I have a some large data structure (N > 10,000) that usually only needs to be created once (at runtime), and can be reused many times afterwards, but it needs to be loaded very quickly. (It is used for user input processing on iPhoneOS.) mmap-ing a file seems to be the best choice.
Are there any data structure libraries for C++ (or C)? Something along the line
ReadOnlyHashTable<char, int> table ("filename.hash");
// mmap(...) inside the c'tor
...
int freq = table.get('a');
...
// munmap(...); inside the d'tor.
Thank you!
Details:
I've written a similar class for hash table myself but I find it pretty hard to maintain, so I would like to see if there's existing solutions already. The library should
Contain a creation routine that serialize the data structure into file. This part doesn't need to be fast.
Contain a loading routine that mmap a file into read-only (or read-write) data structure that can be usable within O(1) steps of processing.
Use O(N) amount of disk/memory space with a small constant factor. (The device has serious memory constraint.)
Small time overhead to accessors. (i.e. the complexity isn't modified.)
Assumptions:
Bit representation of data (e.g. endianness, encoding of float, etc.) does not matter since it is only used locally.
So far the possible types of data I need are integers, strings, and struct's of them. Pointers do not appear.
P.S. Can Boost.intrusive help?
You could try to create a memory mapped file and then create the STL map structure with a customer allocator. Your customer allocator then simply takes the beginning of the memory of the memory mapped file, and then increments its pointer according to the requested size.
In the end all the allocated memory should be within the memory of the memory mapped file and should be reloadable later.
You will have to check if memory is free'd by the STL map. If it is, your customer allocator will lose some memory of the memory mapped file but if this is limited you can probably live with it.
Sounds like maybe you could use one of the "perfect hash" utilities out there. These spend some time opimising the hash function for the particular data, so there are no hash collisions and (for minimal perfect hash functions) so that there are no (or at least few) empty gaps in the hash table. Obviously, this is intended to be generated rarely but used frequently.
CMPH claims to cope with large numbers of keys. However, I have never used it.
There's a good chance it only generates the hash function, leaving you to use that to generate the data structure. That shouldn't be especially hard, but it possibly still leaves you where you are now - maintaining at least some of the code yourself.
Just thought of another option - Datadraw. Again, I haven't used this, so no guarantees, but it does claim to be a fast persistent database code generator.
WRT boost.intrusive, I've just been having a look. It's interesting. And annoying, as it makes one of my own libraries look a bit pointless.
I thought this section looked particularly relevant.
If you can use "smart pointers" for links, presumably the smart pointer type can be implemented using a simple offset-from-base-address integer (and I think that's the point of the example). An array subscript might be equally valid.
There's certainly unordered set/multiset support (C++ code for hash tables).
Using cmph would work. It does have the serialization machinery for the hash function itself, but you still need to serialize the keys and the data, besides adding a layer of collision resolution on top of it if your query set universe is not known before hand. If you know all keys before hand, then it is the way to go since you don't need to store the keys and will save a lot of space. If not, for such a small set, I would say it is overkill.
Probably the best option is to use google's sparse_hash_map. It has very low overhead and also has the serialization hooks that you need.
http://google-sparsehash.googlecode.com/svn/trunk/doc/sparse_hash_map.html#io
GVDB (GVariant Database), the core of Dconf is exactly this.
See git.gnome.org/browse/gvdb, dconf and bv
and developer.gnome.org/glib/2.30/glib-GVariant.html
Related
As part of my test code I need to build complex structure that uses among other stuff 2 std::map instances; both of them have around 1 million elements. In optimized builds it's OK, however, in debug un-optimized builds it takes almost a minute. I use the same data to build that map, basically if I could save chunk of ram and restore it in 20ms then I'd effectively get the same map in my app without waiting a minute every time. What can i do to speed it up? I could try to use custom allocator and save/restore its alloacted storage, or is there a way to construct std::map from data that's already sorted perhaps so that it would be linear in time?
The technical difficulty, is that for std::map in debug mode, the Visual studio compiler inserts checks for correctness, and in some revisions, has inserted elements into the structure to ensure checking is easier.
There are 2 possible solutions :-
Abstraction
If the information provided by the std::map is replaceable by an interface class, then the internals of the std::map can be hidden and moved into a separate compilation unit. This can be compiled outside of the debug environment and performance restored.
alternative data structure
For a piece of information which is broadly static (e.g. a static piece of data you need to retrieve quickly, then std::map is not the fastest way to achieve this, and a sorted std::vector of std::pair<key,value> would be more performant in operation.
