This document how to use the Manifest Committer.
The Manifest committer is a committer for work which provides performance on ABFS for “real world” queries, and performance and correctness on GCS. It also works with other filesystems, including HDFS. However, the design is optimized for object stores where listing operatons are slow and expensive.
The architecture and implementation of the committer is covered in Manifest Committer Architecture.
The protocol and its correctness are covered in Manifest Committer Protocol.
It was added in March 2022, and should be considered unstable in early releases.
The only committer of work from Spark to Azure ADLS Gen 2 “abfs://” storage which is safe to use is the “v1 file committer”.
This is “correct” in that if a task attempt fails, its output is guaranteed not to be included in the final out. The “v2” commit algorithm cannot meet that guarantee, which is why it is no longer the default.
But: it is slow, especially on jobs where deep directory trees of output are used. Why is it slow? It’s hard to point at a particular cause, primarily because of the lack of any instrumentation in the FileOutputCommitter
. Stack traces of running jobs generally show rename()
, though list operations do surface too.
On Google GCS, neither the v1 nor v2 algorithm are safe because the google filesystem doesn’t have the atomic directory rename which the v1 algorithm requires.
A further issue is that both Azure and GCS storage may encounter scale issues with deleting directories with many descendants. This can trigger timeouts because the FileOutputCommitter assumes that cleaning up after the job is a fast call to delete("_temporary", true)
.
The Intermediate Manifest committer is a new committer for work which should deliver performance on ABFS for “real world” queries, and performance and correctness on GCS.
This committer uses the extension point which came in for the S3A committers. Users can declare a new committer factory for abfs:// and gcs:// URLs. A suitably configured spark deployment will pick up the new committer.
Directory performance issues in job cleanup can be addressed by two options 1. The committer will parallelize deletion of task attempt directories before deleting the _temporary
directory. 1. Cleanup can be disabled. .
The committer can be used with any filesystem client which has a “real” file rename() operation. It has been optimised for remote object stores where listing and file probes are expensive -the design is less likely to offer such signifcant speedup on HDFS -though the parallel renaming operations will speed up jobs there compared to the classic v1 algorithm.
The full details are covered in Manifest Committer Architecture.
The hooks put in to support the S3A committers were designed to allow every filesystem schema to provide their own committer. See Switching To an S3A Committer
A factory for the abfs schema would be defined in mapreduce.outputcommitter.factory.scheme.abfs
; and a similar one for gcs
.
Some matching spark configuration changes, especially for parquet binding, will be required. These can be done in core-site.xml
, if it is not defined in the mapred-default.xml
JAR.
<property> <name>mapreduce.outputcommitter.factory.scheme.abfs</name> <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value> </property> <property> <name>mapreduce.outputcommitter.factory.scheme.gs</name> <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory</value> </property>
In Apache Spark, the configuration can be done either with command line options (after the ‘–conf’) or by using the spark-defaults.conf
file. The following is an example of using spark-defaults.conf
also including the configuration for Parquet with a subclass of the parquet committer which uses the factory mechansim internally.
spark.hadoop.mapreduce.outputcommitter.factory.scheme.abfs org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory spark.hadoop.mapreduce.outputcommitter.factory.scheme.gs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol
committerinfo
command to probe committer bindings.The hadoop committer settings can be validated in a recent build of cloudstore and its committerinfo
command. This command instantiates a committer for that path through the same factory mechanism as MR and spark jobs use, then prints its toString
value.
hadoop jar cloudstore-1.0.jar committerinfo<EMAIL_ADDRESS>2021-09-16 19:42:59,731 [main] INFO commands.CommitterInfo (StoreDurationInfo.java:<init>(53)) - Starting: Create committer Committer factory for path<EMAIL_ADDRESS>is org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory@3315d2d7 (classname org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory) 2021-09-16 19:43:00,897 [main] INFO manifest.ManifestCommitter (ManifestCommitter.java:<init>(144)) - Created ManifestCommitter with JobID job__0000, Task Attempt attempt__0000_r_000000_1 and destination<EMAIL_ADDRESS>Created committer of class org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitter<EMAIL_ADDRESS>role='task committer'<EMAIL_ADDRESS>createJobMarker=true, jobUniqueId='job__0000', jobUniqueIdSource='JobID', jobAttemptNumber=0, jobAttemptId='job__0000_0', taskId='task__0000_r_000000', taskAttemptId='attempt__0000_r_000000_1'}, iostatistics=counters=(); gauges=(); minimums=(); maximums=(); means=(); }
The new committer will write a JSON summary of the operation, including statistics, in the _SUCCESS
file.
If this file exists and is zero bytes long: the classic FileOutputCommitter
was used.
If this file exists and is greater than zero bytes long, either the manifest committer was used, or in the case of S3A filesystems, one of the S3A committers. They all use the same JSON format.
Here are the main configuration options of the committer.
| Option | Meaning | Default Value |
|---|---|---|
mapreduce.manifest.committer.delete.target.files |
Delete target files? | false |
mapreduce.manifest.committer.io.threads |
Thread count for parallel operations | 64 |
mapreduce.manifest.committer.summary.report.directory |
directory to save reports. | "" |
mapreduce.manifest.committer.cleanup.parallel.delete |
Delete temporary directories in parallel | true |
mapreduce.fileoutputcommitter.cleanup.skipped |
Skip cleanup of _temporary directory |
false |
mapreduce.fileoutputcommitter.cleanup-failures.ignored |
Ignore errors during cleanup | false |
mapreduce.fileoutputcommitter.marksuccessfuljobs |
Create a _SUCCESS marker file on successful completion. (and delete any existing one in job setup) |
true |
There are some more, as covered in the (Advanced)[#advanced] section.
mapreduce.manifest.committer.io.threads
The core reason that this committer is faster than the classic FileOutputCommitter
is that it tries to parallelize as much file IO as it can during job commit, specifically:
These operations are all performed in the same thread pool, whose size is set in the option mapreduce.manifest.committer.io.threads
.
Larger values may be used.
Hadoop XML configuration
<property> <name>mapreduce.manifest.committer.io.threads</name> <value>32</value> </property>
In spark-defaults.conf
spark.hadoop.mapreduce.manifest.committer.io.threads 32
A larger value than that of the number of cores allocated to the MapReduce AM or Spark Driver does not directly overload the CPUs, as the threads are normally waiting for (slow) IO against the object store/filesystem to complete.
Manifest loading in job commit may be memory intensive; the larger the number of threads, the more manifests which will be loaded simultaneously.
Caveats * In Spark, multiple jobs may be committed in the same process, each of which will create their own thread pool during job commit or cleanup. * Azure rate throttling may be triggered if too many IO requests are made against the store. The rate throttling option mapreduce.manifest.committer.io.rate
can help avoid this.
mapreduce.manifest.committer.writer.queue.capacity
This is a secondary scale option. It controls the size of the queue for storing lists of files to rename from the manifests loaded from the target filesystem, manifests loaded from a pool of worker threads, and the single thread which saves the entries from each manifest to an intermediate file in the local filesystem.
Once the queue is full, all manifest loading threads will block.
