How to cite this paper

Probst, Martin. “Processing Arbitrarily Large XML using a Persistent DOM.” Presented at Balisage: The Markup Conference 2010, Montréal, Canada, August 3 - 6, 2010. In Proceedings of Balisage: The Markup Conference 2010. Balisage Series on Markup Technologies, vol. 5 (2010).

Balisage: The Markup Conference 2010
August 3 - 6, 2010

Balisage Paper: Processing Arbitrarily Large XML using a Persistent DOM

Martin Probst

Martin Probst is a senior software engineer at EMC, working on EMC Documentum xDB. He has been working on XML databases and XQuery in particular since 2004. Martin holds a MSc. in Software Engineering from Hasso Plattner Institute in Potsdam, Germany.

Copyright © 2010 by EMC Corporation. All rights reserved.


As the adoption of XML reaches more and more application domains, data sizes increase, and efficient XML handling gets more and more important. Many applications face scalability problems due to the overhead of XML parsing, the difficulty of effectively finding particular XML nodes, or the sheer size of XML documents, which nowadays can easily exceed gigabytes of data.

In particular the latter issue can make certain tasks seemingly impossible to handle, as many applications depend on parsing XML documents completely into a Document Object Model (DOM) memory structure. Parsing XML into a DOM typically requires close to or even more memory as the serialized XML would consume, thus making it prohibitively expensive to handle XML documents in the gigabyte range. Recent research and development suggests that it is possible to modify these applications to run a wide range of tasks in a streaming fashion, thus limiting the memory consumption of individual applications. However this requires not only changes in the underlying tools, but often also in user code, such as XSLT style sheets. These required changes can often be unintuitive and complicate user code.

A different approach is to run applications against an efficient, persistent, hard-disk backed DOM implementation that does not require entire documents to be in memory at a time. This talk will discuss such a DOM implementation, EMC's xDB, showing how to use binary XML and efficient backend structures to provide a standards compliant, non-memory-backed, transactional DOM implementation, with little overhead compared to regular memory-based DOMs. It will also give performance comparisons and show how to run existing applications transparently against xDB's DOM implementation, using XSLT stylesheets as an example.

Table of Contents

Related Work
Document Model Storage Structure
Traditional Document Model Implementation
Disk based DOM architecture
Storage Layout
Node Structure
Encoding of Primitives
Namebase Codes
Memory Consumption
Programming Model
Performance Comparisons
DOM navigation
XSL transformation


As the adoption of XML reaches more and more application domains, data sizes increase, and efficient XML handling gets more and more important. Many applications face scalability problems due to the overhead of XML parsing, the difficulty of effectively finding particular XML nodes, or the sheer size of XML documents, which nowadays can exceed gigabytes of data.

Parsing a sample 20 MB XML document[1] containing Wikipedia document abstracts into a DOM tree using the Xerces library roughly consumes about 100 MB of RAM. Other document model implementations[2] such as Saxon's TinyTree are more memory efficient; parsing the same document in Saxon consumes about 50 MB of memory. These numbers will vary with document contents, but generally the required memory scales linearly with document size, and is typically a single-digit multiple of the file size on disk. Extrapolating from these (unscientific) numbers, we can easily see that handling documents that range in the hundreds of megabytes will easily outgrow available memory on today's workstations, that typically have several gigabytes of memory available. Even when documents do not outgrow available memory, memory use is a concern in many applications, particularly in servers processing multiple requests in parallel.

Parsing the 20 MB XML file into xDB requires 16 MB of disk space. The amount of memory required is not related to document size and can be very small (in the kilobyte range).

Related Work

One approach to overcome memory issues is to perform streaming operations in very specific situations. Peng and Chawathe (Peng05) present a streaming implementation for a limited subset of XPath operations, Florescu, Hillery et al. (Florescu03) achieve the same for XQuery; the Streaming Transformations for XML (STX) language defines a language similar to XSLT geared at streamed execution (STX); and the unreleased XSLT 2.1 specification will contain special syntax to allow a limited streaming execution mode (Kay10).

However, streaming is a very limiting programming model. Many real world XML applications require access to preceding or following nodes, like the title of the section a node is contained in. To achieve streaming execution, the above approaches have to exclude or severly limit commonly used operations like backwards navigating axes.

Another approach is to use databases to store XML documents, moving them out of memory to secondary storage. There is a wide range of literature on the topic, with early efforts focusing on shredding XML documents to store them in relational databases, either in tables tailored to a specific XML schemata, or in a generic structure (Tatarinov02). As requiring schemata upfront contradicted user expectations and a generic database layout results in too many join operations for common hierarchy navigation, later approaches propose a variety of numbering schemes to encode the document structure (Meier02). While different solutions with different features exist, these numbering schemes generally support resolving at least some structural queries based on the numbering. Early schemes required re-numbering on document modifications, later ones overcome this limitation (ONeil04).