The advantage of the std::vector, is there are guarantees about its layout. If the data is Plain-old-data, then it can be loaded by a std::vector::reserve and a memcpy. Otherwise, filling the elements in the std::vector would still avoid the significant time spent by Visual Studio tracking the memory and structure of the std::map for issues.
Eventually after trying different approaches I ended up using custom allocator.
std::map was one of many containers that's used by my struct to hold the data. Total size of allocated ram was actually around 400MB, the struct contained lists, maps, vectors of different data where many of the members of these containers were pointers into other containers. So, I took radical approach and made it extremely fast to 'resurrect' my entire structure with all the maps and internal pointers. While originally in my post it was about making it 'fast' in debug builds quickly after modifying code and adding extra complexity it became equally applicable to release builds as well: construction time became around 10 seconds in release build.
So, at first I modified all structure members to use my custom allocator, this way I saw how much ram was actually allocated:
total allocated: 1970339320 bytes, total freed: 1437565512 bytes
This way I could estimate that I'll need around 600MB total. Then, in my custom allocator I added static global method my_aloc::start_recording() this method would allocate that 600MB chunk of ram and after start_recording was called my custom allocator would simply return addresses from that 600MB block. After start_recording was called I'd make a copy of my entire structure (it was actually a vector of structures). When making a copy there is no overallocation, each structure member allocated only enough ram for its storage. Basically by copying structs it actually allocated only around 400MB instead of 600MB.
I said that internally in my construct there is lots of pointers to internal members, how to reuse then this 400MB recorded "snapshot" from my custom allocator? I could write code to "patch" pointers, but perhaps it wouldn't even work: I had lots of maps that also use pointers as key with custom compare struct that dereferences pointers to compare actual pointer to values. Also, some maps contained iterators into lists, it would be quite messy to deal with all that. Also, my overall structure isn't set in stone, it's work in progress and if something gets changed then patching code would also need to be changed. So, the answer was quit obvious: I simply needed to load that entire 400MB snapshot at the same base address. In windows I use VirtualAlloc, in linux something like mmap perhaps would need to be used, or alternatively boost shared mem lib could be used to make it more portable. At the end overall load time went down to 150ms, while in release I takes more than 10 seconds, and in debug builds it's already perhaps somewhere in minutes now.
I am developing a set of vector classes that all derived from an abstract vector. I am doing this so that in our software that makes use of these vectors, we can quickly switch between the vectors without any code breaking (or at least minimize failures, but my goal is full compatibility). All of the vectors match.
I am working on a Disk Based Vector that mostly conforms to match the STL Vector implementation. I am doing this because we need to handle large out of memory files that contain various formats of data. The Disk Vector handles data read/write to disk by using template specialization/polymorphism of serialization and deserialization classes. The data serialization and deserialization has been tested, and it works (up to now). My problem occurs when dealing with references to the data.
For example,
Given a DiskVector dv, a call to dv[10] would get a point to a spot on disk, then seek there, read out the char stream. This stream gets passed to a deserializor which converts the byte stream into the appropriate data type. Once I have the value, I my return it.
This is where I run into a problem. In the STL, they return it as a reference, so in order to match their style, I need to return a reference. What I do it store the value in an unordered_map with the given index (in this example, 10). Then I return a reference to the value in the unordered_map.
If this continues without cleanup, then the purpose of the DiskVector is lost because all the data just gets loaded into memory, which is bad due to data size. So I clean up this map by deleting the indexes later on when other calls are made. Unfortunately, if a user decided to store this reference for a long time, and then it gets deleted in the DiskVector, we have a problem.
So my questions
Is there a way to see if any other references to a certain instance are in use?
Is there a better way to solve this while still maintaining the polymorphic style for reasons described at the beginning?
Is it possible to construct a special class that would behave as a reference, but handle the disk IO dynamically so I could just return that instead?
Any other ideas?
So a better solution at what I was trying to do is to use SQLite as the backend for the database. Use BLOBs as the column types for key and value columns. This is the approach I am taking now. That said, in order to get it to work well, I need to use what cdhowie posted in the comments to my question.
I have a program, that uses dynamic programming to calculate some information. The problem is, that theoretically the used memory grows exponentially. Some filters that I use limit this space, but for a big input they also can't avoid that my program runs out of RAM - Memory.