<property> <name>mapreduce.manifest.committer.writer.queue.capacity</name> <value>32</value> </property>
As the local filesystem is usually much faster to write to than any cloud store, this queue size should not be a limit on manifest load performance.
It can help limit the amount of memory consumed during manifest load during job commit. The maximum number of loaded manifests will be:
mapreduce.manifest.committer.writer.queue.capacity + mapreduce.manifest.committer.io.threads
The classic FileOutputCommitter
deletes files at the destination paths before renaming the job’s files into place.
This is optional in the manifest committers, set in the option mapreduce.manifest.committer.delete.target.files
with a default value of false
.
This increases performance and is safe to use when all files created by a job have unique filenames.
Apache Spark does generate unique filenames for ORC and Parquet since SPARK-8406 Adding UUID to output file name to avoid accidental overwriting
Avoiding checks for/deleting target files saves one delete call per file being committed, so can save a significant amount of store IO.
When appending to existing tables, using formats other than ORC and parquet, unless confident that unique identifiers are added to each filename, enable deletion of the target files.
spark.hadoop.mapreduce.manifest.committer.delete.target.files true
Note 1: the committer will skip deletion operations when it created the directory into which a file is to be renamed. This makes it slightly more efficient, at least if jobs appending data are creating and writing into new partitions.
Note 2: the committer still requires tasks within a single job to create unique files. This is foundational for any job to generate correct data.
Spark has a feature called “Dynamic Partition Overwrites”,
This can be initiated in SQL
INSERT OVERWRITE TABLE ...
Or through DataSet writes where the mode is overwrite
and the partitioning matches that of the existing table
sparkConf.set("spark.sql.sources.partitionOverwriteMode", "dynamic") // followed by an overwrite of a Dataset into an existing partitioned table. eventData2 .write .mode("overwrite") .partitionBy("year", "month") .format("parquet") .save(existingDir)
This feature is implemented in Spark, which 1. Directs the job to write its new data to a temporary directory 1. After job commit completes, scans the output to identify the leaf directories “partitions” into which data was written. 1. Deletes the content of those directories in the destination table 1. Renames the new files into the partitions.
This is all done in spark, which takes over the tasks of scanning the intermediate output tree, deleting partitions and of renaming the new files.
This feature also adds the ability for a job to write data entirely outside the destination table, which is done by 1. writing new files into the working directory 1. spark moving them to the final destination in job commit
The manifest committer is compatible with dynamic partition overwrites on Azure and Google cloud storage as together they meet the core requirements of the extension: 1. The working directory returned in getWorkPath()
is in the same filesystem as the final output. 2. rename()
is an O(1)
operation which is safe and fast to use when committing a job.
None of the S3A committers support this. Condition (1) is not met by the staging committers, while (2) is not met by S3 itself.
To use the manifest committer with dynamic partition overwrites, the spark version must contain SPARK-40034 PathOutputCommitters to work with dynamic partition overwrite.
Be aware that the rename phase of the operation will be slow if many files are renamed -this is done sequentially. Parallel renaming would speed this up, but could trigger the abfs overload problems the manifest committer is designed to both minimize the risk of and support recovery from
The spark side of the commit operation will be listing/treewalking the temporary output directory (some overhead), followed by the file promotion, done with a classic filesystem rename()
call. There will be no explicit rate limiting here.
What does this mean?
It means that _dynamic partitioning should not be used on Azure Storage for SQL queries/Spark DataSet operations where many thousands of files are created. The fact that these will suffer from performance problems before throttling scale issues surface, should be considered a warning.
_SUCCESS
filesThe original hadoop committer creates a zero byte _SUCCESS
file in the root of the output directory unless disabled.
This committer writes a JSON summary which includes * The name of the committer. * Diagnostics information. * A list of some of the files created (for testing; a full list is excluded as it can get big). * IO Statistics.
If, after running a query, this _SUCCESS
file is zero bytes long, the new committer has not been used
If it is not empty, then it can be examined.
_SUCCESS
file files through the ManifestPrinter
tool.The summary files are JSON, and can be viewed in any text editor.
For a more succinct summary, including better display of statistics, use the ManifestPrinter
tool.
hadoop org.apache.hadoop.mapreduce.lib.output.committer.manifest.files.ManifestPrinter <path>
This works for the files saved at the base of an output directory, and any reports saved to a report directory.
mapreduce.manifest.committer.summary.report.directory
The committer can be configured to save the _SUCCESS
summary files to a report directory, irrespective of whether the job succeed or failed, by setting a filesystem path in the option mapreduce.manifest.committer.summary.report.directory
.
The path does not have to be on the same store/filesystem as the destination of work. For example, a local filesystem could be used.
XML
<property> <name>mapreduce.manifest.committer.summary.report.directory</name> <value>file:///tmp/reports</value> </property>
spark-defaults.conf
spark.hadoop.mapreduce.manifest.committer.summary.report.directory file:///tmp/reports
This allows for the statistics of jobs to be collected irrespective of their outcome, Whether or not saving the _SUCCESS
marker is enabled, and without problems caused by a chain of queries overwriting the markers.
Job cleanup is convoluted as it is designed to address a number of issues which may surface in cloud storage.
| Option | Meaning | Default Value |
|---|---|---|
mapreduce.fileoutputcommitter.cleanup.skipped |
Skip cleanup of _temporary directory |
false |
mapreduce.fileoutputcommitter.cleanup-failures.ignored |
Ignore errors during cleanup | false |
mapreduce.manifest.committer.cleanup.parallel.delete |
Delete task attempt directories in parallel | true |
The algorithm is:
if `mapreduce.fileoutputcommitter.cleanup.skipped`: return if `mapreduce.manifest.committer.cleanup.parallel.delete`: attempt parallel delete of task directories; catch any exception if not `mapreduce.fileoutputcommitter.cleanup.skipped`: delete(`_temporary`); catch any exception if caught-exception and not `mapreduce.fileoutputcommitter.cleanup-failures.ignored`: throw caught-exception
It’s a bit complicated, but the goal is to perform a fast/scalable delete and throw a meaningful exception if that didn’t work.
When working with ABFS and GCS, these settings should normally be left alone. If somehow errors surface during cleanup, enabling the option to ignore failures will ensure the job still completes. Disabling cleanup even avoids the overhead of cleanup, but requires a workflow or manual operation to clean up all _temporary
directories on a regular basis.
To switch to the manifest committer, the factory for committers for destinations with abfs://
URLs must be switched to the manifest committer factory, either for the application or the entire cluster.
<property> <name>mapreduce.outputcommitter.factory.scheme.abfs</name> <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value> </property>
This allows for ADLS Gen2 -specific performance and consistency logic to be used from within the committer. In particular: * the Etag header can be collected in listings and used in the job commit phase. * IO rename operations are rate limited * recovery is attempted when throttling triggers rename failures.
Warning This committer is not compatible with older Azure storage services (WASB or ADLS Gen 1).
