condor_submit_dag or condor_dagman will cause condor_dagman to not use any existing rescue DAG. This means that previously-completed node jobs will be re-run.

The granularity defining success or failure in the Rescue DAG is the node. For a node that fails, all parts of the node will be re-run, even if some parts were successful the first time. For example, if a node's PRE script succeeds, but then the node's HTCondor job cluster fails, the entire node, including the PRE script, will be re-run. A job cluster may result in the submission of multiple HTCondor jobs. If one of the jobs within the cluster fails, the node fails. Therefore, the Rescue DAG will re-run the entire node, implying the submission of the entire cluster of jobs, not just the one(s) that failed.

Statistics about the failed DAG execution are presented as comments at the beginning of the Rescue DAG input file.

Rescue DAG Naming

The file name of the Rescue DAG is obtained by appending the string .rescue<XXX> to the original DAG input file name. Values for <XXX> start at 001 and continue to 002, 003, and beyond. The configuration variable DAGMAN_MAX_RESCUE_NUM sets a maximum value for <XXX>; see section 3.3.25 for the complete definition of this configuration variable. If you hit the DAGMAN_MAX_RESCUE_NUM limit, the last Rescue DAG file is overwritten if the DAG fails again.

If a Rescue DAG exists when the original DAG is re-submitted, the Rescue DAG with the largest magnitude value for <XXX> will be used, and its usage is implied.


Here is an example showing file naming and DAG submission for the case of a failed DAG. The initial DAG is submitted with

  condor_submit_dag  my.dag
A failure of this DAG results in the Rescue DAG named my.dag.rescue001. The DAG is resubmitted using the same command:
  condor_submit_dag  my.dag
This resubmission of the DAG uses the Rescue DAG file my.dag.rescue001, because it exists. Failure of this Rescue DAG results in another Rescue DAG called my.dag.rescue002. If the DAG is again submitted, using the same command as with the first two submissions, but not repeated here, then this third submission uses the Rescue DAG file my.dag.rescue002, because it exists, and because the value 002 is larger in magnitude than 001.

Backtracking to an Older Rescue DAG

To explicitly specify a particular Rescue DAG, use the optional command-line argument -dorescuefrom with condor_submit_dag. Note that this will have the side effect of renaming existing Rescue DAG files with larger magnitude values of <XXX>. Each renamed file has its existing name appended with the string .old. For example, assume that my.dag has failed 4 times, resulting in the Rescue DAGs named my.dag.rescue001, my.dag.rescue002, my.dag.rescue003, and my.dag.rescue004. A decision is made to re-run using my.dag.rescue002. The submit command is

  condor_submit_dag  -dorescuefrom 2  my.dag
The DAG specified by the DAG input file my.dag.rescue002 is submitted. And, the existing Rescue DAG my.dag.rescue003 is renamed to be my.dag.rescue003.old, while the existing Rescue DAG my.dag.rescue004 is renamed to be my.dag.rescue004.old.

Special Cases

Note that if multiple DAG input files are specified on the condor_submit_dag command line, a single Rescue DAG encompassing all of the input DAGs is generated. A DAG file containing splices also produces a single Rescue DAG file. On the other hand, a DAG containing sub-DAGs will produce a separate Rescue DAG for each sub-DAG that is queued (and for the top-level DAG).

If the Rescue DAG file is generated before all retries of a node are completed, then the Rescue DAG file will also contain Retry entries. The number of retries will be set to the appropriate remaining number of retries. The configuration variable DAGMAN_RESET_RETRIES_UPON_RESCUE, section 3.3.25, controls whether or not node retries are reset in a Rescue DAG.

Partial versus Full Rescue DAGs

As of HTCondor version 7.7.2, the Rescue DAG file is a partial DAG file, not a complete DAG input file as in the past.

A partial Rescue DAG file contains only information about which nodes are done, and the number of retries remaining for nodes with retries. It does not contain information such as the actual DAG structure and the specification of the submit description file for each node job. Partial Rescue DAGs are automatically parsed in combination with the original DAG input file, which contains information about the DAG structure. This updated implementation means that a change in the original DAG input file, such as specifying a different submit description file for a node job, will take effect when running the partial Rescue DAG. In other words, you can fix mistakes in the original DAG file while still gaining the benefit of using the Rescue DAG.