The program is running on 4 threads. When I run it with a really big input I noticed, that at some point the program starts to use the swap memory, because my RAM is not big enough. The consequence of this is, that my CPU-usage decreases from about 380% to 15% or lower.
There is only one variable that uses the memory which is the following datastructure:
Edit (added type) with CLN library:
class My_Map {
typedef std::pair<double,short> key;
typedef cln::cl_I value;
public:
tbb::concurrent_hash_map<key,value>* map;
My_Map() { map = new tbb::concurrent_hash_map<myType>(); }
~My_Map() { delete map; }
//some functions for operations on the map
};
In my main program I am using this datastructure as globale variable:
My_Map* container = new My_Map();
Question:
Is there a way to avoid the shifting of memory between SWAP and RAM? I thought pushing all the memory to the Heap would help, but it seems not to. So I don't know if it is possible to maybe fully use the swap memory or something else. Just this shifting of memory cost much time. The CPU usage decreases dramatically.
If you have 1 Gig of RAM and you have a program that uses up 2 Gb RAM, then you're going to have to find somewhere else to store the excess data.. obviously. The default OS way is to swap but the alternative is to manage your own 'swapping' by using a memory-mapped file.
You open a file and allocate a virtual memory block in it, then you bring pages of the file into RAM to work on. The OS manages this for you for the most part, but you should think about your memory usage so not to try to keep access to the same blocks while they're in memory if you can.
On Windows you use CreateFileMapping(), on Linux you use mmap(), on Mac you use mmap().
The OS is working properly - it doesn't distinguish between stack and heap when swapping - it pages you whatever you seem not to be using and loads whatever you ask for.
There are a few things you could try:
consider whether myType can be made smaller - e.g. using int8_t or even width-appropriate bitfields instead of int, using pointers to pooled strings instead of worst-case-length character arrays, use offsets into arrays where they're smaller than pointers etc.. If you show us the type maybe we can suggest things.
think about your paging - if you have many objects on one memory page (likely 4k) they will need to stay in memory if any one of them is being used, so try to get objects that will be used around the same time onto the same memory page - this may involve hashing to small arrays of related myType objects, or even moving all your data into a packed array if possible (binary searching can be pretty quick anyway). Naively used hash tables tend to flay memory because similar objects are put in completely unrelated buckets.
serialisation/deserialisation with compression is a possibility: instead of letting the OS swap out full myType memory, you may be able to proactively serialise them into a more compact form then deserialise them only when needed
consider whether you need to process all the data simultaneously... if you can batch up the work in such a way that you get all "group A" out of the way using less memory then you can move on to "group B"
UPDATE now you've posted your actual data types...
Sadly, using short might not help much because sizeof key needs to be 16 anyway for alignment of the double; if you don't need the precision, you could consider float? Another option would be to create an array of separate maps...
tbb::concurrent_hash_map<double,value> map[65536];
You can then index to map[my_short][my_double]. It could be better or worse, but is easy to try so you might as well benchmark....
For cl_I a 2-minute dig suggests the data's stored in a union - presumably word is used for small values and one of the pointers when necessary... that looks like a pretty good design - hard to improve on.
If numbers tend to repeat a lot (a big if) you could experiment with e.g. keeping a registry of big cl_Is with a bi-directional mapping to packed integer ids which you'd store in My_Map::map - fussy though. To explain, say you get 987123498723489 - you push_back it on a vector<cl_I>, then in a hash_map<cl_I, int> set [987123498723489 to that index (i.e. vector.size() - 1). Keep going as new numbers are encountered. You can always map from an int id back to a cl_I using direct indexing in the vector, and the other way is an O(1) amortised hash table lookup.
I do embedded software, but this isn't really an embedded question, I guess. I don't (can't for technical reasons) use a database like MySQL, just C or C++ structs.
Is there a generic philosophy of how to handle changes in the layout of these structs from version to version of the program?
Let's take an address book. From program version x to x+1, what if:
a field is deleted (seems simple enough) or added (ok if all can use some new default)?
a string gets longer or shorter? An int goes from 8 to 16 bits of signed / unsigned?
maybe I combine surname/forename, or split name into two fields?
These are just some simple examples; I am not looking for answers to those, but rather for a generic solution.
Obviously I need some hard coded logic to take care of each change.