The core set of Azure-optimized options becomes
<property> <name>mapreduce.outputcommitter.factory.scheme.abfs</name> <value>org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory</value> </property> <property> <name>spark.hadoop.fs.azure.io.rate.limit</name> <value>10000</value> </property>
And optional settings for debugging/performance analysis
<property> <name>mapreduce.manifest.committer.summary.report.directory</name> <value>abfs:// Path within same store/separate store</value> <description>Optional: path to where job summaries are saved</description> </property>
spark.hadoop.mapreduce.outputcommitter.factory.scheme.abfs org.apache.hadoop.fs.azurebfs.commit.AzureManifestCommitterFactory spark.hadoop.fs.azure.io.rate.limit 10000 spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol spark.hadoop.mapreduce.manifest.committer.summary.report.directory (optional: URI of a directory for job summaries)
fs.azure.io.rate.limit
To avoid triggering store throttling and backoff delays, as well as other throttling-related failure conditions file renames during job commit are throttled through a “rate limiter” which limits the number of rename operations per second a single instance of the ABFS FileSystem client may issue.
| Option | Meaning |
|---|---|
fs.azure.io.rate.limit |
Rate limit in operations/second for IO operations. |
Set the option to 0
remove all rate limiting.
The default value of this is set to 10000, which is the default IO capacity for an ADLS storage account.
<property> <name>fs.azure.io.rate.limit</name> <value>10000</value> <description>maximum number of renames attempted per second</description> </property>
This capacity is set at the level of the filesystem client, and so not shared across all processes within a single application, let alone other applications sharing the same storage account.
It will be shared with all jobs being committed by the same Spark driver, as these do share that filesystem connector.
If rate limiting is imposed, the statistic store_io_rate_limited
will report the time to acquire permits for committing files.
If server-side throttling took place, signs of this can be seen in * The store service’s logs and their throttling status codes (usually 503 or 500). * The job statistic commit_file_rename_recovered
. This statistic indicates that ADLS throttling manifested as failures in renames, failures which were recovered from in the comitter.
If these are seen -or other applications running at the same time experience throttling/throttling-triggered problems, consider reducing the value of fs.azure.io.rate.limit
, and/or requesting a higher IO capacity from Microsoft.
Important if you do get extra capacity from Microsoft and you want to use it to speed up job commits, increase the value of fs.azure.io.rate.limit
either across the cluster, or specifically for those jobs which you wish to allocate extra priority to.
This is still a work in progress; it may be expanded to support all IO operations performed by a single filesystem instance.
The manifest committer is compatible with and tested against Google cloud storage through the gcs-connector library from google, which provides a Hadoop filesystem client for the schema gs
.
Google cloud storage has the semantics needed for the commit protocol to work safely.
The Spark settings to switch to this committer are
spark.hadoop.mapreduce.outputcommitter.factory.scheme.gs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol spark.hadoop.mapreduce.manifest.committer.summary.report.directory (optional: URI of a directory for job summaries)
The store’s directory delete operations are O(files)
so the value of mapreduce.manifest.committer.cleanup.parallel.delete
SHOULD be left at the default of true
.
For mapreduce, declare the binding in core-site.xml
or mapred-site.xml
<property> <name>mapreduce.outputcommitter.factory.scheme.gcs</name> <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory</value> </property>
This committer does work with HDFS, it has just been targeted at object stores with reduced performance on some operations, especially listing and renaming, and semantics too reduced for the classic FileOutputCommitter
to rely on (specifically GCS).
To use on HDFS, set the ManifestCommitterFactory
as the committer factory for hdfs://
URLs.
Because HDFS does fast directory deletion, there is no need to parallelize deletion of task attempt directories during cleanup, so set mapreduce.manifest.committer.cleanup.parallel.delete
to false
The final spark bindings becomes
spark.hadoop.mapreduce.outputcommitter.factory.scheme.hdfs org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterFactory spark.hadoop.mapreduce.manifest.committer.cleanup.parallel.delete false spark.sql.parquet.output.committer.class org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter spark.sql.sources.commitProtocolClass org.apache.spark.internal.io.cloud.PathOutputCommitProtocol spark.hadoop.mapreduce.manifest.committer.summary.report.directory (optional: URI of a directory for job summaries)
There are some advanced options which are intended for development and testing, rather than production use.
| Option | Meaning | Default Value |
|---|---|---|
mapreduce.manifest.committer.store.operations.classname |
Classname for Manifest Store Operations | "" |
mapreduce.manifest.committer.validate.output |
Perform output validation? | false |
mapreduce.manifest.committer.writer.queue.capacity |
Queue capacity for writing intermediate file | 32 |
mapreduce.manifest.committer.validate.output
The option mapreduce.manifest.committer.validate.output
triggers a check of every renamed file to verify it has the expected length.
This adds the overhead of a HEAD
request per file, and so is recommended for testing only.
There is no verification of the actual contents.
mapreduce.manifest.committer.store.operations.classname
The manifest committer interacts with filesystems through implementations of the interface ManifestStoreOperations
. It is possible to provide custom implementations for store-specific features. There is one of these for ABFS; when the abfs-specific committer factory is used this is automatically set.
It can be explicitly set.
<property> <name>mapreduce.manifest.committer.store.operations.classname</name> <value>org.apache.hadoop.fs.azurebfs.commit.AbfsManifestStoreOperations</value> </property>
The default implementation may also be configured.
<property> <name>mapreduce.manifest.committer.store.operations.classname</name> <value>org.apache.hadoop.mapreduce.lib.output.committer.manifest.impl.ManifestStoreOperationsThroughFileSystem</value> </property>
There is no need to alter these values, except when writing new implementations for other stores, something which is only needed if the store provides extra integration support for the committer.
It may be possible to run multiple jobs targeting the same directory tree.
For this to work, a number of conditions must be met:
_temporary
directory must be disabled by setting mapreduce.fileoutputcommitter.cleanup.skipped
to true
._temporary
directory later!This has NOT BEEN TESTED


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Fusion3D
Version 6.04
|
Implements the central clock for viewer that can run at variable speeds. More...
#include <clock_input_class.h>
Public Member Functions | |
| clock_input_class () | |
| Constructor. More... | |
| ~clock_input_class () | |
| Destructor. More... | |
| int | read_tagged (const char *filename) |
| int | clear_all () |
| Clear all. More... | |
| int | is_clock_defined () |
| Is the clock defined (its parmameters defined). More... | |
| int | is_clock_running () |
| Is the clock running (updating time).  More... | |
| int | get_realtime_flag () |
| Get the flag indicating forensic or realtime clock operation. More... | |
| int | copy_time_sequence (char *name, int npt, float *times, float *vels) |
| Copy a sequence of times and velocities into the class. More... | |
| int | del_time_sequence (char *name) |
| Delete the time sequence from the class (does not change the clock time interval). More... | |
| bool | get_display_flag () |
| Get the current display flag that indicates whether current time is overlayed on map. More... | |
| int | set_mode (int mode_in) |
| Set the clock mode – 0 for play once, 1 for loop, 2 for swing. More... | |
| int | set_direction (int direction_flag_in) |
| Set the clock direction (forward or backward). More... | |
| int | set_time_delta (float time_delta) |
| Set the time delta in the model (as opposed to wall-clock time). More... | |
| int | increment_clock () |
| Move the clock time one tick (one increment) either forward or backward. More... | |
| int | start_clock () |
| Start the clock. More... | |
| int | stop_clock () |
| Stop the clock. More... | |
| int | mod_intervals (float tmin, float tmax, int type_flag, int del_flag, int diag_flag) |
| Add or delete a time interval. More... | |
| int | set_display_flag (bool flag) |
| Set the display flag that indicates whether current time is overlayed on map. More... | |
Implements the central clock for viewer that can run at variable speeds.
