Patent Publication Number: US-11036799-B2

Title: Low RAM space, high-throughput persistent key value store using secondary memory

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is a continuation of U.S. patent application Ser. No. 12/908,153, filed Oct. 20, 2010, entitled “LOW RAM SPACE, HIGH-THROUGHPUT PERSISTENT KEY-VALUE STORE USING SECONDARY MEMORY”. The entirety of this afore-mentioned application is incorporated herein by reference. 
    
    
     BACKGROUND 
     Flash media has advantages over RAM and hard disk storage, namely that unlike RAM, flash media is persistent, and unlike hard disk storage, flash media provides much faster data access times, e.g., on the order of hundreds or thousands of times faster than hard disk access. Many applications thus may benefit from the use of flash media. 
     However, flash media is expensive, at present costing ten to twenty times more per gigabyte than hard disk storage. Further, flash devices are subject to reduced lifetimes due to page wearing, whereby small random writes (that also have relatively high latency) are not desirable. What is desirable is a technology for using flash media (or other non-RAM, non-hard disk storage) that provides high performance, while factoring in cost considerations, efficiency and media lifetimes. 
     U.S. patent application Ser. No. 12/773,859, assigned to the assignee of the present invention and herein incorporated by reference, describes a flash-based or other secondary storage memory cache having these desired characteristics, such as for use in storing key-value pairs. However, the amount of RAM used is on the order of six bytes (two signature bytes plus four pointer bytes) for each key-value pair maintained in secondary (e.g., flash) storage. Reducing the amount of RAM used is desirable in many scenarios. 
     SUMMARY 
     This Summary is provided to introduce a selection of representative concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in any way that would limit the scope of the claimed subject matter. 
     Briefly, various aspects of the subject matter described herein are directed towards a technology by which a secondary storage device (e.g., in flash memory) contains buckets of linked record, in which the bucket for a record is determined by mapping the record&#39;s key to a RAM-based index having slots corresponding to pointers to the buckets. There are more possible keys than slots/buckets, resulting in collisions that are handled by searching the bucket in a most-recent to least-recent ordering until a match is found, (if any exists). 
     To maintain the ordering, each record is associated with a pointer to the previous record in its bucket, and so on, forming a linked list of records. When a new record is inserted into the secondary storage, the previous pointer in the RAM-based index is associated with the record to be inserted (e.g., as part of a page write that includes the new record), and the index updated to point to the new record, thereby maintaining the linked list ordering. 
     In one aspect, a counter may be associated with each slot of the index to indicate the size of the chain of records in the bucket that corresponds to that slot. If the size is too large, compaction may be performed to writes a plurality of non-contiguous records of a bucket to a single page of contiguous records. 
     In one aspect, a bloom filter may be associated with each slot to indicate to a high probability whether a key of a record is in the bucket that corresponds to that slot. If not, the bucket need not be searched for that key. 
     In one alternative, a load balancing mechanism may operate to balance chain sizes in the buckets. For example, a key to be inserted may be mapped by multiple mapping functions to multiple candidate slots, with one candidate slot selected based upon the chain sizes of the candidate buckets. The key is then inserted into the bucket that corresponds to the selected slot. A bloom filter may be used to avoid searching both candidate buckets when later looking up that record. 
     In one alternative, the index may comprise a first index component and a second index component. Individual slots in the first index may be configured to indicate that a key mapped to such a slot in the first index by a first mapping function is to be secondarily mapped to a slot in the second index by a second mapping function. This facilitates splitting a slot in the first index into sub-slots, such as if the first index slot corresponds to too many keys. 
     Other advantages may become apparent from the following detailed description when taken in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
         FIG. 1  is a block diagram representing an example architecture and data structures for mapping records to secondary storage using a relatively small-sized RAM footprint. 
         FIG. 2  is a flow diagram representing example steps for looking up a record key (e.g., of a key-value pair), including in secondary storage. 
