Reducing overflow of hash table entries

An apparatus and method for reducing overflow in a hash table lookup mechanism that moves entries from full or nearly full buckets in one hash table to less full buckets of another hash table. The number of bucket overflows caused by hashing input addresses can be reduced.

BACKGROUND OF THE INVENTION

Table lookup is a common operation performed by many Internet switches and routers. As depicted inFIG. 1, a typical switch includes a Forwarding Engine, Line Cards, and a Switching Fabric which can be implemented as Application Specific Integrated Circuits (ASICs). The forwarding engine is a processor that has a group of tables which may include an L2 table with MAC addresses, an L3 table with IP addresses, a NetFlow table with flow identifiers, and other tables with L4-L7 information. The address lookup function examines a packet's destination address, stored in a table, and selects an output port associated with that address.

Looking up an address in a table is usually combined with a hashing operation and the performance of the lookup process depends on both the hash function and the table organization. In the switch depicted inFIG. 1, the hashing operation is performed by Linear Feedback Shift Registers (LFSRs) for high speed. Doing a lookup operation means searching for an item in the table. When the item is found (Hit), the table location will also contain other information related to the further processing for that item. For example, on L2 forwarding tables, lookup is done on MAC addresses and the related information contained in the table is the port that first received the MAC address. On L3 forwarding tables, lookup is done on IP addresses and the related information is the port where packets destined to that IP address should be sent.

When the item is not found on the table it will be inserted (Learning phase), and if it is not possible to learn a new entry, then the item will be dropped (Miss). Usually hardware lookups resulting in a miss will be redirected to software, thus slowing down the performance. Tables can be implemented in various ways, including using RAM (e.g. DRAM, Synchronous DRAM (SDRAM), Reduced Latency DRAM (RLDRAM) or Ternary Content Addressable Memory (TCAM)).

A common search mechanism employed is called D-Left Hashing which is depicted in the flow chart ofFIG. 2. D-Left hashing uses two hash tables with two different primitive polynomial hash functions. The search key is hashed with two different and uncorrelated hash functions. The hash functions reduce the key from a large number of bits to a smaller number of bits in a pseudo-random manner. The result of the first hash function is used as an index into the left table. An identical process is followed by using the second hash function and the right table, in parallel with the process performed by the left table.

Since keys in the tables are unique, a key which matches the key data of an entry results in a unique match, and the associated data of that entry is output from the search function. Each table contains as many rows, or buckets, as there are possible results from the hash function. For example, if the hash function produces an 11-bit value then there will be 211, or 2048, buckets in each table. If each table bucket contains four cells, then up to four keys which hash to the same bucket index can be stored in that bucket. The key data field in each of the buckets is compared against the original search key to determine if there is a match.

The D-Left hashing mechanism could easily generate hashing overflow, when all cells are occupied by the index of these two hash tables. As such, additional hardware resources are needed to resolve the overflow. The more overflow generated, the more additional hardware resources are needed.

The challenges in the field of table lookup continue to increase with demands for more and better techniques having greater flexibility and adaptability. Therefore, a need has arisen for a new apparatus and method for efficient and low-cost table lookup techniques.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment of the invention, an optimization algorithm improves the D-Left hashing algorithm. The optimization applies to existing hash tables at any time when insertion or deletion of hash table entries occurs.

The following is an example that will trigger this optimization when both left and right buckets indexed by a new entry have the same number of occupied cells during an insertion attempt. In this case, a new entry X is to be inserted into the hash table. The LeftHash function generates a left index LI(X) to a bucket in the left hash table and the RightHash function generates a right index RI(X) to a bucket in the right hash table. If both the left and right buckets contain the same number (J) of occupied cells, the new algorithm will examine each of the occupied cells in the left and right indexed buckets from 0 to (J−1).

The operation of the algorithm will now be described with reference to the flow chart ofFIG. 3and the block diagram ofFIG. 4Awhere both the left and right buckets indexed by the new entry X have J stored entries. InFIG. 4A, the first cell, L(j=0), in the left bucket indexed by LI(X) holds the entry Y. The RightHash function is applied to the stored entry Y to generate a left hash index RI(Y) to a right bucket in the right hash table. In this example, the number of entries in the right bucket indexed by RI(Y) is equal to 2 which is less than J=3.

In the following the term “moved” is utilized to describe either the operation of moving or the operation of copying an entry to a new cell in a different bucket. Further, a cell is described as “empty” after the entry has been moved and another entry may be written to the cell. The term empty can be applied to a cell that holds data the has been moved and can now be overwritten.

The entry Y held in cell L(j=0) in the left bucket indexed by LI(X) is now moved to the cell R(j=2) in the right bucket indexed by RI(Y) and the entry X is inserted into the now empty cell L(j=0) of the left bucket. In this way the number of entries in the buckets is balanced. The configuration of the tables after the application of the algorithm is depicted inFIG. 4B.

In the exampleFIG. 5A, as was the case forFIG. 4A, The LeftHash function generates a left index LI(X) to a bucket in the left hash table and the RightHash function generates a right index RI(X) to a bucket in the right hash table. If both the left and right buckets contain the same number (J) of occupied cells, the new algorithm is invoked. In this example, the LI(X) bucket holds the stored entries Y, Z, W and the RI(X) bucket holds the stored entries F, G, A so that both buckets hold the same number, J=3, of stored entries.

The algorithm starts by checking the first entry in the left hash table. The right hash function is applied to the entry Y, held in L(j=0) of the LI(X) bucket, to index the right bucket RI(Y) which also holds J=3 entries. Now the algorithm switches to the right hash table to examine the entry F, held in the cell R(j=0) of RI(X). In this example the LI(F) bucket of the left hash table also holds J=3 entries.

The algorithm then switches back to the left hash table to examine the next cell L(j=1) in the left bucket indexed by LI(X). The RightHash function is applied to Z, the entry held in L(j=1) of the left bucket, to generate the index RI(Z) of a right bucket. In this example the number of entries in the right bucket indexed by RI(Z) is equal to 2 which is less than J=3.

The entry Z held in cell L(j=1) in the left bucket indexed by LI(X) is now moved to the cell R(j=2) in the right bucket indexed by RI(Z) and the entry X inserted into the now empty cell L(j=1) of the left bucket. In this way the number of entries in the buckets is balanced. The configuration of the tables after the application of the algorithm is depicted inFIG. 5B.

If the bucket indexed by RI(Z) did not have less than J entries then the cell R(j=1) of the RI(X) bucket would be examined an so on. Thus the algorithm alternately examines successive cells in the left and right buckets indexed by the new entry X.

In another embodiment, the algorithm is expanded in a recursive way, when all J entries of first level buckets are occupied, optimization is applied to those entries of subsequent level buckets until a loop is formed when the algorithm returns to the original hash bucket. If all J entries of buckets traversed are full, then X must be inserted into a J+1 entry.

InFIG. 6A, all the buckets in the right hash table indexed by entries held in the occupied cells of the bucket indexed by LI(X) and RI(X) have J or more entries (only LI(X) is depicted inFIG. 6A). The algorithm is applied to the bucket indexed by the entry Y held in the first cell of the left bucket indexed by LI(X). In this example the algorithm is applied to the cells in the right bucket in the right hash table indexed by RI(Y).

The entry held in the first cell in the right bucket indexed by RI(Y) is A. The LeftHash function is applied to the stored entry A to generate left hash index LI(A) to a left bucket in the left hash table. In this example, the number of entries in the bucket indexed by LI(A) is equal to 2 which is less than J=3.

The entry A held in cell R(j=0) in the right bucket indexed by RI(Y) is now moved to the cell L(j=2) in the left bucket indexed by LI(A) and the entry Y held in cell L(j=0) in the left bucket indexed by LI(X) is moved to the now empty cell R(j=0) in the right bucket indexed by RI(Y). Then, the entry X is inserted into the now empty cell L(j=0) in the left bucket indexed by LI(X). In this way the number of entries in the buckets is balanced. The configuration of the tables after the application of the algorithm is depicted inFIG. 6B.

The invention may be implemented as program code, stored on a computer readable medium, that is executed by a digital computer. The computer readable medium may include, among other things, magnetic media, optical media, electromagnetic fields encoding digital information, and so on.

The invention has now been described with reference to the preferred embodiments. Alternatives and substitutions will now be apparent to persons of skill in the art. In particular, although the above described embodiment utilizes only two hash tables the principles of the invention can be applied to systems using more than two hash tables. Further, the invention has utility in other applications besides switches or routers such as processor caches, translation lookaside buffers, data compression applications, database accelerators, neural networks, and so on. Additionally, although hashing utilizing LFSRs is described, hashing may also be performed by a processor executing software. Further, in the above description the tables have been designated as right and left. Persons of skill in the art realize these terms are only identifiers and there is no geometrical significance to the terms. Accordingly, it is not intended to limit the invention except as provided by the appended claims.