By default, the clock will run at a fixed rate determined by input tags. It will just encompass the time intervals (say for vehicle tracks of MTI data sets) that are registered to it.
The clock can run at a variable rate. The problem with a fixed clock rate is that there are often long intervals between periods of vehicle movement. If one uses a slow clock, then there are long periods with no activity. Conversely, if one sets it high to minimize the waiting times, then the vehicles move very fast. You would like not to completely eliminate these no-activity intervals since they are significant but to significantly reduce the dead time.
The variable clock rate is determined by the input tag "Clock-Compress". Parameters of this tag are a compression factor and a threshold value. Time will only be compressed when all vehicle speeds are below this threshold value. The compression factor varies from 1=no compression to larger values for higher compression. A reasonable value of compression is 2.
| clock_input_class::clock_input_class | ( | ) |
Constructor.
| clock_input_class::~clock_input_class | ( | ) |
Destructor.
| int clock_input_class::clear_all | ( | ) |
Clear all.
| int clock_input_class::copy_time_sequence | ( | char * | name, |
| int | npt, | ||
| float * | times, | ||
| float * | vels | ||
| ) |
Copy a sequence of times and velocities into the class.
The time/velocity sequences typically come from a vehicle track. The purpose is to run the clock at a variable rate – running faster when vehicles are moving slowly or stopped and running slower when there vehicles are moving at speed.
| int clock_input_class::del_time_sequence | ( | char * | name | ) |
Delete the time sequence from the class (does not change the clock time interval).
This method should be combined with the method mod_intervals.
| bool clock_input_class::get_display_flag | ( | ) |
Get the current display flag that indicates whether current time is overlayed on map.
| float clock_input_class::get_max_time | ( | ) |
Get the maximum time.
| float clock_input_class::get_min_time | ( | ) |
Get the minimum time.
| int clock_input_class::get_realtime_flag | ( | ) |
Get the flag indicating forensic or realtime clock operation.
| float clock_input_class::get_time | ( | ) |
Get the current time.
| float clock_input_class::get_time_delta | ( | ) |
Get the time delta in the model (as opposed to wall-clock time).
| int clock_input_class::increment_clock | ( | ) |
Move the clock time one tick (one increment) either forward or backward.
| int clock_input_class::is_clock_defined | ( | ) |
Is the clock defined (its parmameters defined).
| int clock_input_class::is_clock_running | ( | ) |
Is the clock running (updating time).
| int clock_input_class::mod_intervals | ( | float | tmin, |
| float | tmax, | ||
| int | type_flag, | ||
| int | del_flag, | ||
| int | diag_flag | ||
| ) |
Add or delete a time interval.
Time intervals are associated with vehicle tracks or a series of MTI hits. The min and max clock times are adjusted to just encompass all input time intervals.
| tmin | Minimum time for input interval |
| tmax | Maximim time for input interval |
| type_flag | 1=vehicle track, 2=truthed vehicle track, 3=MTI hits |
| del_flag | 0=create new interval defined by (tmin,tmax), 1=delete interval (first interval of input type) |
| diag_flag | 0=no diagnostic output, 1=diagnostic output. |
| int clock_input_class::read_tagged | ( | const char * | filename | ) |
| int clock_input_class::set_direction | ( | int | direction_flag_in | ) |
Set the clock direction (forward or backward).
| direction_flag_in | 1 for forward, -1 for backward |
| int clock_input_class::set_display_flag | ( | bool | flag | ) |
Set the display flag that indicates whether current time is overlayed on map.
| int clock_input_class::set_mode | ( | int | mode_in | ) |
Set the clock mode – 0 for play once, 1 for loop, 2 for swing.
| int clock_input_class::set_time_delta | ( | float | time_delta | ) |
Set the time delta in the model (as opposed to wall-clock time).
| int clock_input_class::start_clock | ( | ) |
Start the clock.
If the SoTimerSensor for the clock is not already scheduled, it is scheduled.
| int clock_input_class::stop_clock | ( | ) |
Stop the clock.
If the SoTimerSensor for the clock is scheduled, it is unscheduled.


current members
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PhD in Physical Chemistry, Massachusetts Institute of Technology Principal Investigator [minibio] Research interests: Cancer metastasis, cancer malignancy Cell surface receptor-intracellular organelle crosstalk Membrane lipid biophysics Single molecule biophysics, super-resolution microscopy, statistical analysis |
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PhD in Physics, Université Pierre et Marie Curie, Laboratoire de Physique et Étude des Matériaux - Paris, France Senior Research Associate MSc, Fundamental and Applied Physics | Université Paris-Sud, Laboratoire Aimé Cotton - Orsay, France. MSc, École Polytechnique, Laboratoire d'Optique Appliquée - Palaiseau, France | 2005 BSc, Physics | Universidad de Los Andes - Bogotá D.C., Colombia | 2004 Postdoctoral training: Postdoctoral Scholar | Optical Imaging Tools: Heart Dysfunction, University of California, Los Angeles | 2009-2012 |
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Undergraduate Student in Physics, George Washington University Undergraduate Student Physics | George Washington University, Washington, DC | 2018- |


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Forget about carving this easy slow-cooked pot roast – pull the fragrant, tender lamb from the bone, rustic style.
Preheat the oven to 200C/400F/Gas 6.
With the tip of a sharp knife, make shallow scores through the outer layer of skin and fat of the lamb shoulder, but not into the meat, cross-hatching the entire surface.
Mix the dried camomile, the thyme, rosemary, sage and oregano together in a bowl. Season well with salt and freshly ground black pepper.
Press a handful of the herb mixture into the skin of the lamb, massaging it in well all over the flesh of the lamb.
Sprinkle some of the herb mixture across the bottom of a casserole with a lid. Place the lamb into the casserole, then squeeze the lemon juice over the top, before sprinkling with any remaining herb mix.
Drizzle the honey over the top of the lamb and pour over a little olive oil.
Pour the water into the casserole, then put the lid on. Transfer to the oven and cook for 2½ hours. Check the dish after one hour - the lamb should be taking on a little colour. If the water has evaporated, add a little more to maintain the dish's moisture.
After the 2½ hours cooking the lamb meat should pull away easily from the bone.
Remove from the oven and allow to rest for 4-5 minutes.
To serve, using a knife and fork, pull large pieces of meat away from the bone and arrange on a large serving place. Drizzle the meat with any pan juices and allow guests to help themselves.


Ayrton Senna
"Being second is to be first of the ones who lose."