What if someone doesn't upgrade from version x to x+1, but waits for x+2? Should I try to combine the changes, or just apply x -> x+ 1 followed by x+1 -> x+2?
What if version x+1 is buggy and we need to roll-back to a previous version of the s/w, but have already "upgraded" the data structures?
I am leaning towards TLV (http://en.wikipedia.org/wiki/Type-length-value) but can see a lot of potential headaches.
This is nothing new, so I just wondered how others do it....
I do have some code where a longer string is puzzled together from two shorter segments if necessary. Yuck. Here's my experience after 12 years of keeping some data compatible:
Define your goals - there are two:
new versions should be able to read what old versions write
old versions should be able to read what new versions write (harder)
Add version support to release 0 - At least write a version header. Together with keeping (potentially a lot of) old reader code around that can solve the first case primitively. If you don't want to implement case 2, start rejecting new data right now!
If you need only case 1, and and the expected changes over time are rather minor, you are set. Anyway, these two things done before the first release can save you many headaches later.
Convert during serialization - at run time, only keep the data in the "new format" in memory. Do necessary conversions and tests at persistence limits (convert to newest when reading, implement backward compatibility when writing). This isolates version problems in one place, helping to avoid hard-to-track-down bugs.
Keep a set of test data from all versions around.
Store a subset of available types - limit the actually serialized data to a few data types, such as int, string, double. In most cases, the extra storage size is made up by reduced code size supporting changes in these types. (That's not always a tradeoff you can make on an embedded system, though).
e.g. don't store integers shorter than the native width. (you might need to do that when you need to store long integer arrays).
add a breaker - store some key that allows you to intentionally make old code display an error message that this new data is incompatible. You can use a string that is part of the error message - then your old version could display an error message it doesn't know about - "you can import this data using the ConvertX tool from our web site" is not great in a localized application but still better than "Ungültiges Format".
Don't serialize structs directly - that's the logical / physical separation. We work with a mix of two, both having their pros and cons. None of these can be implemented without some runtime overhead, which can pretty much limit your choices in an embedded environment. At any rate, don't use fixed array/string lengths during persistence, that should already solve half of your troubles.
(A) a proper serialization mechanism - we use a bianry serializer that allows to start a "chunk" when storing, which has its own length header. When reading, extra data is skipped and missing data is default-initialized (which simplifies implementing "read old data" a lot in the serializationj code.) Chunks can be nested. That's all you need on the physical side, but needs some sugar-coating for common tasks.
(B) use a different in-memory representation - the in-memory reprentation could basically be a map<id, record> where id woukld likely be an integer, and record could be
empty (not stored)
a primitive type (string, integer, double - the less you use the easier it gets)
an array of primitive types
and array of records
I initially wrote that so the guys don't ask me for every format compatibility question, and while the implementation has many shortcomings (I wish I'd recognize the problem with the clarity of today...) it could solve
Querying a non existing value will by default return a default/zero initialized value. when you keep that in mind when accessing the data and when adding new data this helps a lot: Imagine version 1 would calculate "foo length" automatically, whereas in version 2 the user can overrride that setting. A value of zero - in the "calculation type" or "length" should mean "calculate automatically", and you are set.
The following are "change" scenarios you can expect:
a flag (yes/no) is extended to an enum ("yes/no/auto")
a setting splits up into two settings (e.g. "add border" could be split into "add border on even days" / "add border on odd days".)
a setting is added, overriding (or worse, extending) an existing setting.
For implementing case 2, you also need to consider:
no value may ever be remvoed or replaced by another one. (But in the new format, it could say "not supported", and a new item is added)
an enum may contain unknown values, other changes of valid range
phew. that was a lot. But it's not as complicated as it seems.
There's a huge concept that the relational database people use.
It's called breaking the architecture into "Logical" and "Physical" layers.
Your structs are both a logical and a physical layer mashed together into a hard-to-change thing.
You want your program to depend on a logical layer. You want your logical layer to -- in turn -- map to physical storage. That allows you to make changes without breaking things.
You don't need to reinvent SQL to accomplish this.
If your data lives entirely in memory, then think about this. Divorce the physical file representation from the in-memory representation. Write the data in some "generic", flexible, easy-to-parse format (like JSON or YAML). This allows you to read in a generic format and build your highly version-specific in-memory structures.
If your data is synchronized onto a filesystem, you have more work to do. Again, look at the RDBMS design idea.