         FIG. 3  is a flow diagram representing example steps for inserting a keyed record into secondary storage (as part of a page of records). 
         FIG. 4  is a representation of a data structure that maintains counters in association with pointers of an index table. 
         FIG. 5  is a flow diagram representing example steps for compacting a chain of records onto a single page in secondary storage. 
         FIG. 6  is a block diagram representing an example architecture and data structures illustrating compaction and garbage collection. 
         FIG. 7  is a block diagram representing an example architecture and data structures illustrating load balancing. 
         FIG. 8  is a representation of a data structure that maintains bloom filters and counters in association with pointers of an index table. 
         FIG. 9  is a block diagram representing an example architecture and data structures illustrating splitting of an index slot in a first index table into a plurality of slots in a second index table. 
         FIG. 10  shows an illustrative example of a computing environment into which various aspects of the present invention may be incorporated. 
     
    
    
     DETAILED DESCRIPTION 
     Various aspects of the technology described herein are generally directed towards using secondary storage (e.g., flash media storage) in a manner that uses only a relatively very small-sized RAM footprint compared to other technologies, such as for storing key-value pairs. To this end, key-value pairs are mapped by their keys into buckets (of chained records) in sequential secondary storage, with a chain of pointers that relate records of each bucket to the other entries in that bucket. Mapping collisions are thus placed into the same bucket, which is searched to find a matching key. As a result, instead of maintaining pointers in RAM, many of the pointers are maintained in the secondary storage. In one implementation, this results in a RAM footprint on the order of one (plus or minus one-half) byte of RAM per key-value pair. 
     It should be understood that any of the examples herein are non-limiting. Indeed, the technology described herein applies to any type of non-volatile storage that is faster than disk access, not only the flash media exemplified and referred to as an example herein. Thus, as used herein, “flash” is only an example that does not necessarily mean conventional flash media, but in general refers to any secondary storage (with conventional RAM considered primary storage). Moreover, the data structures described herein are only examples; indeed, the storage of any keyed records, not necessarily key-value pairs, may benefit from the technology described herein. As such, the present invention is not limited to any particular embodiments, aspects, concepts, structures, functionalities or examples described herein. Rather, any of the embodiments, aspects, concepts, structures, functionalities or examples described herein are non-limiting, and the present invention may be used in various ways that provide benefits and advantages in computing and data storage and retrieval in general. 
       FIG. 1  shows example architectural components of one implementation of a key-value store maintained using relatively very fast RAM  102  and non-volatile secondary storage  104  (e.g., relatively fast compared to hard disk storage), which may be configured in flash media arranged as pages. In one implementation, the secondary storage  104 /flash store is basically arranged as a sequential log/file. More particularly, the flash store provides persistent storage for the keyed records (e.g., key-value pairs), and in one implementation is organized as a circular append log. Key-value pairs are written to flash in units of a page size to the tail of the log. When the log accumulates garbage (comprising deleted records, older values of updated records and/or orphaned records, described below) beyond a configurable threshold, the pages on flash from the head of the log are recycled such that valid entries from the head of the log are written back to the end of the log. Also described below is compacting the records in a given bucket into a single page on flash, which improves read performance, as described below. 
     As generally as described in the aforementioned U.S. patent application Ser. No. 12/773,859, the RAM  102  includes a write buffer  106  comprising an (e.g., fixed-size) data structure that buffers key-value writes so that a write to flash happens only after there is enough data to fill a flash page (which is typically 2 KB or 4 KB in size). As also described therein, writes also may occur upon a configurable timeout interval being reached. 
     The RAM  102  also includes an index data structure (directory), exemplified as an index table  108 , which has pointers (each being a combination of a page pointer and a page offset) to key-value pairs stored on the secondary storage  104 . A key mapping mechanism/function  110 , such as comprising a hash function or range-mapping function, maps each key of key-value pairs  1121 - 112   m  to one of N slots (each slot corresponding to a bucket) in the index table  108 , where N may be configurable for a given implementation thereby controlling the amount of RAM used for indexing. In other words, the hash function or range mapping function maps any key value to one slot of a plurality thereof, in which the number depends on the mapping function, and is thus configurable. 