To use a partial Rescue DAG, you must re-run condor_submit_dag on the original DAG file, not the Rescue DAG file.

Note that the existence of a DONE specification in a partial Rescue DAG for a node that no longer exists in the original DAG input file is a warning, as opposed to an error, unless the DAGMAN_USE_STRICT configuration variable is set to a value of 1 or higher (which is now the default). Comment out the line with DONE in the partial Rescue DAG file to avoid a warning or error.

The previous (prior to version 7.7.2) behavior of producing full DAG input file as the Rescue DAG is obtained by setting the configuration variable DAGMAN_WRITE_PARTIAL_RESCUE to the non-default value of False. Note that the option to generate full Rescue DAGs is likely to disappear some time during the 8.3 series.

To run a full Rescue DAG, either one left over from an older version of DAGMan, or one produced by setting DAGMAN_WRITE_PARTIAL_RESCUE to False, directly specify the full Rescue DAG file on the command line instead of the original DAG file. For example:

  condor_submit_dag my.dag.rescue002

Attempting to re-submit the original DAG file, if the Rescue DAG file is a complete DAG, will result in a parse failure.

Rescue DAG Generated When There Are Parse Errors

Starting in HTCondor version 7.5.5, passing the -DumpRescue option to either condor_dagman or condor_submit_dag causes condor_dagman to output a Rescue DAG file, even if the parsing of a DAG input file fails. In this parse failure case, condor_dagman produces a specially named Rescue DAG containing whatever it had successfully parsed up until the point of the parse error. This Rescue DAG may be useful in debugging parse errors in complex DAGs, especially ones using splices. This incomplete Rescue DAG is not meant to be used when resubmitting a failed DAG. Note that this incomplete Rescue DAG generated by the -DumpRescue option is a full DAG input file, as produced by versions of HTCondor prior to HTCondor version 7.7.2. It is not a partial Rescue DAG file, regardless of the value of the configuration variable DAGMAN_WRITE_PARTIAL_RESCUE.

To avoid confusion between this incomplete Rescue DAG generated in the case of a parse failure and a usable Rescue DAG, a different name is given to the incomplete Rescue DAG. The name appends the string .parse_failed to the original DAG input file name. Therefore, if the submission of a DAG with

  condor_submit_dag  my.dag
has a parse failure, the resulting incomplete Rescue DAG will be named my.dag.parse_failed.

To further prevent one of these incomplete Rescue DAG files from being used, a line within the file contains the single keyword REJECT. This causes condor_dagman to reject the DAG, if used as a DAG input file. This is done because the incomplete Rescue DAG may be a syntactically correct DAG input file. It will be incomplete relative to the original DAG, such that if the incomplete Rescue DAG could be run, it could erroneously be perceived as having successfully executed the desired workflow, when, in fact, it did not.

2.10.10 DAG Recovery

DAG recovery restores the state of a DAG upon resubmission. Recovery is accomplished by reading the .nodes.log file that is used to enforce the dependencies of the DAG. The DAG can then continue towards completion.

Recovery is different than a Rescue DAG. Recovery is appropriate when no Rescue DAG has been created. There will be no Rescue DAG if the machine running the condor_dagman job crashes, or if the condor_schedd daemon crashes, or if the condor_dagman job crashes, or if the condor_dagman job is placed on hold.

Much of the time, when a not-completed DAG is re-submitted, it will automatically be placed into recovery mode due to the existence and contents of a lock file created as the DAG is first run. In recovery mode, the .nodes.log is used to identify nodes that have completed and should not be re-submitted.

When debugging a DAG in which something has gone wrong, a first determination is whether a resubmission will use a Rescue DAG or benefit from recovery. The existence of a Rescue DAG means that recovery would be inappropriate. A Rescue DAG is has a file name ending in .rescue<XXX>, where <XXX> is replaced by a 3-digit number.