Don't code a simple brainless struct. Create a "record" which maps field names to field values. It's a linked list of name-value pairs. This is easily extensible to add new fields or change the data type of the value.
Some simple guidelines if you're talking about a structure use as in a C API:
have a structure size field at the start of the struct - this way code using the struct can always ensure they're dealing only with valid data (for example, many of the structures the Windows API uses start with a cbCount field so these APIs can handle calls made by code compiled against old SDKs or even newer SDKs that had added fields
Never remove a field. If you don't need to use it anymore, that's one thing, but to keep things sane for dealing with code that uses an older version of the structure, don't remove the field.
it may be wise to include a version number field, but often the count field can be used for that purpose.
Here's an example - I have a bootloader that looks for a structure at a fixed offset in a program image for information about that image that may have been flashed into the device.
The loader has been revised, and it supports additional items in the struct for some enhancements. However, an older program image might be flashed, and that older image uses the old struct format. Since the rules above were followed from the start, the newer loader is fully able to deal with that. That's the easy part.
And if the struct is revised further and a new image uses the new struct format on a device with an older loader, that loader will be able to deal with it, too - it just won't do anything with the enhancements. But since no fields have been (or will be) removed, the older loader will be able to do whatever it was designed to do and do it with the newer image that has a configuration structure with newer information.
If you're talking about an actual database that has metadata about the fields, etc., then these guidelines don't really apply.
What you're looking for is forward-compatible data structures. There are several ways to do this. Here is the low-level approach.
struct address_book
{
unsigned int length; // total length of this struct in bytes
char items[0];
}
where 'items' is a variable length array of a structure that describes its own size and type
struct item
{
unsigned int size; // how long data[] is
unsigned int id; // first name, phone number, picture, ...
unsigned int type; // string, integer, jpeg, ...
char data[0];
}
In your code, you iterate through these items (address_book->length will tell you when you've hit the end) with some intelligent casting. If you hit an item whose ID you don't know or whose type you don't know how to handle, you just skip it by jumping over that data (from item->size) and continue on to the next one. That way, if someone invents a new data field in the next version or deletes one, your code is able to handle it. Your code should be able to handle conversions that make sense (if employee ID went from integer to string, it should probably handle it as a string), but you'll find that those cases are pretty rare and can often be handled with common code.
I have handled this in the past, in systems with very limited resources, by doing the translation on the PC as a part of the s/w upgrade process. Can you extract the old values, translate to the new values and then update the in-place db?
For a simplified embedded db I usually don't reference any structs directly, but do put a very light weight API around any parameters. This does allow for you to change the physical structure below the API without impacting the higher level application.
Lately I'm using bencoded data. It's the format that bittorrent uses. Simple, you can easily inspect it visually, so it's easier to debug than binary data and is tightly packed. I borrowed some code from the high quality C++ libtorrent. For your problem it's so simple as checking that the field exist when you read them back. And, for a gzip compressed file it's so simple as doing:
ogzstream os(meta_path_new.c_str(), ios_base::out | ios_base::trunc);
Bencode map(Bencode::TYPE_MAP);
map.insert_key("url", url.get());
map.insert_key("http", http_code);
os << map;
os.close();
To read it back:
igzstream is(metaf, ios_base::in | ios_base::binary);
is.exceptions(ios::eofbit | ios::failbit | ios::badbit);
try {
torrent::Bencode b;
is >> b;
if( b.has_key("url") )
d->url = b["url"].as_string();
} catch(...) {
}
I have used Sun's XDR format in the past, but I prefer this now. Also it's much easier to read with other languages such as perl, python, etc.
Embed a version number in the struct or, do as Win32 does and use a size parameter.
if the passed struct is not the latest version then fix up the struct.
About 10 years ago I wrote a similar system to the above for a computer game save game system. I actually stored the class data in a seperate class description file and if i spotted a version number mismatch then I coul run through the class description file, locate the class and then upgrade the binary class based on the description. This, obviously required default values to be filled in on new class member entries. It worked really well and it could be used to auto generate .h and .cpp files as well.
I agree with S.Lott in that the best solution is to separate the physical and logical layers of what you are trying to do. You are essentially combining your interface and your implementation into one object/struct, and in doing so you are missing out on some of the power of abstraction.
However if you must use a single struct for this, there are a few things you can do to help make things easier.