     Each slot contains a pointer to a chain (linked list) of one or more records in the secondary storage  104 . The records thus include a pointer field that forms the chain. More particularly, in addition to the key and value fields, each key-value pair record in the secondary storage  104  contains a pointer to the next record (in the order in its respective chain), that is, the previous record (key-value pair) in the sequential log that was mapped to the same slot as the new key-value pair. The chain of records on flash pointed to by each slot thus comprises the bucket of records corresponding to that slot in the index table  108 . 
     Thus, as is understood, mapping collisions are resolved by linear chaining with the chains stored in flash. The average number of records in a bucket, k, is a generally configurable parameter. More particularly, because the chain of key-value pairs in each bucket is typically stored non-contiguously on flash, multiple flash reads may be incurred upon lookup of a key in the store. Lookup time versus RAM space overhead per key-value pair is a tradeoff. The average number of keys in a bucket k is a configurable parameter that allows controlling the tradeoff between RAM space usage versus lookup times. 
     By way of further explanation, consider the following sequence of accesses in key lookup and insert operations performed by a client application, as generally summarized in the flow diagrams of  FIGS. 2 and 3 , respectively. As represented by step  202  of  FIG. 2 , a key lookup (get) operation first looks for a key in the RAM write buffer  106 . Upon a miss (step  204 ), the operation moves to step  206  to look up the key in the index table  108  to find a pointer to the location in secondary storage  104  of the most recent record written to that bucket. For example, a lookup operation on a key uses the hash mapping or range mapping function to determine the index table slot to which the given key belongs, with the pointer in that slot identifying the starting address in secondary storage of that key&#39;s bucket. 
     Beginning with the pointer stored in that slot, the lookup operation follows the chain of records on flash to search for the key, e.g., by reading the appropriate page and searching that page for the matching key. If that page does not contain the matching key, step  212  repeats the process using the pointer in the key&#39;s record at that address; (note that a new page only need be read in if the pointer is to a new page). Upon finding the first record in the chain whose key matches the search key, the lookup operation returns the value associated with that key, as represented by step  214 ; (if there is no matching key, null is returned via step  216 ). The number of flash reads for such a lookup is k/2 on the average, and in the worst case is the size k of the bucket chain. 
     As generally represented in  FIG. 3 , a key insert (or, update/set) operation writes (step  302 ) the key-value pair into the RAM write buffer  106 . As represented by step  304 , when there are enough key-value pairs in the RAM write buffer  106  to fill a flash page (or, a configurable timeout interval since the client call has expired at step  306 , such as on the order of one millisecond), the buffered entries are written to the secondary storage  104 . To this end, at step  308 , for each key-value pair to be written to the secondary storage  104 , the pointer that was previously in that key&#39;s slot is also written to the secondary storage  104  at step  310 , e.g., as a &lt;Key, Value, PTR&gt; record in the page. At step  312 , the pointer in the index table slot is changed to point to the address of that newly written key-value pair. Note that in building the page, ordering is kept so that the index table points to the most-recently inserted record, and that record points to the next most recent, and so on. 
     To summarize, for a given key, the insert operation uses the mapping function to determine the index table slot to which that key belongs. For example, if a 1  represents the address on flash of the first record in this chain (that is, what the pointer in the slot currently points to), then a record is created in flash that corresponds to the inserted (or, updated) key-value pair with its next-pointer field equal to a 1 . This record is appended to the log on flash at its own flash address a 2 ; and the a 2  address on flash placed as the value of the pointer in the respective slot in RAM. Effectively, this newly written record is inserted at the beginning of the chain corresponding to this bucket. 