Determine if a DAG ever completed (independent of whether it was successful or not) by looking at the last lines of the .dagman.out file. If there is a line similar to

  (condor_DAGMAN) pid 445 EXITING WITH STATUS 0
then the DAG completed. This line explains that the condor_dagman job finished normally. If there is no line similar to this at the end of the .dagman.out file, and output from condor_q shows that the condor_dagman job for the DAG being debugged is not in the queue, then recovery is indicated.

2.10.11 Visualizing DAGs with dot

It can be helpful to see a picture of a DAG. DAGMan can assist you in visualizing a DAG by creating the input files used by the AT&T Research Labs graphviz package. dot is a program within this package, available from, and it is used to draw pictures of DAGs.

DAGMan produces one or more dot files as the result of an extra line in a DAGMan input file. The line appears as


This creates a file called which contains a specification of the DAG before any jobs within the DAG are submitted to HTCondor. The file is used to create a visualization of the DAG by using this file as input to dot. This example creates a Postscript file, with a visualization of the DAG:

    dot -Tps -o

Within the DAGMan input file, the DOT command can take several optional parameters:

If conflicting parameters are used in a DOT command, the last one listed is used.

2.10.12 Capturing the Status of Nodes in a File

DAGMan can capture the status of the overall DAG and all DAG nodes in a node status file, such that the user or a script can monitor this status. This file is periodically rewritten while the DAG runs. To enable this feature, the DAG input file must contain a line with the NODE_STATUS_FILE keyword.

The syntax for a NODE_STATUS_FILE specification is

NODE_STATUS_FILE statusFileName [minimumUpdateTime]

The status file is written on the machine on which the DAG is submitted; its location is given by statusFileName, and it may be a full path and file name.

The optional minimumUpdateTime specifies the minimum number of seconds that must elapse between updates to the node status file. This setting exists to avoid having DAGMan spend too much time writing the node status file for very large DAGs. If no value is specified, no limit is set. The node status file can be updated at most once per DAGMAN_USER_LOG_SCAN_INTERVAL, as defined at section 3.3.25, no matter how small the minimumUpdateTime value. Also, the node status file will be updated when the DAG finishes, whether successful or not, even if minimumUpdateTime seconds have not elapsed since the last update.

As an example, if the DAG input file contains the line

  NODE_STATUS_FILE my.dag.status 30
the file my.dag.status will be rewritten at intervals of 30 seconds or more.

This node status file is overwritten each time it is updated. Therefore, it only holds information about the current status of each node; it does not provide a history of the node status. The file contents do not distinguish between HTCondor jobs and Stork jobs.

NOTE: HTCondor version 8.1.6 changes the format of the node status file.

The node status file is a collection of ClassAds in New ClassAd format. There is one ClassAd for the overall status of the DAG, one ClassAd for the status of each node, and one ClassAd with the time at which the node status file was completed as well as the time of the next update.

Here is an example portion of a node status file:

  Type = "DagStatus";
  DagFiles = {
  Timestamp = 1399674138; /* "Fri May  9 17:22:18 2014" */
  DagStatus = 3; /* "STATUS_SUBMITTED ()" */
  NodesTotal = 12;
  NodesDone = 11;
  NodesPre = 0;
  NodesQueued = 1;
  NodesPost = 0;
  NodesReady = 0;
  NodesUnready = 0;
  NodesFailed = 0;
  JobProcsHeld = 0;
  JobProcsIdle = 1;
  Type = "NodeStatus";
  Node = "A";
  NodeStatus = 5; /* "STATUS_DONE" */
  StatusDetails = "";
  RetryCount = 0;
  JobProcsQueued = 0;
  JobProcsHeld = 0;
  Type = "NodeStatus";
  Node = "C";
  NodeStatus = 3; /* "STATUS_SUBMITTED" */
  StatusDetails = "idle";
  RetryCount = 0;
  JobProcsQueued = 1;
  JobProcsHeld = 0;
  Type = "StatusEnd";
  EndTime = 1399674138; /* "Fri May  9 17:22:18 2014" */
  NextUpdate = 1399674141; /* "Fri May  9 17:22:21 2014" */

Possible DagStatus and NodeStatus attribute values are:

A NODE_STATUS_FILE keyword inside any splice is ignored. If multiple DAG files are specified on the condor_submit_dag command line, and more than one specifies a node status file, the first specification takes precedence.

