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+<body class="article">
+<div id="header">
+<h1>Converting Recoll indexing to multithreading</h1>
+<span id="author">Jean-Fran��ois Dock��s</span><br />
+<span id="email"><tt><<a href="mailto:jfd@recoll.org">jfd@recoll.org</a>></tt></span><br />
+<span id="revdate">2012-12-03</span>
+</div>
+<div id="content">
+<div id="preamble">
+<div class="sectionbody">
+<div class="paragraph"><p><a href="http://www.recoll.org"><strong>Recoll</strong></a> is a document indexing application, it
+allows you to find documents by specifying search terms.</p></div>
+<div class="paragraph"><p>The documents need to be <em>indexed</em> for searches to be fast. In a nutshell,
+we convert the different document formats to text, then split the text into
+terms and remember where those occur. This is a time-consuming operation.</p></div>
+<div class="paragraph"><p>Up to version 1.18 <strong>Recoll</strong> indexing is single-threaded: routines which
+call each other sequentially.</p></div>
+<div class="paragraph"><p>In most personal indexer contexts, it is also CPU-bound. There is a lot of
+conversion work necessary for turning those PDF (or other) files into
+appropriately cleaned up pure text, then split it into terms and update the
+index. Given the relatively modest amount of data, and the speed of
+storage, I/O issues are secondary.</p></div>
+<div class="paragraph"><p>Looking at the <em>CPU idle</em> <strong>top</strong> output stuck at 75% on my quad-core CPU,
+while waiting for the indexing to finish, was frustrating, and I was
+tempted to find a way to keep those other cores at temperature and shorten
+the waiting.</p></div>
+<div class="paragraph"><p>For some usages, the best way to accomplish this may be to just partition
+the index and independantly start indexing on different configurations,
+using multiple processes to better utilize the available processing power.</p></div>
+<div class="paragraph"><p>This is not an universal solution though, as it is complicated to set up,
+not optimal in general for indexing performance, and not always optimal
+either at query time.</p></div>
+<div class="paragraph"><p>The most natural way to improve indexing times is to increase CPU
+utilization by using multiple threads inside an indexing process.</p></div>
+<div class="paragraph"><p>Something similar had been done with earlier versions of the <strong>Recoll</strong> GUI,
+which had an internal indexing thread. This had been a frequent source of
+trouble though, and linking the GUI and indexing process lifetimes was a
+bad idea, so, in recent versions, the indexing is always performed by an
+external process. Still, this experience had put in light most of the
+problem areas, and prepared the code for further work.</p></div>
+<div class="paragraph"><p>It should be noted that, as <tt>recollindex</tt> is both <em>nice</em>'d and <em>ionice</em>'d
+as a lowest priority process, it will only use free computing power on the
+machine, and will step down as soon as anything else wants to work.</p></div>
+<div class="sidebarblock">
+<div class="content">
+<div class="paragraph"><p>The only case where you may notice that the indexing is at work
+is when the machine is short on memory and things (such as
+your Web browser) get swapped-out while you are not actively using
+them. You then notice a long delay when you want to start, because they
+need to be swapped back in. There is little which can be done about
+this. Setting <em>idxflushmb</em> to a low value may help in some cases (depending
+on the document sizes). May I also suggest in this case that, if your
+machine can take more memory, it may be a good idea to procure some, as
+memory is nowadays quite cheap, and memory-starved machines are not fun.</p></div>
+</div></div>
+<div class="paragraph"><p>In general, augmenting the machine utilisation by <tt>recollindex</tt> just does
+not change its responsiveness. My PC has a an Intel Pentium Core i5 750 (4
+cores, no hyperthreading), which is far from being a high performance CPU
+(nowadays…), and I often forget that I am running indexing tests, it is
+just not noticeable. The machine does have a lot of memory though (12GB).</p></div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_the_recoll_indexing_processing_flow">The Recoll indexing processing flow</h2>
+<div class="sectionbody">
+<div class="imageblock" style="float:right;">
+<div class="content">
+<img src="nothreads.png" alt="Basic flow" />
+</div>
+</div>
+<div class="paragraph"><p>There are 4 main steps in the <tt>recollindex</tt> processing pipeline:</p></div>
+<div class="olist arabic"><ol class="arabic">
+<li>
+<p>
+Find the file
+</p>
+</li>
+<li>
+<p>
+Convert it to text
+</p>
+</li>
+<li>
+<p>
+Process the text (split, strip etc.) and create a <strong>Xapian</strong> document
+</p>
+</li>
+<li>
+<p>
+Update the index
+</p>
+</li>
+</ol></div>