     Note that if the insert operation corresponds to an update operation on an earlier-inserted key, the most recent value of the key will be (correctly) read during a lookup operation because it will be reached by following the chain, which links from most recent to older written records. Any older key will not be reached before the more recent one, because the old value is further down the chain; the older key will be cleaned up as garbage in the log, as described below. 
     A delete operation on a key is supported through insertion of a null value for that key. Eventually the null entries and any earlier-inserted values of a key on flash are garbage collected. In one implementation, when flash usage and/or the fraction of garbage records in the flash log exceed a certain threshold, a garbage collection (and compaction) operation is initiated to reclaim storage on flash. 
     Turning to maintenance operations, namely compaction and garbage collection, as will be understood, compaction is useful in improving lookup latencies by reducing the number of flash reads when searching a bucket. Garbage collection reclaims storage on flash, and is a consequence of flash being used in a log-structured manner. 
     With respect to compaction, as described above, a lookup operation involves following the chain of key-value records in a bucket on flash. For a chain length of c records in a bucket, this involves an average of c/2 flash reads. Over time, as keys are inserted into a bucket and earlier inserted keys are updated, the chain length for this bucket keeps increasing, which degrades lookup times. This situation may be addressed by periodically compacting the chain on flash in a bucket, which in general places the valid keys in that chain (those that were not deleted or updated with a new value) contiguously on one or more flash pages that are appended to the tail of the log. Thus, if m key-value pairs can be packed onto a single flash page (on the average), the number of flash reads required to search for a key in a bucket of k records is k/(2 m) on the average and at most [k/m] in the worst case. Note that key-value pairs that have been compacted remain in their previous location on flash until garbage collected; these are referred to as orphaned key-value pairs. 
     In order to determine whether to compact a bucket of chained key-value pairs, rather than following each chain and counting its length, a counter (Ctr) may be maintained in the index table for each slot corresponding to that bucket. This is generally represented in the index table  408  of  FIG. 4  (although other arrangements to associate the data may be alternatively employed) by counters Ctr 1 -CtrN associated with pointers Ptr 1 -PtrN for N slots. In general, the counter may be one byte in length and is incremented to match the chain size of non-compacted records, up to its limit. 
     The compaction operations may proceed over time, such as per slot/bucket as shown in  FIG. 5 , e.g., via a compaction mechanism  660  ( FIG. 6 ), (which may be integrated with a garbage collection mechanism described below). Initially, as key-value pairs are added at different times to a bucket, they appear on different flash pages and are chained together individually on flash. When enough valid records accumulate in a bucket to fill a flash page (e.g., m of them as evaluated at step  502 ), such as tracked by the slot&#39;s counter, the records are compacted and appended onto a new flash page, such as at the tail of the log. To this end, the valid records of the chain are found via steps  504 ,  506  and  510 , and added to a compaction buffer  606  ( FIG. 6 ) in RAM at step  508 . When the records are buffered, the compaction buffer  606  is written to a flash page and the index updated at the slot to point to that page (step  512 ). The chain now comprises a single flash page size and requires one flash read (instead of approximately m reads) to fully search. 
     Thereafter, as further records  661  and  662  are appended to a bucket as generally represented in  FIG. 6 , they are chained together individually and are searched before the compacted group of records in the chain, that is, before the flash page  664  (shown shaded in  FIG. 6 ). Thus, once compacted, at any given time, the chain on flash for each bucket may begin with a chained sequence of one or more individual records  661 ,  662  followed by groups of compacted records, with each group appearing on the same flash page (e.g., the page  664 ). 
     Over time, enough new records may accumulate in the bucket to allow them to be compacted to a second flash page, and so on. To this end, the compaction process repeats, whereby the chain now comprises two compacted flash pages, and so on. Because an insert operation may make a record on a compacted page invalid, previously compacted pages may be re-compacted. 