1) Some sort of version number field is practically required. If your structure is changing, you will need an easy way to look at it and know how to interpret it. Along these same lines, it is sometimes useful to have the total length of the struct stored in a structure field somewhere.
2) If you want to retain backwards compatibility, you will want to remember that code will internally reference structure fields as offsets from the structure's base address (from the "front" of the structure). If you want to avoid breaking old code, make sure to add all new fields to the back of the structure and leave all existing fields intact (even if you don't use them). That way, old code will be able to access the structure (but will be oblivious to the extra data at the end) and new code will have access to all of the data.
3) Since your structure may be changing sizes, don't rely on sizeof(struct myStruct) to always return accurate results. If you follow #2 above, then you can see that you must assume that a structure may grow larger in the future. Calls to sizeof() are calculated once (at compile time). Using a "structure length" field allows you to make sure that when you (for example) memcpy the struct you are copying the entire structure, including any extra fields at the end that you aren't aware of.
4) Never delete or shrink fields; if you don't need them, leave them blank. Don't change the size of an existing field; if you need more space, create a new field as a "long version" of the old field. This can lead to data duplication problems, so make sure to give your structure a lot of thought and try to plan fields so that they will be large enough to accommodate growth.
5) Don't store strings in the struct unless you know that it is safe to limit them to some fixed length. Instead, store only a pointer or array index and create a string storage object to hold the variable-length string data. This also helps protect against a string buffer overflow overwriting the rest of your structure's data.
Several embedded projects I have worked on have used this method to modify structures without breaking backwards/forwards compatibility. It works, but it is far from the most efficient method. Before long, you end up wasting space with obsolete/abandoned structure fields, duplicate data, data that is stored piecemeal (first word here, second word over there), etc etc. If you are forced to work within an existing framework then this might work for you. However, abstracting away your physical data representation using an interface will be much more powerful/flexible and less frustrating (if you have the design freedom to use such a technique).
You may want to take a look at how Boost Serialization library deals with that issue.
How can I store a hash table with separate chaining in a file on disk?
Generating the data stored in the hash table at runtime is expensive, it would be faster to just load the HT from disk...if only I can figure out how to do it.
Edit:
The lookups are done with the HT loaded in memory. I need to find a way to store the hashtable (in memory) to a file in some binary format. So that next time when the program runs it can just load the HT off disk into RAM.
I am using C++.
What language are you using? The common method is to do some sort binary serialization.
Ok, I see you have edited to add the language. For C++ there a few options. I believe the Boost serialization mechanism is pretty good. In addition, the page for Boost's serialization library also describes alternatives. Here is the link:
http://www.boost.org/doc/libs/1_37_0/libs/serialization/doc/index.html
Assuming C/C++: Use array indexes and fixed size structs instead of pointers and variable length allocations. You should be able to directly write() the data structures to file for later read()ing.
For anything higher-level: A lot of higher language APIs have serialization facilities. Java and Qt/C++ both have methods that sprint immediately to mind, so I know others do as well.
You could just write the entire data structure directly to disk by using serialization (e.g. in Java). However, you might be forced to read the entire object back into memory in order to access its elements. If this is not practical, then you could consider using a random access file to store the elements of the hash table. Instead of using a pointer to represent the next element in the chain, you would just use the byte position in the file.
Ditch the pointers for indices.
This is a bit similar to constructing an on-disk DAWG, which I did a while back. What made that so very sweet was that it could be loaded directly with mmap instead reading the file. If the hash-space is manageable, say 216 or 224 entries, then I think I would do something like this:
Keep a list of free indices. (if the table is empty, each chain-index would point at the next index.)
When chaining is needed use the free space in the table.
If you need to put something in an index that's occupied by a squatter (overflow from elsewhere) :
record the index (let's call it N)
swap the new element and the squatter
put the squatter in a new free index, (F).
follow the chain on the squatter's hash index, to replace N with F.
If you completely run out of free indices, you probably need a bigger table, but you can cope a little longer by using mremap to create extra room after the table.
This should allow you to mmap and use the table directly, without modification. (scary fast if in the OS cache!) but you have to work with indices instead of pointers. It's pretty spooky to have megabytes available in syscall-round-trip-time, and still have it take up less than that in physical memory, because of paging.
Perhaps DBM could be of use to you.
If your hash table implementation is any good, then just store the hash and each object's data - putting an object into the table shouldn't be expensive given the hash, and not serialising the table or chain directly lets you vary the exact implementation between save and load.