2.10.13 A Machine-Readable Event History, the jobstate.log File

DAGMan can produce a machine-readable history of events. The jobstate.log file is designed for use by the Pegasus Workflow Management System, which operates as a layer on top of DAGMan. Pegasus uses the jobstate.log file to monitor the state of a workflow. The jobstate.log file can used by any automated tool for the monitoring of workflows.

DAGMan produces this file when the keyword JOBSTATE_LOG is in the DAG input file. The syntax for JOBSTATE_LOG is

JOBSTATE_LOG JobstateLogFileName

No more than one jobstate.log file can be created by a single instance of condor_dagman. If more than one jobstate.log file is specified, the first file name specified will take effect, and a warning will be printed in the dagman.out file when subsequent JOBSTATE_LOG specifications are parsed. Multiple specifications may exist in the same DAG file, within splices, or within multiple, independent DAGs run with a single condor_dagman instance.

The jobstate.log file can be considered a filtered version of the dagman.out file, in a machine-readable format. It contains the actual node job events that from condor_dagman, plus some additional meta-events.

The jobstate.log file is different from the node status file, in that the jobstate.log file is appended to, rather than being overwritten as the DAG runs. Therefore, it contains a history of the DAG, rather than a snapshot of the current state of the DAG.

There are 5 line types in the jobstate.log file. Each line begins with a Unix timestamp in the form of seconds since the Epoch. Fields within each line are separated by a single space character.

DAGMan start
This line identifies the condor_dagman job. The formatting of the line is

timestamp INTERNAL *** DAGMAN_STARTED dagmanCondorID ***

The dagmanCondorID field is the condor_dagman job's ClusterId attribute, a period, and the ProcId attribute.

DAGMan exit
This line identifies the completion of the condor_dagman job. The formatting of the line is

timestamp INTERNAL *** DAGMAN_FINISHED exitCode ***

The exitCode field is value the condor_dagman job returns upon exit.

Recovery started
If the condor_dagman job goes into recovery mode, this meta-event is printed. During recovery mode, events will only be printed in the file if they were not already printed before recovery mode started. The formatting of the line is


Recovery finished or Recovery failure
At the end of recovery mode, either a RECOVERY_FINISHED or RECOVERY_FAILURE meta-event will be printed, as appropriate.

The formatting of the line is




This line is used for all other event and meta-event types. The formatting of the line is

timestamp JobName eventName condorID jobTag - sequenceNumber

The JobName is the name given to the node job as defined in the DAG input file with the keyword JOB. It identifies the node within the DAG.

The eventName is one of the many defined event or meta-events given in the lists below.

The condorID field is the job's ClusterId attribute, a period, and the ProcId attribute. There is no condorID assigned yet for some meta-events, such as PRE_SCRIPT_STARTED. For these, the dash character ('-') is printed.

The jobTag field is defined for the Pegasus workflow manager. Its usage is generalized to be useful to other workflow managers. Pegasus-managed jobs add a line of the following form to their HTCondor submit description file:

+pegasus_site = "local"
This defines the string local as the jobTag field.

Generalized usage adds a set of 2 commands to the HTCondor submit description file to define a string as the jobTag field:

+job_tag_name = "+job_tag_value"
+job_tag_value = "viz"
This defines the string viz as the jobTag field. Without any of these added lines within the HTCondor submit description file, the dash character ('-') is printed for the jobTag field.

The sequenceNumber is a monotonically-increasing number that starts at one. It is associated with each attempt at running a node. If a node is retried, it gets a new sequence number; a submit failure does not result in a new sequence number. When a Rescue DAG is run, the sequence numbers pick up from where they left off within the previous attempt at running the DAG. Note that this only applies if the Rescue DAG is run automatically or with the -dorescuefrom command-line option.

Here is an example of a very simple Pegasus jobstate.log file, assuming the example jobTag field of local:

1292620511 INTERNAL *** DAGMAN_STARTED 4972.0 ***
1292620523 NodeA PRE_SCRIPT_STARTED - local - 1
1292620523 NodeA PRE_SCRIPT_SUCCESS - local - 1
1292620525 NodeA SUBMIT 4973.0 local - 1
1292620525 NodeA EXECUTE 4973.0 local - 1
1292620526 NodeA JOB_TERMINATED 4973.0 local - 1
1292620526 NodeA JOB_SUCCESS 0 local - 1
1292620526 NodeA POST_SCRIPT_STARTED 4973.0 local - 1
1292620531 NodeA POST_SCRIPT_TERMINATED 4973.0 local - 1
1292620531 NodeA POST_SCRIPT_SUCCESS 4973.0 local - 1
1292620535 INTERNAL *** DAGMAN_FINISHED 0 ***

Events defining the eventName field

Meta-Events defining the eventName field

2.10.14 Status Information for the DAG in a ClassAd

The condor_dagman job places information about the status of the DAG into its own job ClassAd. The attributes are fully described at section 12. The attributes are

2.10.15 Utilizing the Power of DAGMan for Large Numbers of Jobs

Using DAGMan is recommended when submitting large numbers of jobs. The recommendation holds whether the jobs are represented by a DAG due to dependencies, or all the jobs are independent of each other, such as they might be in a parameter sweep. DAGMan offers:

Throttling limits the number of submitted jobs at any point in time.
Retry of jobs that fail
This is a useful tool when an intermittent error may cause a job to fail or may cause a job to fail to run to completion when attempted at one point in time, but not at another point in time. The conditions under which retry occurs are user-defined. In addition, the administrative support that facilitates the rerunning of only those jobs that fail is automatically generated.
Scripts associated with node jobs
PRE and POST scripts run on the submit host before and/or after the execution of specified node jobs.

Each of these capabilities is described in detail within this manual section about DAGMan. To make effective use of DAGMan, there is no way around reading the appropriate subsections.

To run DAGMan with large numbers of independent jobs, there are generally two ways of organizing and specifying the files that control the jobs. Both ways presume that programs or scripts will generate needed files, because the file contents are either large and repetitive, or because there are a large number of similar files to be generated representing the large numbers of jobs. The two file types needed are the DAG input file and the submit description file(s) for the HTCondor jobs represented. Each of the two ways is presented separately:

A unique submit description file for each of the many jobs.
A single DAG input file lists each of the jobs and specifies a distinct submit description file for each job. The DAG input file is simple to generate, as it chooses an identifier for each job and names the submit description file. For example, the simplest DAG input file for a set of 1000 independent jobs, as might be part of a parameter sweep, appears as
  # file sweep.dag
  JOB job0 job0.submit
  JOB job1 job1.submit
  JOB job2 job2.submit
  JOB job999 job999.submit
There are 1000 submit description files, with a unique one for each of the job<N> jobs. Assuming that all files associated with this set of jobs are in the same directory, and that files continue the same naming and numbering scheme, the submit description file for job6.submit might appear as
  # file job6.submit
  universe = vanilla
  executable = /path/to/executable
  log = job6.log
  input =
  output = job6.out
  arguments = "-file job6.out"

Submission of the entire set of jobs uses the command line

  condor_submit_dag sweep.dag

A benefit to having unique submit description files for each of the jobs is that they are available if one of the jobs needs to be submitted individually. A drawback to having unique submit description files for each of the jobs is that there are lots of submit description files.

Single submit description file.
A single HTCondor submit description file might be used for all the many jobs of the parameter sweep. To distinguish the jobs and their associated distinct input and output files, the DAG input file assigns a unique identifier with the VARS keyword.
  # file sweep.dag
  JOB job0 common.submit
  VARS job0 runnumber="0"
  JOB job1 common.submit
  VARS job1 runnumber="1"
  JOB job2 common.submit
  VARS job2 runnumber="2"
  JOB job999 common.submit
  VARS job999 runnumber="999"

The single submit description file for all these jobs utilizes the runnumber variable value in its identification of the job's files. This submit description file might appear as

  # file common.submit
  universe = vanilla
  executable = /path/to/executable
  log = wholeDAG.log
  input = job$(runnumber).in
  output = job$(runnumber).out
  arguments = "-$(runnumber)"
The job with runnumber="8" expects to find its input file in the single, common directory, and it sends its output to job8.out. The single log for all job events of the entire DAG is wholeDAG.log. Using one file for the entire DAG meets the limitation that no macro substitution may be specified for the job log file, and it is likely more efficient as well. This node's executable is invoked with
  /path/to/executable -8

These examples work well with respect to file naming and file location when there are less than several thousand jobs submitted as part of a DAG. The large numbers of files per directory becomes an issue when there are greater than several thousand jobs submitted as part of a DAG. In this case, consider a more hierarchical structure for the files instead of a single directory. Introduce a separate directory for each run. For example, if there were 10,000 jobs, there would be 10,000 directories, one for each of these jobs. The directories are presumed to be generated and populated by programs or scripts that, like the previous examples, utilize a run number. Each of these directories named utilizing the run number will be used for the input, output, and log files for one of the many jobs.

As an example, for this set of 10,000 jobs and directories, assume that there is a run number of 600. The directory will be named dir600, and it will hold the 3 files called in, out, and log, representing the input, output, and HTCondor job log files associated with run number 600.

The DAG input file sets a variable representing the run number, as in the previous example:

  # file biggersweep.dag
  JOB job0 bigger.submit
  VARS job0 runnumber="0"
  JOB job1 bigger.submit
  VARS job1 runnumber="1"
  JOB job2 bigger.submit
  VARS job2 runnumber="2"
  JOB job9999 bigger.submit
  VARS job9999 runnumber="9999"

A single HTCondor submit description file may be written. It resides in the same directory as the DAG input file.

  # file bigger.submit
  universe = vanilla
  executable = /path/to/executable
  log = log
  input = in
  output = out
  arguments = "-$(runnumber)"
  initialdir = dir$(runnumber)

One item to care about with this set up is the underlying file system for the pool. The transfer of files (or not) when using initialdir differs based upon the job universe and whether or not there is a shared file system. See section 11 for the details on the submit command initialdir.

Submission of this set of jobs is no different than the previous examples. With the current working directory the same as the one containing the submit description file, the DAG input file, and the subdirectories,

  condor_submit_dag biggersweep.dag

2.10.16 Workflow Metrics

condor_dagman may report workflow metrics to one or more HTTP servers. This capability is currently only used for workflows run under Pegasus. The reporting is disabled by setting the CONDOR_DEVELOPERS configuration variable to NONE, or by setting the PEGASUS_METRICS environment variable to any value other than True (case-insensitive) or 1. The dagman.out file will indicate whether or not metrics were reported.

For every DAG, a metrics file is created independent of the reporting of those metrics. This metrics file is named <dag_file_name>.metrics, where <dag_file_name> is the name of the DAG input file. In a workflow with nested DAGs, each nested DAG will create its own metrics file.

Here is an example metrics output file:


Here is an explanation of each of the items in the file:

The braindump.txt file is generated by pegasus-plan; the name of the braindump.txt file is specified with the PEGASUS_BRAINDUMP_FILE environment variable. If not specified, the file name defaults to braindump.txt, and it is placed in the current directory.

Note that the total_job_time value is always zero, because the calculation of that value has not yet been implemented.

If a DAG succeeds, but the metrics reporting fails, the DAG is still considered successful.

The metrics are reported only at the end of a DAG run. This includes reporting the metrics if the condor_dagman job is removed, or if the DAG drains from the queue because of being halted by a halt file.

The metrics are reported by the condor_dagman_metrics_reporter executable as described in the manual page at  [*].

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