     Note that when a key-value pair size is relatively small, e.g., 64 bytes, there may not be enough records in a bucket to fill a flash page, because this number is (roughly) upper bounded by the parameter k. In this case, the benefits of compaction may be obtained by applying the procedure to groups of chains in multiple buckets at a time. 
     Because compaction copies records to the end of the log (or optionally to another log) and accordingly adjusts the pointers, the original records are not deleted, however there are no longer any pointers that point to them. These no-longer-pointed-to records are referred to as orphaned records (a type of garbage) in the flash log. As mentioned above, other garbage records also accumulate in the log as a result of key update and delete operations, that is, invalid records are present. These orphaned and invalid records are garbage collected. 
     In one implementation, the garbage collection operation starts scanning key-value pairs from the (current) head of the log, and skips over garbage (invalid or orphaned) key-value pair records while copying valid key-value pair records from the head to the tail of the log, including adjusting pointers as appropriate for the new location. Once the valid records of a page have been copied, that page can be reused when later needed in the circular log. Garbage collection may stop when floor thresholds are reached for flash usage and/or fraction of garbage records remaining in the flash log. 
     More particularly, when a certain configurable fraction of garbage accumulates in the log (in terms of space occupied), a cleaning operation is performed to clean and compact the log. The cleaning operation considers currently used flash pages in oldest first order and deallocates them. On each page, the sequence of key-value pairs is scanned to determine whether they are valid or not. The classification of a key-value pair record on flash follows from doing a lookup on the respective key starting from the index table (if this record is the same as that returned by the lookup, then it is valid). If it appears later in the chain than a valid record for that key, then this record is invalid and corresponds to an obsolete version of the key; otherwise, the record is orphaned and cannot be reached by following pointers from the index table (this may happen because of the compaction procedure, for example). When an orphaned record is encountered at the head of the log, it is skipped and the head position of the log is advanced to the next record. As described above with reference to the insertion operation, the first record in each bucket chain (the one pointed to from the index table slot) is the most recently inserted record, while the last record in the chain is the earliest inserted record in that bucket. Thus, the last record in a bucket chain is encountered first during the garbage collection process and it may be a valid or invalid (obsolete version of the respective key) record. A valid record needs to be reinserted at the tail of the log, while an invalid record can be skipped. In either situation, the next pointer in its predecessor record in the chain needs to be updated. To avoid in-place updates (random writes) on flash, this requires relocating the predecessor record and so forth all the way to the first record in the chain. 
     In one implementation, entire bucket chains on flash are garbage collected at a time. When the last record in a bucket chain is encountered in the log during garbage collection, all valid records in that chain are compacted and relocated to the tail of the log. In other words, when the garbage collector is invoked, scanning starts from the (current) head of the log and skips over orphaned records until it encounters the first valid or invalid record (that is part of some bucket chain). Then, the garbage collector collects that entire bucket chain, compacts and writes the valid records in that chain to the tail of the log, and returns. 
     This garbage collection strategy provides a benefit in that the writing of an entire chain of records in a bucket to the tail of the log also allows them to be compacted and placed contiguously on one or more flash pages, and helps to speed up the lookup operations on those keys. Another benefit is that because garbage (orphaned) records are created further down the log between the (current) head and tail (corresponding to the locations of all records in the chain before relocation), the garbage collection process is sped up for the respective pages when they are encountered later, since orphaned records can be simply discarded. 
     Note that in one implementation, the client key lookup/insert operations, writing key-value pairs to flash store and updating the RAM index operations, and reclaiming space on flash pages operations, are each handled by separate threads in a multi-threaded architecture. 
     Turning to an alternative aspect generally represented in  FIG. 7 , load balancing may be used to reduce variations in bucket sizes, (which correspond to chain lengths and associated lookup times). Otherwise, the mapping of keys to buckets may lead to skewed distributions in the number of keys in each bucket chain, thus creating variations in average lookup times across buckets. Load balancing of keys across buckets may be used to keep each bucket chain about the same size. 