+<div class="paragraph"><p>The first step, walking the file system (or some other data source), is
+usually much faster than the others, and we just leave it alone to be
+performed by the main thread. It outputs file names (and the associated
+<strong>POSIX</strong> <em>stat</em> data).</p></div>
+<div class="paragraph"><p>The last step, <strong>Xapian</strong> index updating, can only be single-threaded.</p></div>
+<div class="paragraph"><p>The first idea is to change the indexing pipeline so that each step is
+performed by an independant worker thread, passing its output to the next
+thread, in assembly-line fashion.</p></div>
+<div class="paragraph"><p>In order to achieve this, we need to decouple the different phases. They
+are normally linked by procedure calls, which we replace with a job
+control object: the <em>WorkQueue</em>.</p></div>
+<div class="sect2">
+<h3 id="_the_workqueue">The WorkQueue</h3>
+<div class="paragraph"><p>The <em>WorkQueue</em> object is implemented by a reasonably simple class, which
+manages an input queue on which client append jobs, and a set of worker
+threads, which retrieve and perform the jobs, and whose lifetime are
+managed by the <em>WorkQueue</em> object. The
+<a href="https://bitbucket.org/medoc/recoll/src/f06f3aba912045d6ad52e9a0fd930b95e363fd10/src/utils/workqueue.h?at=default">implementation</a> is straightforward with
+<strong>POSIX</strong> threads synchronization functions and C++ <strong>STL</strong> data structures.</p></div>
+<div class="paragraph"><p>In practise it proved quite simple to modify existing code to create a job
+object and put it on the queue, instead of calling the downstream routine
+with the job parameters, <em>while keeping the capacity to call the downstream
+routine directly</em>. The kind of coupling is determined either by compilation
+flags (for global disabling/enabling of multithreading), or according to
+configuration data, which allows experimenting with different threads
+arrangements just by changing parameters in a file, without recompiling.</p></div>
+<div class="paragraph"><p>Each <em>WorkQueue</em> accepts two parameters: the length of the input queue
+(before a client will block when trying to add a job), and the number of
+worker threads. Both parameters can be set in the <strong>Recoll</strong> configuration
+file for each of the three queues used in the indexing pipeline. Setting
+the queue length to -1 will disable the corresponding queue (using a direct
+call instead).</p></div>
+<div style="clear:both;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_the_assembly_line">The Assembly Line</h2>
+<div class="sectionbody">
+<div class="imageblock" style="float:right;">
+<div class="content">
+<img src="assembly.png" alt="Assembly line" />
+</div>
+</div>
+<div class="paragraph"><p>So the first idea is to create 3 explicit threads to manage the file
+conversion, the term generation, and the <strong>Xapian</strong> index update. The first
+thread prepares a file, passes it on to the term generation thread, and
+immediately goes back to work on the next file, etc.</p></div>
+<div class="paragraph"><p>The presumed advantage of this method is that the different stages, which
+perform disjointed processing, should share little, so that we can hope to
+minimize the changes necessitated by the threads interactions.</p></div>
+<div class="paragraph"><p>However some changes to the code were needed to make this work (and a few
+bugs were missed, which only became apparent at later stages, confirming
+that the <em>low interaction</em> idea was not completely false).</p></div>
+<div class="sect2">
+<h3 id="_converting_to_multithreading_what_to_look_for">Converting to multithreading: what to look for</h3>
+<div class="paragraph"><p>I am probably stating the obvious here, but when preparing a program for
+multi-threading, problems can only arise where non-constant data is
+accessed by different threads.</p></div>
+<div class="paragraph"><p>Once you have solved the core problems posed by the obvious data that needs
+to be shared, you will be left to deal with less obvious, hidden,
+interactions inside the program.</p></div>
+<div class="paragraph"><p>Classically this would concern global or static data, but in a C++ program,
+class members will be a concern if a single object can be accessed by
+several threads.</p></div>
+<div class="paragraph"><p>Hunting for static data inside a program of non trivial size is not always
+obvious. Two approaches can be used: hunting for the <em>static</em> keyword in
+source code, or looking at global and static data symbols in <strong>nm</strong> output.</p></div>
+<div class="paragraph"><p>Once found, there are mostly three types of static/global data:</p></div>
+<div class="ulist"><ul>
+<li>
+<p>
+Things that need to be eliminated: for example, routines can be made
+ reentrant by letting the caller supply a storage buffer instead of using
+ an internal static one (which was a bad idea in the first place
+ anyway).
+</p>
+</li>
+<li>
+<p>
+Things that need to be protected: sometimes, the best approach is just
+ to protect the access with a mutex lock. It is trivial to encapsulate
+ the locks in C++ objects to use the "Resource Acquisition is
+ Initialization" idiom, easily making sure that locks are freed when
+ exiting the critical section. A very basic
+ <a href="https://bitbucket.org/medoc/recoll/src/f06f3aba9120/src/utils/ptmutex.h?at=default">example of implementation</a>
+ can be found in the <strong>Recoll</strong> source code.
+</p>
+</li>
+<li>
+<p>
+Things which can stay: this is mostly initialization data such as value
+ tables which are computed once, and then stay logically constant during
+ program execution. In order to be sure of a correct single-threaded
+ initialization, it is best to explicitly initialize the modules or
+ functions that use this kind of data in the main thread when the program
+ starts.
+</p>
+</li>
+</ul></div>
+</div>
+<div class="sect2">
+<h3 id="_assembly_line_approach_the_results">Assembly line approach: the results</h3>
+<div class="paragraph"><p>Unfortunately, the assembly line approach yields very modest improvements
+when used inside <strong>Recoll</strong> indexing. The reason, is that this method needs
+stages of equivalent complexity to be efficient. If one of the stages
+dominates the others, its thread will be the only one active at any time,
+and little will be gained.</p></div>
+<div class="paragraph"><p>What is especially problematic is that the balance between tasks need not
+only exist on average, but also for the majority of individual jobs.</p></div>
+<div class="paragraph"><p>For <strong>Recoll</strong> indexing, even if the data preparation and index update steps
+are often of the same order of magnitude <em>on average</em>, their balance
+depends a lot on the kind of data being processed, so that things are
+usually unbalanced at any given time: the index update thread is mostly
+idle while processing PDF files, and the data preparation has little to do
+when working on HTML or plain text.</p></div>
+<div class="paragraph"><p>In practice, very modest indexing time improvements from 5% to 15% were
+achieved with this method.</p></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_the_next_step_multi_stage_parallelism">The next step: multi-stage parallelism</h2>
+<div class="sectionbody">
+<div class="imageblock" style="float:right;">
+<div class="content">
+<img src="multipara.png" alt="Multi-stage parallelism" />
+</div>
+</div>
+<div class="paragraph"><p>Given the limitations of the assembly line approach, the next step in the
+transformation of <strong>Recoll</strong> indexing was to enable full parallelism wherever
+possible.</p></div>
+<div class="paragraph"><p>Of the four processing steps (see figures), two are not candidates for
+parallelization:</p></div>
+<div class="ulist"><ul>
+<li>
+<p>
+File system walking is so fast compared to the other steps that using
+ several threads would make no sense (it would also quite probably become
+ IO bound if we tried anyway).
+</p>
+</li>
+<li>
+<p>
+The <strong>Xapian</strong> library index updating code is not designed for
+ multi-threading and must stay protected from multiple accesses.
+</p>
+</li>
+</ul></div>
+<div class="paragraph"><p>The two other steps are good candidates.</p></div>
+<div class="paragraph"><p>Most of the work to make <strong>Recoll</strong> code reentrant had been performed for the
+previous transformation. Going full-parallel only implied protecting the
+data structures that needed to be shared by the threads performing a given
+processing step.</p></div>
+<div class="paragraph"><p>Just for the anecdotic value, a list of the elements that needed mutexes:</p></div>
+<div class="ulist"><ul>
+<li>
+<p>
+Filter subprocesses cache: some file conversion subprocesses may be
+ expensive (starting a Python process is no piece of cake), so they are
+ cached for reuse after they are done translating a file. The shared cache
+ needs protection.
+</p>
+</li>
+<li>
+<p>
+Status updates: an object used to update the current file name and indexing
+ status to a shared file.
+</p>
+</li>
+<li>
+<p>
+Missing store: the list of missing helper programs
+</p>
+</li>
+<li>
+<p>
+The readonly <strong>Xapian</strong> database object: a Xapian::Database object which is
+ used for checking the validity of current index data against a file’s
+ last modification date.
+</p>
+</li>
+<li>
+<p>
+Document existence map: a bit array used to store an existence bit about
+ every document, and purge the disappeared at the end of the indexing
+ pass. This is accessed both from the file conversion and database update
+ code, so it also needed protection in the previous assembly line
+ approach.
+</p>
+</li>
+<li>
+<p>
+Mbox offsets cache. Used to store the offsets of individual messages
+ inside <strong>mbox</strong> files.
+</p>
+</li>
+<li>
+<p>
+<strong>iconv</strong> control blocks: these are cached for reuse in several places, and
+ need protection. Actually, it might be better in multithreading context
+ to just suppress the reuse and locking. Rough tests seem to indicate that
+ the impact on overall performance is small, but this might change with
+ higher parallelism (or not…).
+</p>
+</li>
+</ul></div>
+<div class="paragraph"><p>The <strong>Recoll</strong> configuration also used to be managed by a single shared
+object, which is mutable as values may depend on what area of the
+file-system we are exploring, so that the object is stateful and updated as
+we change directories. The choice made here was to duplicate the object
+where needed (each indexing thread gets its own). This gave rise to the
+sneakiest bug in the whole transformation (see further down).</p></div>
+<div class="paragraph"><p>Having a dynamic way to define the threads configuration makes it easy to
+experiment. For example, the following data defines the configuration that
+was finally found to be best overall on my hardware:</p></div>
+<div class="literalblock">
+<div class="content">
+<pre><tt>thrQSizes = 2 2 2
+thrTCounts = 4 2 1</tt></pre>
+</div></div>
+<div class="paragraph"><p>This is using 3 queues of depth 2, 4 threads working on file conversion, 2
+on text splitting and other document processing, and 1 on Xapian updating
+(no choice here).</p></div>
+<div style="clear:both;"></div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_bench_results">Bench results</h2>
+<div class="sectionbody">
+<div class="paragraph"><p>So the big question after all the work: was it worth it ? I could only get
+a real answer when the program stopped crashing, so this took some time and
+a little faith…</p></div>
+<div class="paragraph"><p>The answer is mostly yes, as far as I’m concerned. Indexing tests running
+almost twice as fast are good for my blood pressure and I don’t need a
+faster PC, I’ll buy more red wine instead (good for my health too, or maybe
+not). And it was a fun project anyway.</p></div>
+<div class="tableblock">
+<table rules="all"
+width="70%"
+frame="border"
+cellspacing="0" cellpadding="4">
+<caption class="title">Table 1. Results on a variety of file system areas:</caption>
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<thead>
+<tr>
+<th align="left" valign="top">Area </th>
+<th align="left" valign="top">Seconds before </th>
+<th align="left" valign="top">Seconds after</th>
+<th align="left" valign="top"> Percent Improvement</th>
+<th align="left" valign="top"> Speed Factor</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="left" valign="top"><p class="table">home</p></td>
+<td align="left" valign="top"><p class="table">12742</p></td>
+<td align="left" valign="top"><p class="table">6942</p></td>
+<td align="left" valign="top"><p class="table">46%</p></td>
+<td align="left" valign="top"><p class="table">1.8</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">mail</p></td>
+<td align="left" valign="top"><p class="table">2700</p></td>
+<td align="left" valign="top"><p class="table">1563</p></td>
+<td align="left" valign="top"><p class="table">58%</p></td>
+<td align="left" valign="top"><p class="table">1.7</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">projets</p></td>
+<td align="left" valign="top"><p class="table">5022</p></td>
+<td align="left" valign="top"><p class="table">1970</p></td>
+<td align="left" valign="top"><p class="table">61%</p></td>
+<td align="left" valign="top"><p class="table">2.5</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">pdf</p></td>
+<td align="left" valign="top"><p class="table">2164</p></td>
+<td align="left" valign="top"><p class="table">770</p></td>
+<td align="left" valign="top"><p class="table">64%</p></td>
+<td align="left" valign="top"><p class="table">2.8</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">otherhtml</p></td>
+<td align="left" valign="top"><p class="table">5593</p></td>
+<td align="left" valign="top"><p class="table">4014</p></td>
+<td align="left" valign="top"><p class="table">28%</p></td>
+<td align="left" valign="top"><p class="table">1.4</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="tableblock">
+<table rules="all"
+width="70%"
+frame="border"
+cellspacing="0" cellpadding="4">
+<caption class="title">Table 2. Characteristics of the data</caption>
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<col width="20%" />
+<thead>
+<tr>
+<th align="left" valign="top">Area </th>
+<th align="left" valign="top"> Files MB </th>
+<th align="left" valign="top"> Files </th>
+<th align="left" valign="top"> DB MB </th>
+<th align="left" valign="top"> Documents</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="left" valign="top"><p class="table">home</p></td>
+<td align="left" valign="top"><p class="table">64106</p></td>
+<td align="left" valign="top"><p class="table">44897</p></td>
+<td align="left" valign="top"><p class="table">1197</p></td>
+<td align="left" valign="top"><p class="table">104797</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">mail</p></td>
+<td align="left" valign="top"><p class="table">813</p></td>
+<td align="left" valign="top"><p class="table">232</p></td>
+<td align="left" valign="top"><p class="table">663</p></td>
+<td align="left" valign="top"><p class="table">47267</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">projets</p></td>
+<td align="left" valign="top"><p class="table">2056</p></td>
+<td align="left" valign="top"><p class="table">34504</p></td>
+<td align="left" valign="top"><p class="table">549</p></td>
+<td align="left" valign="top"><p class="table">40281</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">pdf</p></td>
+<td align="left" valign="top"><p class="table">1123</p></td>
+<td align="left" valign="top"><p class="table">1139</p></td>
+<td align="left" valign="top"><p class="table">111</p></td>
+<td align="left" valign="top"><p class="table">1139</p></td>
+</tr>
+<tr>
+<td align="left" valign="top"><p class="table">otherhtml</p></td>
+<td align="left" valign="top"><p class="table">3442</p></td>
+<td align="left" valign="top"><p class="table">223007</p></td>
+<td align="left" valign="top"><p class="table">2080</p></td>
+<td align="left" valign="top"><p class="table">221890</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="paragraph"><p><em>home</em> is my home directory. The high megabyte value is due to a number of
+very big and not indexed <strong>VirtualBox</strong> images. Otherwise, it’s a wide
+mix of source files, email, miscellaneous documents and ebooks.</p></div>
+<div class="paragraph"><p><em>mail</em> is my mail directory, full of <strong>mbox</strong> files.</p></div>
+<div class="paragraph"><p><em>projets</em> mostly holds source files, and a number of documents.</p></div>
+<div class="paragraph"><p><em>pdf</em> holds random <strong>pdf</strong> files harvested on the internets. The performance
+is quite spectacular, because most of the processing time goes to
+converting them to text, and this is done in parallel. Probably could be
+made a bit faster with more cores, until we hit the <strong>Xapian</strong> update speed
+limit.</p></div>
+<div class="paragraph"><p><em>otherhtml</em> holds myriad of small html files, mostly from
+<strong>wikipedia</strong>. The improvement is not great here because a lot of time is
+spent in the single-threaded <strong>Xapian</strong> index update.</p></div>
+<div class="paragraph"><p>The tests were made with queue depths of 2 on all queues, and 4 threads
+working on the file conversion step, 2 on the term generation.</p></div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_a_variation_linear_parallelism">A variation: linear parallelism</h2>
+<div class="sectionbody">
+<div class="paragraph"><p>Once past the assembly-line idea, another possible transformation would be
+to get rid of the two downstream queues, and just create a job for each
+file and let it go to the end (using a mutex to protect accesses to the
+writable <strong>Xapian</strong> database).</p></div>
+<div class="paragraph"><p>With the current <strong>Recoll</strong> code, this can be defined by the following
+parameters (one can also use a deeper front queue, this changes little):</p></div>
+<div class="literalblock">
+<div class="content">
+<pre><tt>thrQSizes = 2 -1 -1
+thrTCounts = 4 0 0</tt></pre>
+</div></div>
+<div class="paragraph"><p>In practise, the performance is close to the one for the multistage
+version.</p></div>
+<div class="paragraph"><p>If we were to hard-code this approach, this would be a simpler
+modification, necessitating less changes to the code, but it has a slight
+inconvenient: when working on a single big multi-document file, no
+parallelism at all can be obtained. In this situation, the multi-stage
+approach brings us back to the assembly-line behaviour, so the improvements
+are not great, but they do exist.</p></div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_miscellany">Miscellany</h2>
+<div class="sectionbody">
+<div class="sect2">
+<h3 id="_the_big_gotcha_my_stack_dump_staring_days">The big gotcha: my stack dump staring days</h3>
+<div class="paragraph"><p>Overall, debugging the modified program was reasonably
+straightforward. Data access synchronization issues mostly provoke dynamic
+data corruption, which can be beastly to debug. I was lucky enough that
+most crashes occurred in the code that was actually related to the
+corrupted data, not in some randomly located and unrelated dynamic memory
+user, so that the issues were reasonably easy to find.</p></div>
+<div class="paragraph"><p>One issue though kept me working for a few days. The indexing process kept
+crashing randomly at an interval of a few thousands documents, segfaulting
+on a bad pointer. An access to the configuration data structure seemed to
+be involved, but, as each thread was supposed to have its own copy, I was
+out of ideas.</p></div>
+<div class="paragraph"><p>After reviewing all the uses for the configuration data (there are quite a
+few), the problem was finally revealed to lie with the filter process
+cache. Each filter structure stored in the cache stores a pointer to a
+configuration structure. This belonged to the thread which initially
+created the filter. But the filter would often be reused by a different
+thread, with the consequence that the configuration object was now accessed
+and modified by two unsynchronized threads… Resetting the config pointer
+at the time of filter reuse was the ridiculously simple single-line fix to
+this evasive problem.</p></div>
+<div class="paragraph"><p>Looking at multi-threaded stack dumps is mostly fun for people with several
+heads, which is unfortunately not my case, so I was quite elated when this
+was over.</p></div>
+</div>
+<div class="sect2">
+<h3 id="_fork_performance_issues">Fork performance issues</h3>
+<div class="paragraph"><p>On a quite unrelated note, something that I discovered while evaluating the
+program performance is that forking a big process like <tt>recollindex</tt> can be
+quite expensive. Even if the memory space of the forked process is not
+copied (it’s Copy On Write, and we write very little before the following
+exec), just duplicating the memory maps can be slow when the process uses a
+few hundred megabytes.</p></div>
+<div class="paragraph"><p>I modified the single-threaded version of <tt>recollindex</tt> to use <strong>vfork</strong>
+instead of <strong>fork</strong>, but this can’t be used with multiple threads (no
+modification of the process memory space is allowed in the child between
+<strong>vfork</strong> and <strong>exec</strong>, so we’d have to have a way to suspend all the threads
+first).</p></div>
+<div class="paragraph"><p>I did not implement a solution to this issue, and I don’t think
+that a simple one exists. The workaround is to use modest <strong>Xapian</strong> flush
+values to prevent the process from becoming too big.</p></div>
+<div class="paragraph"><p>A longer time solution would be to implement a small slave process to do
+the executing of ephemeral external commands.</p></div>
+</div>
+</div>
+</div>
+</div>
+<div id="footnotes"><hr /></div>
+<div id="footer">
+<div id="footer-text">
+Last updated 2012-12-04 11:14:07 CET
+</div>
+</div>
+</body>
+</html>