Ayrton Senna in his McLaren before a race (USA, Phoenix)
Ayrton Senna da Silva was a Brazilian racing driver who won the Formula One World Drivers' Championship in 1988, 1990 and 1991, and who is widely regarded as one of the greatest Formula One drivers of all time. He died in an accident leading the 1994 San Marino Grand Prix driving for Williams Racing.
Senna began his motorsport career in karting, moved up to open-wheel racing in 1981, and won the 1983 British Formula Three Championship. He made his Formula One debut with Toleman-Hart in 1984, before moving to Lotus-Renault the following year and winning six Grands Prix over the next three seasons. In 1988, he joined Frenchman Alain Prost at McLaren-Honda. Between them, they won all but one of the 16 Grands Prix that year, and Senna claimed his first World Championship. Prost claimed the championship in 1989, and Senna his second and third championships in 1990 and 1991. In 1992, the Williams-Renault combination began to dominate Formula One. Senna nonetheless managed to finish the 1993 season as runner-up, winning five races and negotiating a move to Williams in 1994.
Senna has often been voted as the best and most influential Formula One driver of all time in various motorsport polls. He was recognised for his qualifying speed over one lap, and from 1989 until 2006 he held the record for most pole positions. He was also acclaimed for his wet weather performances, such as the 1984 Monaco Grand Prix, the 1985 Portuguese Grand Prix, and the 1993 European Grand Prix. He holds a record six victories at the Monaco Grand Prix, and is the fifth-most successful driver of all time in terms of race wins. Senna courted controversy throughout his career, particularly during his turbulent rivalry with Prost. In the Japanese Grands Prix of 1989 and 1990, each of which decided the championship of that year, collisions between Senna and Prost determined the eventual winner.


Bitcoin-Qt version 0.8.1 released
18 March 2013
Bitcoin-Qt version 0.8.1 is now available from: http://sourceforge.net/projects/bitcoin/files/Bitcoin/bitcoin-0.8.1/
This is a maintenance release that adds a new network rule to avoid a chain-forking incompatibility with versions 0.7.2 and earlier.
Please report bugs using the issue tracker at github: https://github.com/bitcoin/bitcoin/issues
How to Upgrade
If you are running an older version, shut it down. Wait until it has completely shut down (which might take a few minutes for older versions), then run the installer (on Windows) or just copy over /Applications/Bitcoin-Qt (on Mac) or bitcoind/bitcoin-qt (on Linux).
If you are upgrading from version 0.7.2 or earlier, the first time you run 0.8.1 your blockchain files will be re-indexed, which will take anywhere from 30 minutes to several hours, depending on the speed of your machine.
0.8.1 Release notes
The 0.8.1 release has just two changes from the 0.8.0 release:
-
A new block-acceptance rule that will be enforced from 21 March 2013 until 15 May 2013 to prevent accepting blocks that fail to validate on pre-0.8 peers.
-
A new compiled-in checkpoint at block number 225,430 – the first block of the 11 March 2013 chain fork.
0.8.0 Release notes
Incompatible Changes
This release no longer maintains a full index of historical transaction ids by default, so looking up an arbitrary transaction using the getrawtransaction RPC call will not work. If you need that functionality, you must run once with -txindex=1 -reindex=1 to rebuild block-chain indices (see below for more details).
Improvements
Mac and Windows binaries are signed with certificates owned by the Bitcoin Foundation, to be compatible with the new security features in OSX 10.8 and Windows 8.
LevelDB, a fast, open-source, non-relational database from Google, is now used to store transaction and block indices. LevelDB works much better on machines with slow I/O and is faster in general. Berkeley DB is now only used for the wallet.dat file (public and private wallet keys and transactions relevant to you).
Pieter Wuille implemented many optimizations to the way transactions are verified, so a running, synchronized node uses less working memory and does much less I/O. He also implemented parallel signature checking, so if you have a multi-CPU machine all CPUs will be used to verify transactions.
New Features
“Bloom filter” support in the network protocol for sending only relevant transactions to lightweight clients.
contrib/verifysfbinaries is a shell-script to verify that the binary downloads at sourceforge have not been tampered with. If you are able, you can help make everybody’s downloads more secure by running this occasionally to check PGP signatures against download file checksums.
contrib/spendfrom is a python-language command-line utility that demonstrates how to use the “raw transactions” JSON-RPC api to send coins received from particular addresses (also known as “coin control”).
New/changed settings (command-line or bitcoin.conf file)
dbcache : controls LevelDB memory usage.
par : controls how many threads to use to validate transactions. Defaults to the number of CPUs on your machine, use -par=1 to limit to a single CPU.
txindex : maintains an extra index of old, spent transaction ids so they will be found by the getrawtransaction JSON-RPC method.
reindex : rebuild block and transaction indices from the downloaded block data.
New JSON-RPC API Features
lockunspent / listlockunspent allow locking transaction outputs for a period of time so they will not be spent by other processes that might be accessing the same wallet.
addnode / getaddednodeinfo methods, to connect to specific peers without restarting.
importprivkey now takes an optional boolean parameter (default true) to control whether or not to rescan the blockchain for transactions after importing a new private key.
Important Bug Fixes
Privacy leak: the position of the “change” output in most transactions was not being properly randomized, making network analysis of the transaction graph to identify users’ wallets easier.
Zero-confirmation transaction vulnerability: accepting zero-confirmation transactions (transactions that have not yet been included in a block) from somebody you do not trust is still not recommended, because there will always be ways for attackers to double-spend zero-confirmation transactions. However, this release includes a bug fix that makes it a little bit more difficult for attackers to double-spend a certain type (“lockTime in the future”) of zero-confirmation transaction.
Dependency Changes
Qt 4.8.3 (compiling against older versions of Qt 4 should continue to work)
Thanks to everybody who contributed to the 0.8.0 release:
- Alexander Kjeldaas
- Andrey Alekseenko
- Arnav Singh
- Christian von Roques
- Eric Lombrozo
- Forrest Voight
- Gavin Andresen
- Gregory Maxwell
- Jeff Garzik
- Luke Dashjr
- Matt Corallo
- Mike Cassano
- Mike Hearn
- Peter Todd
- Philip Kaufmann
- Pieter Wuille
- Richard Schwab
- Robert Backhaus
- Rune K. Svendsen
- Sergio Demian Lerner
- Wladimir J. van der Laan
- burger2
- default
- fanquake
- grimd34th
- justmoon
- redshark1802
- tucenaber
- xanatos


- Review:
- Reminders
- First Course Assignment
- More about the Grammar of Graphics
- Outline/Definitions
- Code Examples
- Additional Tools
- Next class
William R. Buchanan, Ph.D.
Strategic Data Fellow at MDE/Adjunct Prof at JSU
qplot()
and ggplot()
functions, and different levels of sophistication regarding the interpretationggplot2
, Hadley Wickham# Load the function that you'll need
source("https://github.com/ed514/JSU/blob/master/20140916/getRGraphicsExamples.R")
# If you just want to download the examples so you can look at them in the
# moment use
getRGraphicsExamples(out = 0)
# If you want to download/save the files instead use (out = 1) is the
# argument to save the files
getRGraphicsExamples(out = 1)
# The chapter 9 link was broken, otherwise there are chapters from 1 - 19 To
# print a chapter's examples to screen uncomment the line below and
# substitute the chapter number for the @ symbol. Also, the name of object
# that contains the examples is the name you see below.
RGraphExamples[["chapter@"]]
# To view a specific example you'll need to substitute the name of the file
# where you see nameOfExampleFile.R below
<EMAIL_ADDRESS># To find out the names of the files for each of the chapters in the book
# substitute the chapter number for the @ symbol
names(RGraphExamples[["chapter@"]])
# If you saved the files you can use this to see the file names
list.files()
# If you want to edit/view the files, it's best to split them in half
firstHalf <- list.files(pattern = "RGraphics-chapter[1-8]{1}.R", ignore.case = FALSE)
secondHalf <- list.files(pattern = "RGraphics-chapter[1-8]{2}.R", ignore.case = FALSE)
# Then you can use the following command to open/edit the files
file.edit(firstHalf)
file.edit(secondHalf)


The tanks just got filled. Even if we didn't make any money off them, who would it hurt to take a few balloons. (17 inch ones, not the small ones). That was the situation a few months ago, when it was warmer. It was no big deal selling gas, but now its cold and business has stopped. But not everything has stopped. The few balloons that I use to take has turned into fifteen balloons at a time. I stopped hanging out with a lot of friends to hang out with a more possessive one, the tank. The best part about it is we (me and by buddy) had four tanks between us. The tanks totaled Seventy-five pounds altogether. (that's a lot of balloons, to give you an idea, we figured about five, 17 inch balloons per pound).
It turned into hitting the tank all nights of the week. My fingers soon became tingly and very cold.  Not in the way most people would think, I would forget parts of physical function. I dunno its hard to explain, but I didn't randomly start forgetting everything I had ever learned. You would not think, looking at me, that I would ever be a nitrous zealot. I am in great physical condition, and my diet is right on. For gods sake, I work at GNC!
Oh sure it was fun, and it always will be, but I had learned the hard way. I don't know if I will return back to normal, hopefully I will, eventually. Just be aware, it does happen to people.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Storyboard ReplicationObjectScenarioForCoverage
Create some objects just for coverage. This does not serve as an usage example.


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Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Lamb's tongue and sweetbreads are very delicate and tender - delicious served here with roasted leek and gribiche.
For the sweetbread fritters, begin by heating a non-stick frying pan. Once hot, add the olive oil and the diced sweetbreads, season with salt and freshly ground black pepper and cook until crisp.
Add the shallots, garlic and parsley to the pan and fry for a minute.
Pour in the lamb gravy and take off the heat.
Pour the sweetbreads mixture into ice cube containers and freeze.
For the lamb’s tongue, bring a large pan of water to the boil, add all the ingredients then return to a simmer and cook for 2½ - 3 hours.
Once cooked, remove from the heat and let the lamb’s tongues cool in the stock. Once cooled, remove the tongues from the stock, peel them and cut into strips.
Mix the eggs and milk to make an egg wash. Place the flour, egg wash and breadcrumbs into three separate bowls.
Once the sweetbread fritters are frozen, turn them out of the moulds and cover them first with the flour, then egg wash, then breadcrumbs. Repeat this process three times on each fritter.
Set the fritters aside to defrost.
For the leeks, preheat the oven to 180C/350F/Gas 4.
Place the cleaned leeks onto aluminium foil. Add the thyme, bay leaves, garlic and a knob of butter. Season well with salt and freshly ground black pepper and drizzle with olive oil.
Wrap up the aluminium foil, to make a parcel, and cook in the oven for 30-35 minutes, or until the leeks are tender.
For the gribiche, mix all the ingredients together and set aside.
To finish the lamb’s tongues, heat a frying pan until hot, add a splash of oil and the lamb’s tongue strips and fry until crisp and golden-brown.
To finish the sweetbread fritters, heat a deep fat fryer to 190C/375F. Or alternatively heat vegetable oil in a deep sided, heavy-based pan until a breadcrumb sizzles and turns golden-brown when dropped into it. (CAUTION: hot oil can be dangerous. Do not leave unattended.)
Carefully drop the sweetbread fritters into the oil and cook for 2-3 minutes until golden-brown, crisp, and hot through.
Remove the fritters from the hot oil with a slotted spoon, and drain onto kitchen paper. Season with a little salt and freshly ground black pepper.
To serve, cut the leek down the centre and place one half on each serving plate. Spoon some gribiche on top of the leek. Place the fritter and lamb’s tongue on top of the gribiche. Garnish with sliced baby carrots, broad beans, parsley leaf, radishes and shallot rings.


The Four Million
by O. Henry
"A Service of Love"
Additional Information
- Year Published: 1906
- Language: English
- Country of Origin: United States of America
- Source: O. Henry, The Four Million (Doubleday, Page & Company; New York: 1906) Henry, O. (1906). The Four Million. Doubleday, Page & Company; New York.
-
Readability:
- Flesch–Kincaid Level: 5.2
- Word Count: 2,337
- Genre: Realism
- Keywords: fate, fiction, short story
- ✎ Cite This
Downloads
When one loves one's Art no service seems too hard.
That is our premise. This story shall draw a conclusion from it, and show at the same time that the premise is incorrect. That will be a new thing in logic, and a feat in story–telling somewhat older than the great wall of China.
Joe Larrabee came out of the post–oak flats of the Middle West pulsing with a genius for pictorial art. At six he drew a picture of the town pump with a prominent citizen passing it hastily. This effort was framed and hung in the drug store window by the side of the ear of corn with an uneven number of rows. At twenty he left for New York with a flowing necktie and a capital tied up somewhat closer.
Delia Caruthers did things in six octaves so promisingly in a pine–tree village in the South that her relatives chipped in enough in her chip hat for her to go "North" and "finish." They could not see her f—, but that is our story.
Joe and Delia met in an atelier where a number of art and music students had gathered to discuss chiaroscuro, Wagner, music, Rembrandt's works, pictures, Waldteufel, wall paper, Chopin and Oolong.
Joe and Delia became enamoured one of the other, or each of the other, as you please, and in a short time were married—for (see above), when one loves one's Art no service seems too hard.
Mr. and Mrs. Larrabee began housekeeping in a flat. It was a lonesome flat—something like the A sharp way down at the left–hand end of the keyboard. And they were happy; for they had their Art, and they had each other. And my advice to the rich young man would be—sell all thou hast, and give it to the poor—janitor for the privilege of living in a flat with your Art and your Delia.
Flat–dwellers shall indorse my dictum that theirs is the only true happiness. If a home is happy it cannot fit too close—let the dresser collapse and become a billiard table; let the mantel turn to a rowing machine, the escritoire to a spare bedchamber, the washstand to an upright piano; let the four walls come together, if they will, so you and your Delia are between. But if home be the other kind, let it be wide and long—enter you at the Golden Gate, hang your hat on Hatteras, your cape on Cape Horn and go out by the Labrador.