     One straightforward way to achieve this is to use a known power of (e.g., two) choice idea. For example, with a load balanced design for a hash table mapping function, each key (e.g.,  1121  in  FIG. 7 ) may be hashed to two candidate hash table directory buckets, using two hash functions  710 A and  710 B. The counter in the index table  708 , which tracks the size of the chain, may be accessed by load balance logic  770 . The candidate bucket with the lesser number of keys is selected for appending the record to that bucket, and its corresponding slot counter incremented. In the example of  FIG. 7 , the selected slot/bucket is represented by the solid line from key mapping function  710 A (rather than the dashed line from  710 B). 
     The above-described load balancing alternative, without more, leads to an increase in the number of flash reads needed during lookup. That is, for a lookup operation, each key may need to be searched for in both of its candidate buckets, whereby the average as well as worst case number of flash reads (and thus corresponding lookup times) doubles. 
     To avoid this latency, as generally represented in  FIG. 8 , a bloom filter may be added per index table slot that summarizes the keys that have been inserted in the respective bucket, whereby (most of the time, to a relatively high probability) only one bucket chain on flash needs to be searched during a lookup. In general, a bloom filter contains an entry that either guarantees that a key is not in a chain, or to a high probability indicates that a key may be in a chain; that is, there are no false negatives, although there may be false positives. 
     Note that the bloom filter in each slot can be sized based on desired characteristics, e.g., to contain about k keys, because load balancing ensures that when the hash table reaches its budgeted full capacity, each bucket will contain not many more than k keys (with very high probability). Dimensioning a bloom filter to use one byte per key gives a false positive probability of two percent; hence the bloom filter in each slot may be of size k bytes. A larger bloom filter decreases the false positive rate, but at the cost of memory, and vice-versa. 
     Moreover, the introduction of bloom filters has another desirable side effect, namely that lookups on non-existent keys will (almost always) not require any flash reads, because the bloom filters in both candidate slots of the key will likely indicate when a key is not present, and only rarely suggest that a key may be present when it is actually not. This is in contrast to not having bloom filters, in which lookups for non-existent keys lead to flash reads that involve traversing the entire chain in the respective bucket; (indeed, bloom filters may be used without load balancing, including with or without counters, for avoiding looking up non-existent keys). 
     Still further, while having bloom filters in each bucket reduce lookup times when two-choice load balancing is used, bloom filters themselves also benefit from load balancing. More particularly, load balancing aims to keep the number of keys in each bucket upper bounded (roughly) by the parameter k. This helps to keep bloom filter false positive probabilities in that bucket bounded, as per the dimensioned capacity of k keys. Without load balancing, many more than k keys may be inserted into a given bucket, which increases the false positive rates of the respective bloom filter well beyond that for which it was dimensioned. 
     The additional fields added to each slot in the index table are represented in  FIG. 8 , e.g., with k bytes per bloom filter (BF), one byte per chain size counter (Ctr), and four bytes per pointer (Ptr) in one implementation. During a lookup operation, the key is mapped to its two candidate directory buckets, with the chain on flash being searched only if the respective bloom filter indicates that the key may be in that bucket. Thus, accounting for bloom filter false positives, the chain on flash will be searched with no success in less than two percent of the lookups, given a bloom filter dimensioned as described above. 
     Note that when load balancing and an insert operation corresponds to an update of an earlier inserted key, the record is inserted in the same bucket as the earlier one, even if the choice determined by load balancing (out of two candidate buckets) is the other bucket. Otherwise the key may be inserted in the bloom filters of both candidate slots, which may cause traversing of more than one bucket chain on flash during lookups. Moreover, a similar problem arises with version resolution during lookups if different versions of a key are allowed to be inserted in both candidate buckets. This also leads to efficiencies during garbage collection operations because the obsolete values of a key appear in the same bucket chain on flash. Note that overriding of the load balancing-based choice of insertion bucket can be avoided when the application does not perform updates to earlier inserted keys, which occurs in certain applications, such as storage deduplication. 