Using databases with numbering schemes, we can overcome main memory limitations for storage and search. However the question of how to efficiently process potentially large XML documents in application code remains.


This paper presents a different approach: a persistent, hard-disk based DOM that can store arbitrarily large XML documents and provides read/write access to them through the regular Java DOM API without using a node numbering scheme. Regular XML applications can use the familiar DOM programming model and the wealth of available XML tools relying on the DOM interface. This paper is based on EMC Documentum xDB's implementation of the presented concepts.

xDB differs from other persistent XML stores and other persistent DOM implementations in that it is optimized for direct DOM access. Navigation operations corresponding to the common DOM operations are executed in constant time comparable to main-memory based DOM implementations. xDB, unlike other XML databases, does not use a node numbering scheme but relies on indexes for fast value lookups.

We extensively describe xDB's data organization and storage layout, with a particular focus on efficient encoding of node data. We provide performance comparisons with two different main memory DOM implementations.

Document Model Storage Structure

Traditional Document Model Implementation

DOM implementations (or any document model) usually create classes for the various XML node types, and have fields to store the node's properties, like the node name, the list of children, the parent node, and so on.

Figure 1 shows a typical DOM element implementation, ignoring any inheritance chain and inlining all fields for the sake of simplicity. We can see that the Element node keeps references to other nodes in the tree, some basic references like the first child and the next sibling to implicitly form the tree itself, and some to enhance navigation speed, like the parent reference or the previous sibling reference, that could be emulated using the basic references.

Figure 1: A typical DOM Element Implementation (simplified)

There are many different ways of representing an XML document, differing in memory consumption, navigation speed for particular axes, whether the document can be modified after parsing, and so on. Some implementations avoid storing immediate object references to limit the number of objects held in memory, but still store the complete document in memory. However they all rely on the assumption that the complete document is parsed at once, and kept in memory while operating on it.

This leads to the observed limitations in supported document size caused by memory exhaustion.

Disk based DOM architecture

Our DOM implementation avoids this limitation by storing a binary representation of an XML document on the hard disk. While this representation avoids keeping the whole document in memory at all times, it is still directly navigable from the programming interface, without requiring re-parsing or buffering parts of the document during program execution. Documents are also mutable — nodes can modified after initial parsing, again without requiring the whole document to be in memory.

Nodes are identified using a 64 bit number and stored in data pages that allocate individual nodes in slots. The first 54 bit of a node identifier point to the page, the latter 10 bits identify a slot within a particular page. As the pages are stored in files in the file system, within the first 54 bits, 20 bits are used to identify a particular file used for storage. Node identifiers directly point to the physical location of a node; there is no indirection between node identifiers and physical node location (see section “Modifications” for a detailed discussion of the impact on updates). xDB node identifiers can be considered "physical identifiers".

This approach is quite different from other XML databases, that typically use a node numbering scheme to identify nodes, separating the logical node identifier from its physical identifier/location. A node numbering scheme can be helpful to answer unbounded structural queries, such as the XPath /bib//book//author. On the other hand, such numbering schemes only help evaluating queries over a large node set; navigating within individual nodes in a DOM-like fashion will probably be slower due to the additional indirection required (i.e., typically an index lookup by node ID). We assert that unbounded structural queries are rare in actual applications. Applications typically either search for specific pieces of information, or process a complete document or document fragment. Searching within XML can be mapped to value based indexes, while processing XML commonly means visiting more or less every node of the document, thus requiring efficient navigation. xDB provides configurable index structures that can answer combined structure and value queries (e.g. /bib//book[author/lastname = 'Doe']) to support efficient searching for nodes. To process nodes once they have been found, xDB resorts to efficient navigation using physical node identifiers.

Pages are of configurable size but should be identical in size to the underlying file system's page size, for reasons of data consistency in write operations[3]. This means pages are usually 4 kilobytes (Windows, Linux, Mac OS X) or 8 kilobytes (certain UNIXes).

Individual XML documents can spread across any number of physical files, pages, and slots, but always use at least one complete page; pages are not shared between multiple documents. The minimum storage size consumed by a document is thus one page (typically 4 kilobytes). The theoretical maximum size for an individual document is the number of possible files times the number of pages in a file, times the individual page size. A database configured with just a single file can thus (assuming 4096 bytes page size, using non-SI prefixes) hold documents up to 234 * 212 bytes = 246 ≈ 64 terabytes in size; an ideally configured database with 220 files could theoretically hold 266 bytes ≈ 64 exabytes. The attentive reader will have precluded that we have not been able to test the implementation with exabytes of storage. This is left as an exercise to the first customer reaching that database size. Databases with dozens of terabytes of data are common in customer deployments.

These theoretical storage limits refer to the size of documents in xDB's internal, proprietary binary XML format. We will show that this does not necessarily correspond to regular serialized XML size, but is typically comparable or slightly less.

Storage Layout

xDB's storage layout is depicted in Figure 2. Pages are stored in one of potentially many data files grouped together in segments. Data files are cached in main memory within a data structure called page cache. Each page contains multiple slots, which in turn store individual nodes. Slot size is not fixed - different nodes, even of the same type, can have different lengths. Slot allocation information is stored in an administrative section within the data page, giving start and end locations for each slot.

Figure 2: Storage Layout in xDB

The layout of an individual slot depends on the node's type. Storage layout is defined through an XML dialect, which is then used to generate Java source code through an XSL transformation as part of the build process.

Node Structure

xDB stores node trees much like other DOM implementations, spanning a tree through first-child and next-sibling pointers. The major difference is that xDB takes great care to reduce the size of objects, and that xDB directly operates on byte arrays to store nodes.

Figure 3 shows the bit layout of the two bytes comprising an XML Element header. All nodes start with a sequence of bits determining the node type. The most common nodes (elements and text nodes) only need two bits of storage for the type, as their type codes are 01 and 00. Other node types are stored using longer bit sequences starting with a bit 1. The following bits all represent booleans, indicating the presence or absence of fields in the elements storage and other properties of the node and its storage layout. This reduces on-disk storage consumption for elements that do not have children or siblings and documents that do not use various features like namespaces, post schema validation infoset (PSVI) information, and so on. The bit fields read only and namespace node handle two special cases. Inlined entity reference nodes are marked as read only to protect against incorrect modifications, thus the bit field. Additionally, xDB supports DOM Level 1 documents that do not have namespace support. For those documents, compliant DOM implementations must return null for calls to the namespace-aware methods like getLocalName(). If the bit is set to 0, xDB will return null on those calls.

Figure 3: DOM Element Header

For an element, this header is followed by the following, partially optional fields:

  • Parent reference

  • Next sibling reference (optional)

  • Previous sibling reference (optional)

  • First child reference (optional)

  • Last child reference (optional)

  • Local name code (integer)

  • Namespace URI code (integer, optional)

  • Prefix name code (integer, optional)

  • First attribute reference (optional)

  • Primitive type name code (integer, optional)

  • Complete type name code (integer, optional)

  • PSVI properties (integer, optional)

These fields following this header can have different types: 32 bit numbers, 64 bit numbers, node references as relative 64 bit node IDs, strings, and string lists. The presence of optional fields if governed by their respective bit field in the element header. If a field is absent, it takes no storage space at all and retrieving it will return a specified default value, for example null for node references or -1 for undefined namebase codes.

Another storage size optimization is the First Child next? bit field. If true, the following node on the data page is the first child of this node, allowing us not to store the node ID. This reflects the observation that most XML documents are parsed once and rarely modified. In that case, the parsing process will cause an element's children to be stored directly following the element within the data page.

xDB stores all different DOM node types using this scheme, exploiting the various optimizations possible for different node types - for example, attribute nodes do not need a type field as their type is implicitly known at the place where they are referenced.

Encoding of Primitives

All numeric fields, i.e. integers, longs, and node references, are stored using run-length encoded numbers. This means that, for example, a 64 bit number that would normally consume 8 bytes of storage space will be compressed depending on its actual magnitude. Values below 0x80 (128 decimal) take only one byte of storage space, values below 0x4000 (16384 decimal) take only two bytes, and so on. This significantly reduces storage size as node references are not stored as the complete node ID, but as the relative offset to the current node ID, giving small numbers even in large databases. Node references are additionally left shifted and then complemented if negative so that small negative offsets are stored as small positive numbers, giving a more compressed number. Because nodes are typically added in batch (either during parsing, or during larger modifications), nodes usually reference other nodes that are very close to them on the data pages. This means we can typically store node references in a byte or two. The same applies to the 32 bit numbers used for namebase codes (see below).

Text data from XML text nodes, CDATA sections, and attribute values is stored using an encoding scheme similar to UTF-8. In UTF-8 encoded text, it is always possible to identify whether a byte is starting a new Unicode codepoint, or if it is part of a multi-byte encoded codepoint that started earlier. To support this, UTF-8 'wastes' some bits that could otherwise be used to encode the codepoints themselves more compact. xDB does not need this functionality as our storage layout explicitly marks start and end of strings, allowing us to encode text data a bit compacter. Text data longer than a single database page is stored using special text-only database pages to avoid the overhead of page administration.

String lists, such as the value of attributes of type NMTOKENS, are stored as a list of zero-byte separated strings.

Namebase Codes

xDB stores node names using a so-called namebase, a table mapping integer codes to individual NCNames. This both reduces storage size and replaces string comparisons with simple integer comparisons for name comparisons. URI, prefix, and local name are stored separately to speed up queries using wildcard XPath name tests like prefix:*. While namespace prefixes are not significant according to the XML infoset standard and standards like XPath, they are significant in other standards, like the DOM. Because of this, and because of the user expectation to have complete document fidelity, we store the namespace prefix.


Modifying a node simply means modifying the underlying data page, to reflect the changes that are made to the node. All DOM operations, such as replaceNode, are directly reflected in the underlying byte arrays.

A problem with the very condensed layout of nodes is that a node might require more storage space than available in its slot after an update. For example, if we add a child element to an XML element node that was previously empty, the element will need more storage space. In the simple case, the space in the data page that is immediately following the element is unused, and we can simply grow the element.

In the complex case however, the space following the element node is already used by another node, and we cannot grow the node. At the same time, we cannot simply store the element at a different place and free the originally used storage space, as other nodes will be directly pointing to the current location of the node. If we were to move a node, we would have to update all existing pointers to this node, which is a potentially very expensive operation, requiring the complete database to be scanned.

To avoid this, xDB replaces the current node store with a forwarding pointer that will always be small enough to fit in the available space. The pointer references the actual location of the node in the database, and all operations on nodes are safeguarded by a check for a forwarding reference.

This scheme has the drawback that documents receiving many update operations can fragment over time. It is possible to create degenerate cases where many nodes will be replaced by forwarding pointers. Again, the observation is that most XML documents are parsed exactly once and hardly ever modified.


Nodes are represented at runtime by regular Java objects. The node storage layout is defined by an XML file that also specifies class name, super class, and the class this node is owned by (e.g. document for most DOM nodes). In the build process, an XSLT stylesheet transforms these XML definitions into a Java source file, by convention ending in 'Store' (e.g. DomElementStore). The Java file defines an abstract class that provides getters and setters for the node's fields operating directly on the page byte array. This is then extended by a concrete class that provides the public API for a given DOM node, e.g. DomElement. This class then implements the respective org.w3c.dom interface, typically along several other xDB specific ones. That allows us to keep generated and handwritten code separate.

From a Java perspective, every DOM node object instance references only the page (as a Java object) and the slot (as an int), thus instantiated DOM node objects consume, depending on JVM implementation and CPU architecture, between 16 and 32 bytes of main memory. The page object references the byte array storing page data only through a level of indirection, so even if a DOM node is instantiated in a Java program, the underlying data page can be swapped out by the cache manager.

Method calls on a DOM interface cause the data page to be obtained (and loaded into memory, if necessary), after which we decode the requested information through a sequence of bit operations. The traditional "fields" of an XML element like children, node name, etc. are never cached. This allows us to keep the memory footprint of a Java application minimal - only DOM nodes directly referenced by a user program are actually kept in memory.

shows the Java code retrieving the first child of a DOM element node, pretty printed and simplified from the original, generated version. The actual code is guarded by a method that checks permissions and whether the current transaction is open or interrupted.

The code then retrieves the byte array holding the data of the page where the node is stored. This might trigger the page to be swapped in if needed. The code then determines the start and end offsets of this node within the data page. If the node is marked as a forwarding object, the code will retrieve the actual data page and update start and end offsets (not shown).

In the series of if statements afterwards, we check the bit fields in the first byte of the element header, referenced by a[start]. The first comparison checks the third bit ("First Child null?") and returns null if true, the second comparison checks if "First Child Next?". In that case, the diff (offset of the first child node relative to this node) is one — it is the next node on this page. Otherwise, we have to actually read the node reference, by first finding it in the storage and putting the position in the variable fs. First we add two to skip the element header (two bytes). Because we don't know the length of the first reference (the parent), we then read the long, and skip as many bytes as it consumes in storage (packedLongLen()). The next two references are optional, so we check whether they are present using bit comparisons again, and skip ahead if they are present. Afterwards we read the actual difference, convert the transformed relative difference back, and add it to our current node identifier (getPageAndSlot() in the code), retrieve a page object and compute the slot from the number. The call to DomNode.newDomNode() will inspect the type field of the referenced object and return a new instance of the appropriate subclass, pointing to the new node ID.

Figure 4: Retrieving the first child of an element (Java)

DomNode getFirstChildRef() {
  Page slotpage = getPage();
  byte[] a = slotpage.getDataReadOnly();
  int slot = getSlot();
  int offset = getOffset(a, slot);
  int start = Page.unpack2(a, offset);
  int end = Page.unpack2(a, offset - 2);
  if (a[start] <= FORWARD_OBJECT) {
    // handle forwarded objects by updating a, start, and end

  long diff;
  if ((a[start] & 1 << 3) != 0) {
    return null;
  } else if ((a[start] & 1 << 2) != 0) {
    diff = 1L;
  } else {
    int fs = start;
    fs += 2;
    fs += packedLongLen(a, fs);
    if ((a[start + 1] & 1 << 7) == 0)
      fs += packedLongLen(a, fs);
    if ((a[start + 1] & 1 << 6) == 0)
      fs += packedLongLen(a, fs);

    assert fs < end;
    diff = getRelDiff(unpackLong(a, fs));
  long id = getPageAndSlot() + diff;
  Page idpage = getPageFromPS(id);
  int idslot = getSlotFromPS(id);

  return DomNode.newDomNode(idpage, idslot);

Because we don't reference or cache constructed child objects, in xDB's DOM implementation one logical node might be represented by many different Java object instances. This is in line with the W3C DOM specification that requires users to compare nodes for identity using the function isSameNode(), but sometimes surprising to programmers that expect a simple equality comparison (node1 == node2) to be sufficient. Modifications to logical nodes are immediately reflected by all Java DOM node instances, because they are reflected in the underlying byte arrays.

The amount of work needed for something as simple as retrieving the first child of an element might seem staggering, but the time these bit shift operations take is quite small in practice, as we will show in section “Performance Comparisons”.

On Disk Layout Example

Figure 5 shows a simple example document, and the binary representation of the first two elements on disk in the canonical hex formatting. The bytes surrounding the two elements are blanked out as they are of no interest.

The document root element, <address_list/>, is stored in the five bytes on the first line. The first two bytes, representing the element header, indicate that this is a regular element node with no sibling or attributes, but a first and a last child, the first child being the immediately following node on the page. These bytes are then followed by the parent offset (encoded as 01, meaning an offset of -1, i.e. the previous node), the offset of the last child (70), and the namebase code of the local name (00).

The document was parsed with the element-content-whitespace option disabled, so the white space characters preceding the first <address/> element were stripped and did not result in a DOM text node.

The first <address/> element is stored in 8 bytes. The header indicates that all node reference fields are present, except for the previous sibling. This is then followed by the encoded offset of the parent (again, -1), the next sibling (1a), the offsets of first and last child (04 and 16), the namespace code (01), and the offset of the first attribute (02, meaning +1).

Figure 5: On disk layout of a sample document

  <address id='1'>

0001e0d0  __ __ __ __ __ __ __ __  __ __ __ 15 ff 01 70 00  |ttmjtu2/ynm...p.|
0001e0e0  10 7f 01 1a 04 16 01 02  __ __ __ __ __ __ __ __  |........?...B...|

The root element <address_list/> consumes 29 bytes of disk storage when stored as regular XML on disk in UTF-8 (opening and closing tag). In xDB's binary format, it shrinks to 5 bytes, plus 4 bytes for the slot information (not shown above), plus a certain amount of storage for the namebase entry which is shared among all elements with the same name. The more complex <address/> element consumes 19 vs 8 bytes. This reduction in size depends on the document and the use of XML features, such as validation and namespaces. However we can see that in particular elements containing predominantly elements will shrink in size.

The original addresslist XML document consumed 1138 bytes of disk space, 975 bytes with ignorable whitespace removed. The binary document representation consumes 768 bytes (within a 4kb page), giving a bloat factor of ~0.78. These numbers can greatly vary from document to document, depending on factors such as namespace use, number of nodes, and so on. The general experience with XML documents is that the bloat factor will typically be around 0.9, giving a 10% compression.

Memory Consumption

As mentioned above, in xDB DOM nodes are only shallow pointers into the data storage. The data itself is stored in files, and partially paged into memory in a page cache. These pages are kept in Java byte arrays at runtime and DOM operations directly operate on the byte arrays. As DOM nodes only indirectly reference these byte arrays, the database engine is free to evict data pages from the cache and/or load other cache pages in.

A JVM running an xDB database server will allocate a fixed amount of memory to the xDB page cache, similar to other databases. The amount of memory taken is configurable, and the database can run correctly with only one data page of cache memory. However, again like other databases, less cache memory means more page faults, which in turn means slower operation. The ideal amount of memory to allocate for cache pages is difficult to determine, as more cache memory will mean that less memory is available to the regular Java application code. A very large cache will thus cause more garbage collection runs, which will again slow down the application. Depending on the application, the available Java heaps space might even get exhausted if it is mostly consumed by xDB cache pages. As a rule of thumb, users are advised to allocate half of the Java heap to the database cache, which works well in most cases.

xDB's page cache uses a combination of Least Recently Used (LRU) and an aging cache scheme. This means that if the cache has to evict a page, it will attempt to first evict pages that have not been used for a long time, but prefer evicting pages that have been used few times over pages that have been used frequently. If a user program uses a piece of data frequently and also scans a large set of data, only referencing every page once, this means that the cache will not be completely overwritten by the large scan.

Beyond the amount of memory allocated for the page cache, xDB itself uses some memory for internal data structures like the cache itself, administrative data for background threads, and so on. The amount of memory used for these data structures is however fixed, and not proportional to the size of documents or other structures contained in the library. A full xDB database server can run in a JVM with 16 megabytes of RAM allocated to it.

Because xDB does not force DOM structures to stay in memory, applications can perform DOM operations on documents of effectively arbitrary size. However it is still possible to write the user application in a way that causes memory exhaustion. For example, a program traversing a large document and adding all DOM nodes it encounters to a data structure will exhaust memory, because it keeps the individual DOM node objects from being garbage collected. Our experience however shows that such programs are a not common.

Programming Model

xDB is a compliant implementation of the W3C DOM level 3 standard. xDB's DOM nodes implement the relevant Java interfaces of the org.w3c.dom package, including the extensions for loading and saving documents (DOM LS).

This means that program code written against the familiar classes, such as e.g. org.w3.dom.Element, will work without modification when using xDB. The only differences in program code will be in the way the initial Document instance is obtained.

Figure 6 shows a sample code snippet operating on an xDB-backed DOM. The code first obtains an XhiveDriverIf, which is the main Java entry point to all xDB operations. After initializing the driver, which will start background threads and allocate the page cache, the code creates a new XhiveSessionIf, which represents the context of transactional operations. The code then begins a new transaction, retrieves a document that is stored in the database's root library[4]. The object representing the document implements the org.w3c.dom.Document interface. Any program code written for any Java DOM implementation can now operate on the document. In particular, XSLT engines that support DOM documents (i.e. all Java XSLT engines) can transparently perform transformations on these documents. All changes to the document will be persisted in the session.commit() call, or rolled back on session.rollback(), if the user code throws an unexpected exception. Multiple transactions can operate on database and library contents concurrently in complete isolation. xDB transactions are of serializable isolation level and conform to the ACID properties.

Figure 6: Obtaining a Document reference from xDB

XhiveDriverIf driver = XhiveDriverFactory.getDriver("xhive://localhost:1235");
XhiveSessionIf session = driver.createSession();
session.connect("username", "password", "Database");
try {
  XhiveLibraryIf rootLibrary = session.getDatabase().getRoot();
  org.w3c.dom.Document document = rootLibrary.get("somedocument.xml");
  // operate on document
} finally {
  if (session.isOpen()) session.rollback();

xDB locks database contents to avoid concurrent modifications. Locking happens on a per-document basis; parallel transactions can modify separate documents concurrently, but if a document is already locked by another transaction, the transaction will block. Deadlocks are resolved by selecting a victim, whose transaction will be cancelled and rolled back.

Performance Comparisons

While the indirection of storage and the bit operations cause a certain overhead, performance is comparable to other DOM implementations. Some synthetic benchmarks[5] show that common XML operations take on the order of twice as long in execution as in other implementations, while requiring significantly less memory. XSL transformations on gigabyte-sized XML documents are possible and run in reasonable time.

All the following benchmarks have been run on a MacBook Pro 2.8 GHz Core 2 Duo machine with 4 GB of DDR3 RAM and a solid state disk drive. The Java implementation was Apple's modified distribution of the Sun HotSpot JVM in server mode (-server), giving 512 MB of RAM if not otherwise indicated (-Xmx512M). All times were measured after running the benchmark code several times in a warmup phase to account for Just-In-Time compilation (JIT). All implementation tests were run in a clean JVM to avoid skewing results through the effects of class loading and the different compilation results it can produce. After warmup, individual benchmarks were run 100 times (except for the long running large XSLT transformation tests, which were run 10 times each); results are given as the average runtime with standard deviation.

The three implementations used were Saxon Home Edition, xDB 10.0.0, Xerces 2.10.0, and Xalan 2.7.1.


An important performance benchmark in XML processing is parsing XML documents. In this benchmark, we parsed a 20 MB XML document of Wikipedia article titles, URLs, abstracts, and links. The document has a relatively simple, flat structure, with an element for every document that has several children for the document's details.

Table I

Parsing a 20 MB XML document

Implementation Average Runtime (ms) Standard Deviation (ms)
Saxon 696 45
xDB 1388 8
xDB (-Xmx64M) 1565 7
Xerces 624 98

xDB's parser and DOM implementation take about twice as long to parse a document, probably due to the conversions necessary for xDB's internal storage. The lower standard deviation compared to the other two implementations is most likely because xDB creates less objects in memory, and thus is affected less by Java garbage collection.

Reducing the available memory to 64 megabytes, the other two implementations failed parsing the document due to memory exhaustion. xDB's parsing time increases, as the parsed document will no longer easily fit the available cache memory and parts have to be paged out. If we increase document size further or decrease available memory more, the parsing process will be effectively limited by the throughput of the hard disk.

More thorough scalability tests of xDB XML parsing can be found in a whitepaper by Jeroen van Rotterdam (vanRotterdam09)

DOM navigation

In this test, we walk the document from the parsing test using DOM navigation, or in the case of Saxon using it's XDM data model. The tests performs an in-order visit of all nodes in the document, retrieving text values for attributes and text nodes, and node names for nodes that have a name (elements, attributes, processing instructions). The numbers for Saxon are not entirely comparable, as the code uses a different document object model to achieve the same task. Document parsing time is not included in the numbers.

Table II

Walking a 20 MB XML DOM

Implementation Average Runtime (ms) Standard Deviation (ms)
Saxon 78.6 1.1
xDB 204.2 0.8
xDB (-Xmx64M) 348.9 0.8
Xerces 95.8 0.4

Again, processing takes about twice as long on xDB's DOM implementation. Runtime increases slightly with constrained memory.

XSL transformation

In this test, we transform the aforementioned document using a simple XSLT 1.0 stylesheet. We use the Saxon and Xalan XSLT implementations, both on their native DOM implementations (Xerces for Xalan), and both on xDB's DOM implementation.

We can again see that xDB's DOM is slower than the XSLT processor's native implementations, but again the overhead is tolerable. Saxon performs slightly worse than Xalan on xDB's DOM; initial investigation suggest this might be due to Saxon's DOM driver that operates on DOM node lists, which are computationally expensive as they have to be live lists, directly reflecting any changes in the parent node. This is problematic in an implementation such as xDB, where single logical DOM nodes can be represented by many different objects. In addition, xDB does not maintain a list with all children of a node, so that indexed access to a child within a DOM list is not O(1) in all cases.

Table III

XSLT transformation of a 20 MB XML document

Implementation Average Runtime (ms) Standard Deviation (ms)
Saxon 90 6
xDB (Xalan) 160 4
xDB (Saxon) 251 10
Xalan (Xerces) 155 17

We performed the same test on an identically structured document of 900 MB. Neither Saxon nor Xalan were able to parse the document, even with 3.5 GB of memory available. Running Xalan on xDB exhausted memory as well, as the implementation appears to copy some of the DOM structure into it's own data structures, exhausting memory in the process.

Running Saxon on top of xDB's DOM implementation managed to transform the document in reasonable time. Both with a large (512 MB) and a small heap (64 MB), the document was transformed in less than a minute. Available memory has no noticeable impact on performance; runtime is effectively limited by IO throughput as xDB loads cache pages from the disk. This also explains the supra-linear runtime growth from 250 ms for a 20 MB document to 45 seconds for a 900 MB document; the first test exclusively uses heap memory, while the second test is limited at least in part by disk performance.

Table IV

XSLT transformation of a 900 MB document using Saxon on an xDB DOM with different Java heap sizes

Available JVM Heap Memory Average Runtime (seconds) Standard Deviation (seconds)
64 MB 47 1
512 MB 45 2

Interestingly, runtime profiling of these simple XSLT transformations shows that significant time (15-20%) is spent looking up element names by their namebase code. This could be entirely avoided if the DOM API provided an interface to create an implementation specific node name matcher. That would not only allow us to avoid the namebase lookup but also reduce relatively expensive string comparisons on node names to integer comparisons on namebase codes in xDB's case.


The benchmark results show that xDB's hard-disk backed DOM implementation is slower than alternative main memory based implementations for small document sizes. On the other hand, our scheme makes it possible to handle documents of sizes that simply cannot be processed using main memory implementations. Beyond that, xDB ofcourse offers many other database features that might be useful to users.

The fact that xDB performs well compared to a DOM implementation such as Xerces might be surprising, given that, when navigating to the first child of an element, a Xerces DOM node can simply return the value of a field (or the first value of an array) where xDB has to perform a whole series of bit manipulations. We believe that this is an effect of a modern processor's memory hierarchy. As CPU internal caches and clock rates are significantly faster than main memory access, modern CPUs spend most of their clock cycles waiting for memory to be loaded into the CPU's cache (Manegold00, Chilimbi99). xDB's compact storage layout sacrifices several CPU operations for reduced memory use, but those CPU operations can execute in the time a CPU would normally spend waiting for memory to be loaded. Because related nodes are allocated in contiguous byte arrays, it is also more likely that the CPU already holds the memory location for the next node in a cache line.

Note that these benchmarks assume a document that is parsed once to be processed, and then discarded. In real world applications, documents will often be processed many times. Because of its persistent storage, xDB can avoid re-parsing documents over and over again, giving substantial benefits for such applications.



[Peng05] F Peng, S S Chawathe: XSQ: A Streaming XPath Engine. ACM Trans. Database Syst (2005). doi:

[Florescu03] Daniela Florescu, Chris Hillery, Donald Kossmann, Paul Lucas, Fabio Riccardi, Till Westmann, Michael J. Carey, Arvind Sundararajan, Geetika Agrawal: The BEA Streaming XQuery Processor. The VLDB Journal (2003)

[STX] Streaming Transformations for XML. Homepage, on the web at

[Kay10] Michael Kay: XSL Transformations (XSLT) 2.1. World Wide Web Consortium Working Draft (2010), on the web at

[ONeil04] P. O’Neil, E. O’Neil, S. Pal, I. Cseri, G. Schaller, and N. Westbury. ORDPATH: Insert-Friendly XML Node Labels. SIGMOD, 2004. doi:

[Meier02] Wolfgang Meier: eXist: An Open Source Native XML Database. NODe Workshop (2002)

[Manegold00] Manegold, S., Boncz, P.A., and Kersten, M.L: Optimizing database architecture for the new bottleneck: memory access. VLDB Journal (2000)

[Chilimbi99] T. M. Chilimbi, B. Davidson J. R. Larus: Cache-conscious structure layout. ACM Sigplan Notices (1999). doi:

[Tatarinov02] I. Tatarinov, S. D. Viglas, K. Beyer, J. Shanmugasundaram, E. Shekita, C. Zhang: Storing and Querying Ordered XML Using a Relational Database System. SIGMOD (2002). doi:

[vanRotterdam09] J. van Rotterdam: EMC Documentum xDB 9.0 Scalability Tests. Whitepaper, on the web at

[1] Encoded as UTF-8 text. In UTF-16, the same document (as expected) takes about 40 MB.

[2] While technically, DOM is the name of a particular standardized XML document model programming interface, for the sake of this paper, any document model implementation is equivalent. We are concerned with how to store XML documents as navigable structures, not any particular programming interface against them. As the DOM is the most prevalent document model interface, we use the term interchangeably.

[3] When flushing data to disk, it is only guaranteed that an individual page is written atomically. Thus, if your data pages are larger than the file system data page, you might risk partially-written pages in the case of system failures.

[4] xDB databases are structured like UNIX file systems; all documents are contained in libraries, which in turn belong to other libraries, and the complete database is descendant of a root library.

[5] These benchmarks, as all benchmarks, should be taken with a grain of salt. We do not claim that these benchmarks hold much scientific significance or represent performance in your particular XML application. Readers should also be aware that there is always bias if an implementer tests his own implementation. However the results should give a rough impression of the overhead associated with this DOM implementation model.


F Peng, S S Chawathe: XSQ: A Streaming XPath Engine. ACM Trans. Database Syst (2005). doi:


Daniela Florescu, Chris Hillery, Donald Kossmann, Paul Lucas, Fabio Riccardi, Till Westmann, Michael J. Carey, Arvind Sundararajan, Geetika Agrawal: The BEA Streaming XQuery Processor. The VLDB Journal (2003)


Streaming Transformations for XML. Homepage, on the web at


Michael Kay: XSL Transformations (XSLT) 2.1. World Wide Web Consortium Working Draft (2010), on the web at


P. O’Neil, E. O’Neil, S. Pal, I. Cseri, G. Schaller, and N. Westbury. ORDPATH: Insert-Friendly XML Node Labels. SIGMOD, 2004. doi:


Wolfgang Meier: eXist: An Open Source Native XML Database. NODe Workshop (2002)


Manegold, S., Boncz, P.A., and Kersten, M.L: Optimizing database architecture for the new bottleneck: memory access. VLDB Journal (2000)


T. M. Chilimbi, B. Davidson J. R. Larus: Cache-conscious structure layout. ACM Sigplan Notices (1999). doi:


I. Tatarinov, S. D. Viglas, K. Beyer, J. Shanmugasundaram, E. Shekita, C. Zhang: Storing and Querying Ordered XML Using a Relational Database System. SIGMOD (2002). doi:


J. van Rotterdam: EMC Documentum xDB 9.0 Scalability Tests. Whitepaper, on the web at