Joe was painting in the class of the great Magister—you know his fame. His fees are high; his lessons are light—his high–lights have brought him renown. Delia was studying under Rosenstock—you know his repute as a disturber of the piano keys.
They were mighty happy as long as their money lasted. So is every—but I will not be cynical. Their aims were very clear and defined. Joe was to become capable very soon of turning out pictures that old gentlemen with thin side–whiskers and thick pocketbooks would sandbag one another in his studio for the privilege of buying. Delia was to become familiar and then contemptuous with Music, so that when she saw the orchestra seats and boxes unsold she could have sore throat and lobster in a private dining–room and refuse to go on the stage.
But the best, in my opinion, was the home life in the little flat—the ardent, voluble chats after the day's study; the cozy dinners and fresh, light breakfasts; the interchange of ambitions—ambitions interwoven each with the other's or else inconsiderable—the mutual help and inspiration; and—overlook my artlessness—stuffed olives and cheese sandwiches at 11 p.m.
But after a while Art flagged. It sometimes does, even if some switchman doesn't flag it. Everything going out and nothing coming in, as the vulgarians say. Money was lacking to pay Mr. Magister and Herr Rosenstock their prices. When one loves one's Art no service seems too hard. So, Delia said she must give music lessons to keep the chafing dish bubbling.
For two or three days she went out canvassing for pupils. One evening she came home elated.
"Joe, dear," she said, gleefully, "I've a pupil.  General—General A. B. Pinkney's daughter—on Seventy–first street.  Byzantine I think you would call it. And inside! Oh, Joe, I never saw anything like it before.
"My pupil is his daughter Clementina. I dearly love her already. She's a delicate thing—dresses always in white; and the sweetest, simplest manners! Only eighteen years old. I'm to give three lessons a week; and, just think, Joe! $5 a lesson. I don't mind it a bit; for when I get two or three more pupils I can resume my lessons with Herr Rosenstock. Now, smooth out that wrinkle between your brows, dear, and let's have a nice supper."
"That's all right for you, Dele," said Joe, attacking a can of peas with a carving knife and a hatchet, "but how about me? Do you think I'm going to let you hustle for wages while I philander in the regions of high art? Not by the bones of Benvenuto Cellini! I guess I can sell papers or lay cobblestones, and bring in a dollar or two."
Delia came and hung about his neck.
"Joe, dear, you are silly. You must keep on at your studies. It is not as if I had quit my music and gone to work at something else. While I teach I learn. I am always with my music. And we can live as happily as millionaires on $15 a week. You mustn't think of leaving Mr. Magister."
"All right," said Joe, reaching for the blue scalloped vegetable dish.  It isn't Art.
"When one loves one's Art no service seems too hard," said Delia.
"Magister praised the sky in that sketch I made in the park," said Joe. "And Tinkle gave me permission to hang two of them in his window.
"I'm sure you will," said Delia, sweetly. "And now let's be thankful for Gen. Pinkney and this veal roast."
During all of the next week the Larrabees had an early breakfast. Joe was enthusiastic about some morning–effect sketches he was doing in Central Park, and Delia packed him off breakfasted, coddled, praised and kissed at 7 o'clock. Art is an engaging mistress. It was most times 7 o'clock when he returned in the evening.
At the end of the week Delia, sweetly proud but languid, triumphantly tossed three five–dollar bills on the 8×10 (inches) centre table of the 8×10 (feet) flat parlour.
"Sometimes," she said, a little wearily, "Clementina tries me. I'm afraid she doesn't practise enough, and I have to tell her the same things so often. And then she always dresses entirely in white, and that does get monotonous. But Gen. Pinkney is the dearest old man! I wish you could know him, Joe. He comes in sometimes when I am with Clementina at the piano—he is a widower, you know—and stands there pulling his white goatee. 'And how are the semiquavers and the demisemiquavers progressing?' he always asks.
"I wish you could see the wainscoting in that drawing–room, Joe! And those Astrakhan rug portières.  I hope she is stronger than she looks. Oh, I really am getting attached to her, she is so gentle and high bred. Gen. Pinkney's brother was once Minister to Bolivia."
And then Joe, with the air of a Monte Cristo, drew forth a ten, a five, a two and a one—all legal tender notes—and laid them beside Delia's earnings.
"Sold that watercolour of the obelisk to a man from Peoria," he announced overwhelmingly.
"Don't joke with me," said Delia, "not from Peoria!"
"All the way. I wish you could see him, Dele. Fat man with a woollen muffler and a quill toothpick. He saw the sketch in Tinkle's window and thought it was a windmill at first. He was game, though, and bought it anyhow. He ordered another—an oil sketch of the Lackawanna freight depot—to take back with him. Music lessons! Oh, I guess Art is still in it."
"I'm so glad you've kept on," said Delia, heartily. "You're bound to win, dear. Thirty–three dollars! We never had so much to spend before. We'll have oysters to–night."
"And filet mignon with champignons," said Joe. "Where is the olive fork?"
On the next Saturday evening Joe reached home first. He spread his $18 on the parlour table and washed what seemed to be a great deal of dark paint from his hands.
Half an hour later Delia arrived, her right hand tied up in a shapeless bundle of wraps and bandages.
"How is this?" asked Joe after the usual greetings. Delia laughed, but not very joyously.
"Clementina," she explained, "insisted upon a Welsh rabbit after her lesson.  Welsh rabbits at 5 in the afternoon. The General was there. You should have seen him run for the chafing dish, Joe, just as if there wasn't a servant in the house. I know Clementina isn't in good health; she is so nervous. In serving the rabbit she spilled a great lot of it, boiling hot, over my hand and wrist. It hurt awfully, Joe. And the dear girl was so sorry! But Gen. Pinkney!—Joe, that old man nearly went distracted. He rushed downstairs and sent somebody—they said the furnace man or somebody in the basement—out to a drug store for some oil and things to bind it up with.
"What's this?" asked Joe, taking the hand tenderly and pulling at some white strands beneath the bandages.
"It's something soft," said Delia, "that had oil on it. Oh, Joe, did you sell another sketch?" She had seen the money on the table.
"Did I?" said Joe; "just ask the man from Peoria. He got his depot to–day, and he isn't sure but he thinks he wants another parkscape and a view on the Hudson. What time this afternoon did you burn your hand, Dele?"
"Five o'clock, I think," said Dele, plaintively. "The iron—I mean the rabbit came off the fire about that time. You ought to have seen Gen. Pinkney, Joe, when—"
"Sit down here a moment, Dele," said Joe. He drew her to the couch, sat beside her and put his arm across her shoulders.
"What have you been doing for the last two weeks, Dele?" he asked.
She braved it for a moment or two with an eye full of love and stubbornness, and murmured a phrase or two vaguely of Gen. Pinkney; but at length down went her head and out came the truth and tears.
"I couldn't get any pupils," she confessed. "And I couldn't bear to have you give up your lessons; and I got a place ironing shirts in that big Twenty–fourth street laundry. And I think I did very well to make up both General Pinkney and Clementina, don't you, Joe? And when a girl in the laundry set down a hot iron on my hand this afternoon I was all the way home making up that story about the Welsh rabbit. You're not angry, are you, Joe? And if I hadn't got the work you mightn't have sold your sketches to that man from Peoria."
"He wasn't from Peoria," said Joe, slowly.
"Well, it doesn't matter where he was from. How clever you are, Joe—and—kiss me, Joe—and what made you ever suspect that I wasn't giving music lessons to Clementina?"
"I didn't," said Joe, "until to–night. And I wouldn't have then, only I sent up this cotton waste and oil from the engine–room this afternoon for a girl upstairs who had her hand burned with a smoothing–iron. I've been firing the engine in that laundry for the last two weeks."
"And then you didn't—"
"My purchaser from Peoria," said Joe, "and Gen. Pinkney are both creations of the same art—but you wouldn't call it either painting or music."
And then they both laughed, and Joe began:
"When one loves one's Art no service seems—"
But Delia stopped him with her hand on his lips. "No," she said—"just 'When one loves.'"


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THE RUN OF THE COUNTRY A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 7.6
Ireland, 1995 U.S. Availability: 9/95 (limited) Running Length: 1:49 MPAA Classification: R (Sex, nudity, violence, profanity) Theatrical Aspect Ratio: 2.35:1
Cast: Albert Finney, Matthew Keeslar, Victoria Smurfit, Anthony Brophy Director: Peter Yates Producers: Peter Yates and Ruth Boswell Screenplay: Shane Connaughton Cinematography: Mike Southon Music: Cynthia Millar U.S. Distributor: Columbia Pictures
Albert Finney is one of today's best character actors, and lately he's been spending a lot of time in the skin of a lonely, aging Irishman. In addition to his role as the father in THE RUN OF THE COUNTRY, he has appeared as a similar character in THE PLAYBOYS and A MAN OF NO IMPORTANCE. Similar, yet not the same. Accomplished thespian that he is, Finney does something subtly different with each of the parts, and though many of the particulars are the same, no one would confuse any of the three men.
Yet Finney's character is not the focus of THE RUN OF THE COUNTRY. His is a supporting role, presenting an opportunity for conflict with his teenage son, Danny (Matthew Keeslar). This is Danny's story--how, coping with the recent death of his beloved mother, he struggles to find independence from an overbearing father. Part of this process is moving out of his childhood home and in with his friend, Prunty (Anthony Brophy), a cattle herder with a love of booze and a penchant for crude talk. Another part is falling in love. The object of his affection is Annagh (Victoria Smurfit), a girl from just north of the border separating Catholic South Ireland from the Protestant North.
Though it takes place in contemporary Ireland, the way in which people live--many in ramshackle huts with little evidence of modern "conveniences"--gives the story a timeless quality. Likewise, the characters would probably be equally at home in the nineteenth century as in the twentieth. Danny and Annagh are both virgins well into their teens and Danny's father is opposed to any sort of contraceptive.
The best-crafted element of the film is the love story. Director Peter Yates brings real feeling and tenderness to the interactions between Danny and Annagh, from their first furtive glances at each other to Danny's faltering attempts to address her in conversation. Annagh is somewhat more world-wise than Danny, which makes it amusing to watch her nonplussed reactions to some of his unintentional double entendres--phrases he innocently thinks have only one meaning.
The relationship between Danny and his father, although of equal importance to the romance, is less developed. Drawn with broad strokes, this conflict always seems more peripheral than central, which robs a cathartic scene of emotional power. Finney does all he can with the material, creating a mild character always on the brink of an emotional eruption, but Matthew Keeslar can't keep up with him. In fact, Keeslar, who is at times flat, isn't the equal of any of his co-stars. Both Victoria Smurfit and Anthony Brophy have impressive debuts.
The script, adapted by Shane Connaughton (the writer of MY LEFT FOOT) from his own book, has a fair number of affecting sequences, and manages to deftly mix humor and serious issues. While the political situation and the possible involvement of the IRA are kept in the background, they are not ignored. Part of growing up in Ireland, after all, is coming to grips with this volatile element of life. At one point, Prunty advises: "Have a dream ya can live for, lad, not one that'll put you in the grave." More crucial to the story, however, are the boundaries created by religion, education, and class. Every conflict in this film has its roots in one of these three causes. So, although THE RUN OF THE COUNTRY has its weak spots, it nevertheless gives us a more original coming-of-age story than that which typically graces American screens.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
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| 023:027 Khan | : |
So We inspired him (saying): "Construct the ship under Our Eyes and under Our Revelation (guidance). Then, when Our Command comes, and the oven gushes forth water, take on board of each kind two (male and female), and your family, except those thereof against whom the Word has already gone forth. And address Me not in favour of those who have done wrong. Verily, they are to be drowned. |
| 023:027 Maulana | : |
So We revealed to him: Make the ark under Our eyes and according to Our revelation; then when our command comes, and water gushes forth from the valley, take into it of every kind a pair, two, and thy people, except those among them again whom the word has gone forth, and speak not to Me in respect of those who are unjust; surely they will be drowned. |
| 023:027 Pickthal | : |
Then We inspired in him, saying: Make the ship under Our eyes and Our inspiration. Then, when Our command cometh and the oven gusheth water, introduce therein of every (kind) two spouses, and thy household save him thereof against whom the Word hath already gone forth. And plead not with Me on behalf of those who have done wrong. Lo! they will be drowned. |
| 023:027 Rashad | : |
We then inspired him: "Make the watercraft under our watchful eyes, and in accordance with our inspiration. When our command comes, and the atmosphere boils up, put on it a pair of every kind (of your domesticated animals), and your family, except those condemned to be doomed. Do not speak to Me on behalf of those who transgressed; they will be drowned. |
| 023:027 Sarwar | : |
We inspired him, saying, "Build the Ark before Our eyes and by the instruction of Our revelation. When our decree comes to pass and water comes forth from the Oven, embark in the Ark with a pair of every kind of animals and your family except those already doomed (to perish). Do not plead with Me for the unjust; they will be drowned." |
| 023:027 Shakir | : |
So We revealed to him, saying: Make the ark before Our eyes and (according to) Our revelation; and when Our command is given and the valley overflows, take into it of every kind a pair, two, and your followers, except those among them against whom the word has gone forth, and do not speak to Me in respect of those who are unjust; surely they shall be drowned. |
| 023:027 Sherali | : |
So WE directed him by revelation: 'Make the Ark under Our eyes and according to Our revelation. And when Our command comes, and the fountains of the earth gush forth, take thou into it two pairs of every species and thy family, except those of them against whom the word has already gone forth. And address ME not concerning those who have done wrong; they shall, surely, be drowned. |
| 023:027 Yusufali | : |
So We inspired him (with this message): "Construct the Ark within Our sight and under Our guidance: then when comes Our Command, and the fountains of the earth gush forth, take thou on board pairs of every species, male and female, and thy family- except those of them against whom the Word has already gone forth: And address Me not in favour of the wrong-doers; for they shall be drowned (in the Flood). |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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getLevel(): number Gets the level of logging of ALL the application loggers. Returns The logLevel value used in all loggers.