     As can be readily appreciated, the amount of RAM space used is reduced in the implementations described herein. For example, consider that the pointer to flash in each slot is four bytes, which accommodates up to 4 GB of a byte-addressable log. If records are of a fixed size such as sixty-four bytes, then this can accommodate up to 256 GB of 64-byte granularity addressable log. (Larger pointer sizes can be used according to application requirements.) Then, with an average bucket size value of k=10, the RAM space overhead is only 4/k=0.4 bytes=3.2 bits per entry, independent of key-value size. The average number of flash reads per lookup is k/2=5; with current flash media achieving flash read times in the range of 10 microseconds, this corresponds to a lookup latency of about 50 microseconds. The parameter k thus provides a mechanism for achieving tradeoffs between low RAM space usage and low lookup latencies. 
     In an implementation having bloom filters, the RAM space overhead per bucket has three components, e.g., a k-byte bloom filter, a one-byte chain size counter, and a four-byte pointer. This space overhead per slot is amortized over an average of k keys (in that bucket), whereby the RAM space overhead per entry is computed as (k+1+4)/k=1+5/k, which is about 1.5 bytes for k=10. The average number of flash reads per lookup is k/2=5 (with high probability). Moreover, the variation across lookup latencies for different keys is more controlled in this implementation, (compared to a non-bloom filter implementation), as bucket chains are about the same size due to load balancing of keys across buckets. 
     Turning to another alternative,  FIG. 9  shows an implementation in which a slot  990  in a first index table  908  corresponding to a bucket in secondary storage is mapped by a second mapping function  992  to a sub-slot in a second index table  909  corresponding to a sub-bucket. In general, this splitting via secondary mapping may be used when a bucket becomes too large with respect to the number of records therein. 
     By way of example, consider that the first mapping function is a range mapping function that maps keys to a slot based on their range, e.g., using simple numbers, keys having values between one and ten go to slot one, keys between eleven and twenty go to slot two, and so on. If the counter associated with a given slot exceeds some splitting value, then that slot/bucket is split by a second mapping function, e.g., keys between eleven and fifteen go to sub-slot  995  while keys between sixteen and twenty go to sub-slot  996 . The pointer (or another indicator) in the first index table may be used to tell whether for a given key the pointer points directly to the secondary storage bucket or indicates the need to use the second mapping function to find a sub-bucket pointer in a sub-slot. 
     Note that while the simplified example above used a basic dividing of a range into two halves, a more complex function may be used. For example, a dynamic B+ treeload balancing function or the like may be used to split a bucket more evenly such that the sub-slot/sub-bucket counts determine how to divide the keys, e.g., keys eleven to thirteen may be in one sub-bucket, and keys fourteen to twenty another; this may readjust as needed. Moreover, more than two sub-slots/sub-buckets per primary slot also may be used. 
     Note that the above splitting operation involves changing pointers, which can be relatively complex. However, the above-described compaction process inherently adjusts pointers, and thus compacting the records in each sub-bucket (e.g., sub-range) may be performed to automatically adjust the pointers. 
     One benefit of range mapping is locality. For example, the key-value pairs coming from the same website or other source may have keys that are numerically close to one another. Lookups may occur on such keys around the same time. By range mapping and compacting, flash reads may be reduced, because numerically close keys, often looked up around the same time, will be on the same compacted page in many instances. 
     Exemplary Operating Environment 
       FIG. 10  illustrates an example of a suitable computing and networking environment  1000  on which the examples of  FIGS. 1-9  may be implemented. The computing system environment  1000  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  1000  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  1000 . 
     The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to: personal computers, server computers, hand-held or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and so forth, which perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and/or remote computer storage media including memory storage devices. 
     With reference to  FIG. 10 , an exemplary system for implementing various aspects of the invention may include a general purpose computing device in the form of a computer  1010 . Components of the computer  1010  may include, but are not limited to, a processing unit  1020 , a system memory  1030 , and a system bus  1021  that couples various system components including the system memory to the processing unit  1020 . The system bus  1021  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
     The computer  1010  typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer  1010  and includes both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the computer  1010 . Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above may also be included within the scope of computer-readable media. 
     The system memory  1030  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  1031  and random access memory (RAM)  1032 . A basic input/output system  1033  (BIOS), containing the basic routines that help to transfer information between elements within computer  1010 , such as during start-up, is typically stored in ROM  1031 . RAM  1032  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  1020 . By way of example, and not limitation,  FIG. 10  illustrates operating system  1034 , application programs  1035 , other program modules  1036  and program data  1037 . 
     The computer  1010  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 10  illustrates a hard disk drive  1041  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  1051  that reads from or writes to a removable, nonvolatile magnetic disk  1052 , and an optical disk drive  1055  that reads from or writes to a removable, nonvolatile optical disk  1056  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  1041  is typically connected to the system bus  1021  through a non-removable memory interface such as interface  1040 , and magnetic disk drive  1051  and optical disk drive  1055  are typically connected to the system bus  1021  by a removable memory interface, such as interface  1050 . 
     The drives and their associated computer storage media, described above and illustrated in  FIG. 10 , provide storage of computer-readable instructions, data structures, program modules and other data for the computer  1010 . In  FIG. 10 , for example, hard disk drive  1041  is illustrated as storing operating system  1044 , application programs  1045 , other program modules  1046  and program data  1047 . Note that these components can either be the same as or different from operating system  1034 , application programs  1035 , other program modules  1036 , and program data  1037 . Operating system  1044 , application programs  1045 , other program modules  1046 , and program data  1047  are given different numbers herein to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer  1010  through input devices such as a tablet, or electronic digitizer,  1064 , a microphone  1063 , a keyboard  1062  and pointing device  1061 , commonly referred to as mouse, trackball or touch pad. Other input devices not shown in  FIG. 10  may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  1020  through a user input interface  1060  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  1091  or other type of display device is also connected to the system bus  1021  via an interface, such as a video interface  1090 . The monitor  1091  may also be integrated with a touch-screen panel or the like. Note that the monitor and/or touch screen panel can be physically coupled to a housing in which the computing device  1010  is incorporated, such as in a tablet-type personal computer. In addition, computers such as the computing device  1010  may also include other peripheral output devices such as speakers  1095  and printer  1096 , which may be connected through an output peripheral interface  1094  or the like. 
     The computer  1010  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  1080 . The remote computer  1080  may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  1010 , although only a memory storage device  1081  has been illustrated in  FIG. 10 . The logical connections depicted in  FIG. 10  include one or more local area networks (LAN)  1071  and one or more wide area networks (WAN)  1073 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  1010  is connected to the LAN  1071  through a network interface or adapter  1070 . When used in a WAN networking environment, the computer  1010  typically includes a modem  1072  or other means for establishing communications over the WAN  1073 , such as the Internet. The modem  1072 , which may be internal or external, may be connected to the system bus  1021  via the user input interface  1060  or other appropriate mechanism. A wireless networking component such as comprising an interface and antenna may be coupled through a suitable device such as an access point or peer computer to a WAN or LAN. In a networked environment, program modules depicted relative to the computer  1010 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 10  illustrates remote application programs  1085  as residing on memory device  1081 . It may be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     An auxiliary subsystem  1099  (e.g., for auxiliary display of content) may be connected via the user interface  1060  to allow data such as program content, system status and event notifications to be provided to the user, even if the main portions of the computer system are in a low power state. The auxiliary subsystem  1099  may be connected to the modem  1072  and/or network interface  1070  to allow communication between these systems while the main processing unit  1020  is in a low power state. 
     CONCLUSION 
     While the invention is susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the invention to the specific forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention.