Title of Invention: Log Forwarding to Avoid Deadlocks During Parallel Log Replay in Asynchronous Table Replication Abstract: Disclosed herein are system, method, and computer program product embodiments for removing a deadlock during replication from distributed source tables to a replica node. An embodiment operates by detecting a deadlock at a parallel log replayer at a replica node. A first replication log entry from a queue at the parallel log replayer is then selected based on whether removing the first replication log entry from the queue removes the deadlock. The first replication log entry is then forwarded to a waiting queue. A second replication log entry is then replayed at the parallel log replayer. After replaying the second replication log entry, the first replication log entry is replayed at the parallel log replayer. Detailed Description: CROSS-REFERENCE TO RELATED APPLICATIONS This application claims benefit of US Provisional Patent Application No. 62/084,065 filed Nov. 25, 2014, which is assigned to the assignee of the present patent application, the contents of which are incorporated herein by reference in their entirety. BACKGROUND Database management systems often need to be able to handle a large number of database transactions. This is often the case where database management systems are used by insurance companies, financial services companies, or e-commerce websites. In order to handle a large number of database transactions, database management systems often distribute read transactions to replica tables at a replica node. Database management systems maintain the replica tables by replicating changes from the source tables at a source node to the corresponding replica tables at the replica node. In order to further improve database transaction performance for the database management system, the source tables may be distributed across multiple source nodes. But when the source tables are distributed across multiple source nodes, a deadlock may result when the database management system replicates changes to the replica tables. This results in replication being halted and creates a visibility gap between the source tables and the replica tables. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings are incorporated herein and form a part of the specification. FIG. 1 is a block diagram of a distributed database system with table replication, according to an example embodiment. FIG. 2 is a block diagram of a distributed database system with transactional and parallel log replay for asynchronous table replication, according to an example embodiment. FIG. 3 illustrates an example of ensuring transactional consistency when updating the same row of a replica table, according to the example embodiment described in FIG. 2. FIG. 4 illustrates an example of ensuring transactional consistency during parallel log replay, according to the example embodiment described in FIG. 2. FIG. 5 illustrates an example of how data integrity is ensured during parallel log replay, according to the example embodiment described in FIG. 2. FIG. 6 illustrates an example of a deadlock during parallel log replay, according to the example embodiment described in FIG. 2. FIG. 7 is a flowchart illustrating a process for transactional and parallel log replay, according to an example embodiment described in FIG. 2. FIG. 8 illustrates an example of row-ID based lost change detection and resynchronization, according to an example embodiment. FIG. 9 is a flowchart illustrating a process for row-ID based lost change detection and resynchronization, according to an example embodiment. FIG. 10 is a flowchart illustrating a process for minimizing table locking during lost change detection and resynchronization, according to an example embodiment. FIG. 11 is a block diagram of a distributed database system supporting replication from distributed source tables with transactional and parallel log replay, according to an example embodiment FIG. 12 illustrates an example of avoiding a deadlock in distributed database system supporting replication from distributed source tables with transactional and parallel log replay, according to the example embodiment described in FIG. 11. FIG. 13 is a flowchart illustrating a process for dynamically resolving a deadlock by log forwarding, according to an example embodiment. FIG. 14 is an example computer system useful for implementing various embodiments. In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears. DETAILED DESCRIPTION Provided herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for resolving a deadlock in a database management system supporting table replication from distributed source tables with transactional and parallel log replay. An embodiment operates by detecting a deadlock at a parallel log replayer at a replica node. A replication log entry is then selected by the database management system from a queue at the parallel log replayer based on whether removing it from the queue resolves the deadlock. The database management system then forwards the selected replication log entry to a waiting queue. The database management system then replays the remaining replication log entries in parallel at the parallel log replayer. Accordingly, by having the database management system forward replication log entries to a waiting queue, the deadlock is eliminated and table replication is not halted. This further minimizes the visibility gap between the source tables and the replica tables. A database management system is a collection of computer software programs that control the organization, storage, and retrieval of data in a database. A database is an organized collection of data. A database may be organized according to a database model. A database model determines the logical structure of a database and how data is stored, organized, and manipulated. For example, a relational model is a popular database model. A relational database model organizes data as a set of tables from which data can be accessed or reassembled in many different ways without having to reorganize the tables. Each table may contain one or more data categories in columns. Each row may contain a unique instance of data for the categories defined by the columns. For example, a business order entry database may include a table that describes a customer with columns for name, address, phone number, and so forth. Each row may have a primary key. A primary key is a column, or combination of columns, designated to uniquely identify a row. Each table may be represented using either row-based storage or column-based storage. In row-based storage, the database management system stores data row by row in the database. In column-based storage, the database management system stores data column by column in the database. A database management system that uses column-based storage is often faster than a database management system that uses row-based storage. This is often the case when a database management system performs read intensive operations on a large data repository. This is because a column-oriented database management system only needs to scan the relevant columns when performing an operation. In contrast, a row-oriented database management system must scan the columns of the rows it is reading. A column-oriented database system is often chosen where operations may be performed on only a few columns. Similarly, a column-oriented database system may be chosen where a table has a large number of columns, or a table has a large number of rows and column operations are typically performed by the database management system. A request to query, insert, or update a database may be made to a database management system using a database language. A database language is a computer language used to make requests of a database management system. For example, Structured Query Language (SQL) is a database language used to communicate with a database management system. A request to query, insert, or update a database may be performed as a database transaction by a database management system. A database transaction consists of one or more independent units of work, each reading or writing data to the database. A database transaction may be read or write. A read database transaction does not write data to the database. For example, a query is a read database transaction. A write database transaction writes data to the database. For example, an insert is a write database transaction. A database management system executes a database transaction either completely or stevenmolina@example.org. If no errors occur during the execution of the database transaction then the database management system commits the transaction to the database. The database management system commits a database transaction to the database by executing a transaction commit operation. A transaction commit operation causes the database management system to apply all data manipulations within the scope of the database transaction to the database. If an error occurs during the execution of the database transaction, none of the data manipulations within the scope of the database transaction are applied by the database management system to the database. In no case can a partially complete database transaction be committed by the database management system to the database. In other words, execution of a database transaction by the database management system always leaves the database in a consistent state. A database management system executes a database transaction in isolation from other database transactions. Furthermore, the database management system checks that the result of executing a database transaction satisfies existing database constraints. In order to track and manage each database transaction, the database management system assigns each database transaction a transaction ID. FIG. 1 illustrates a distributed database system 100 with table replication, according to an example embodiment. Distributed database system 100 includes a database management system 102 and a distributed database 104. Database management system 102 is a collection of computer software programs that control the organization, storage, and retrieval of data in distributed database 104. A request to query, insert, or update data in distributed database 104 is performed as a database transaction by database management system 102. Distributed database 104 is stored in source node 106 and replica node 108. Source node 106 and replica node 108 may be separate computers located in the same physical location. Source node 106 and replica node 108 may also be separate computers dispersed over a network of interconnected computers. Distributed database 104 is a relational database. For example, distributed database 104 includes tables A, B, C, D, E, and F. The tables of distributed database 104 are stored in source node 106 and replica node 108. The tables stored in source node 106 are source tables. The source tables in source node 106 contain the current data in distributed database 104. As would be appreciated by a person of ordinary skill in the art, the source tables in source node 106 may be stored across a plurality of source nodes. Specifically, each source node of a plurality of source nodes may store a subset of the source tables in distributed database 104 and operate exclusively on that particular subset. The tables stored in replica node 108 are replica tables. A replica table is a copy of a source table in source node 106. As would be appreciated by a person of ordinary skill in the art, the replica tables may be stored across a plurality of replica nodes. Database management system 102 may designate one or more source tables stored in source node 106 for replication to replica node 108. Database management system 102 then maintains copies of these designated source tables at replica node 108 as replica tables. For example, database management system 102 replicates tables E and F at source node 106 as tables E′ and F′ at replica node 108. In other words, tables E′ and F′ are copies of tables E and F. As would be appreciated by a person of ordinary skill in the art, database management system 102 may replicate all, or a proper subset, of the source tables in source node 106 to replica node 108 depending on usage requirements. By maintaining replica tables at replica node 108, database management system 102 may distribute read database transactions among the source tables at source node 106 and the replica tables at replica node 108. In other words, database management system 102 can perform load balancing by distributing read database transactions to the replica tables. This may increase overall read database transaction performance for database management system 102 of distributed database 104 by reducing central processing unit (CPU) consumption and table contention at source node 106. Database management system 102 may submit a read database transaction to either the source tables or replica tables. This is because database management system 102 maintains the state of the source tables in source node 106 with the state of the replica tables in replica node 108. Database management system 102 must submit a write database transaction to the source tables in source node 106. This is because the source tables in source node 106 contain the current data. Database management system 102 cannot send a write database transaction directly to the replica tables in replica node 108 because the source tables in source node 106 would end up containing outdated data. Specifically, the data in the source tables in source node 106 would become inconsistent with the data in the replica tables in replica node 108. In order to ensure that the data in the source tables in source node 106 is consistent with the data in the replica tables in replica node 108, database management system 102 replays all write database transactions at the source tables in source node 106 to the corresponding replica tables in replica node 108. This ensures that the data in the replica tables is consistent with the data in the corresponding source tables. Database management system 102 may replay all write database transactions at the source tables to the corresponding replica tables either synchronously or asynchronously. In synchronous table replication, database management system 102 updates the source table and the corresponding replica table at the same time. In other words, database management system 102 updates the replica table during the same transaction boundary as the source table. This ensures that the replica table will contain the same data as the source table. However, synchronous table replication often increases the write database transaction response time for database management system 102. This is because the replica table is updated by database management system 102 at the same time as the corresponding source table. In asynchronous table replication, database management system 102 does not update the source table and the replica table at the same time. Rather, database management system 102 may update the replica table after the write database transaction is committed at the source table. This means the replica table may contain outdated data compared to the source table. However, asynchronous table replication often incurs significantly less performance overhead for database management system 102 than synchronous table replication. While database management system 102 often incurs less performance overhead when performing asynchronous table replication, it often introduces significant delay in table replication. This is because database management system 102 must ensure transactional consistency when replaying write database transactions at the replica table. Specifically, database management system 102 may have to replay write database transactions at the replica table more slowly in order to ensure transactional consistency. FIG. 2 illustrates a distributed database system with transactional and parallel log replay for asynchronous table replication, according to an example embodiment. This example embodiment for asynchronous table replication minimizes table replication delay by replaying write database transactions in parallel while still ensuring transactional consistency. Specifically, the database management system in this example embodiment replays replication log entries in parallel while serializing the replay of transaction commit log entries. A technical problem with a database management system replaying write database transactions in parallel is ensuring transactional consistency when the transactions update the same row of a replica table in the distributed database. In the example of FIG. 3 for the embodiment of FIG. 2, there are three consecutive write database transactions (e.g. T1, T2, and T3) for the same row at a source table. Transaction T1 is the first transaction and inserts a row at the source table. Transaction T2 is the second transaction and updates the row at the source table. Transaction T3 is the final transaction and also updates the row at the source table. The database management system must replay these three database write transactions in order at the replica table in order to ensure transactional consistency. For example, if transaction T2 is replayed after transaction T3, the final value of column 1 of the row at the replica table will be “B”. However, this would be inconsistent with the value of column 1 of the row at the source table which is “C”. In an example embodiment, a database management system may ensure transactional consistency by replaying database write transactions at the replica node based on table ID. In other words, the database management system may replay a single database write transaction to a replica table at a time. However, if the database management system frequently updates a source table, then the database management system may have to serially replay the database write transactions to the replica table. This may significantly limit the speed by which the database management system may replay database write transactions. The embodiment in FIG. 2 overcomes this technical problem by creating a row-ID column in the source tables and the replica tables. Specifically, each row in a source table or replica table has a row-ID column. The row-ID column may be of small length. For example, the row-ID column may be 8 bytes in length. However, a person of ordinary skill in the art would appreciate that the row-ID column may be of different length. In the embodiment in FIG. 2, the database management system increments the value of row-ID column of a row in the source table for a write database transaction to the row. The database management system similarly increments the value of the row-ID column at the corresponding row of the replica table after replaying the write database transaction at the replica table. The row-ID column value is different than the primary key column value of a row because the value of the row-ID column is incremented when the row is updated. In contrast, the primary key column value is never updated for a row. In other words, the row-ID column value is a change identifier whereas the primary key column value is a row identifier. In the embodiment in FIG. 2, the database management system may increment the row-ID column value for each write database transaction with minimal performance overhead. This is because of two aspects of the embodiment in FIG. 2. First, in the embodiment in FIG. 2, the row-ID column value is of small length. Specifically, the row-ID column value may be 8 bytes in length. Because the row-ID column value is of small length, the database management system may increment the row-ID column value using a single atomic hardware instruction. For example, the row-ID column value may be incremented using a compare-and-swap instruction. Because the database management system may perform a single atomic hardware instruction with minimal overhead, the database management system may efficiently increment the row-ID column value. Second, the database management system may not need to log the incrementing of the row-ID column value because the next row-ID column value can be reset as the maximum value of the available values in the row-ID column. For example, the database management system may reset the row-ID column value as the maximum value of the available values in the row-ID column after a restart of the database management system. The embodiment in FIG. 2 includes distributed database system 104 from FIG. 1. The tables of distributed database 104 are stored in source node 106 and replica node 108. Source node 106 and replica node 108 are also from FIG. 1. Source node 106 includes a write set extractor 202, a replication log generator 204, a log send buffer 206, a log sender 208, and a transaction manager 210. Write set extractor 202 extracts the operation type, table ID, transaction ID, the new row image, and row-ID column value for each write database transaction on a row of a source table in source node 106. The operation type represents the type of write database transaction being performed. For example, the write database transaction could be an insert, update, or delete operation. The table ID is a value that uniquely identifies the table containing the row being updated. The database management system may assign a unique table ID value to each source table. The transaction ID is a value that uniquely identifies a write database transaction to be performed by the database management system. The transaction ID allows the database management system to ensure the order in which write database transactions are performed. For example, a write database transaction with a transaction ID of 101 must be performed before a write database transaction with a transaction ID of 102 for the same given row. Otherwise the row will contain inaccurate data. Replication log generator 204 generates a replication log entry for a changed row of a source table. Specifically, the replication log entry may contain the operation type, table ID, transaction ID, the new row image for the changed row as extracted by the write set extractor 202. In addition, the replication log entry may contain one or more row-ID column values. For an insert operation, a replication log entry contains the new row-ID column value of the inserted row. For an update operation, a replication log entry contains the old row-ID column value of a row before the update operation and the new row-ID column value after the update operation is completed. For a delete operation, a replication log entry contains the old row-ID column value of the row to be deleted before the delete operation is completed. As would be appreciated by a person of ordinary skill in the art, a replication log entry may be represented and stored in various ways. Replication log generator 204 appends a generated replication log entry to log send buffer 206. Log send buffer 206 stores replication log entries and transaction commit log entries. Log sender 208 send replication write log entries and transaction commit log entries in log send buffer 206 to replica node 108. For example, where source node 106 and replica node 108 are connected over a computer network, log sender 208 sends replication log entries in log send buffer 206 to replica node 108 over the computer network. In order to ensure transactional consistency at the source tables of source node 106, transaction manager 210 performs a transaction commit operation to apply a write database transaction to the source tables. In addition, transaction manager 210 creates a transaction commit log entry when the write database transaction is committed by transaction manager 210 to the source tables. A transaction commit log entry includes the transaction ID of the committed write database transaction. Transaction manager 210 appends a transaction commit log entry to the log send buffer 206. Log sender 206 sends the transaction commit log entry in log send buffer 206 to replica node 108 to apply the committed write database transaction to the replica tables. At replica node 108, replication log receiver and dispatcher 212 receives replication log entries and transaction commit log entries from source node 106. Replication log receiver and dispatcher 212 dispatches a received log entry to parallel log replayer 214 or transaction log replayer 216 depending on the type of log entry. If the received log entry is a replication log entry, replication log receiver and dispatcher 212 dispatches the replication log entry to the parallel write log replayer 214. Parallel log replayer 214 may include multiple queues, and each queue may be assigned a replication log entry for replay. Parallel log replayer 214 may replay the replication log entries assigned to each queue simultaneously in parallel. By replaying the replication log entries in parallel, parallel log replayer 214 may minimize the table replication delay between source node 106 and the replica node 108. Moreover, parallel log replayer 214 may replay two replication log entries for the same replica table in parallel. This is possible because transaction log replayer 216 replays transaction commit log entries serially as discussed below. If the received log entry is a transaction commit log entry, replication log receiver and dispatcher 212 dispatches the transaction commit log entry to the transaction log replayer 216. This is necessary in ensure transactional consistency during the parallel replay of replication log entries by parallel log replayer 214. The embodiment of FIG. 2 ensures transactional consistency during the parallel replay of replication log entries by parallel log replayer 214 by enforcing two conditions. First, the result of the replay of a replication log entry may only become visible at a replica table after the corresponding transaction commit log entry is replayed. In other words, the database management system must replay the corresponding transaction commit log entry before the changes made at the source tables are actually persisted at the replica tables. Second, replication log entries may be replayed in parallel independently of their execution order at the source table but transaction commit log entries must be replayed in exactly the same order as they were performed at the source tables. The embodiment of FIG. 2 ensures these two conditions are satisfied by replaying transaction commit log entries serially at transaction commit replayer 216 and replaying replication log entries in parallel at parallel log replayer 214 based on their row-ID column values. Specifically, parallel log replayer 214 replays a replication log entry for an update or delete operation after its old row-ID column value is visible in the replica table. This ensures that the second one of two conflicting write database transactions to the same row is replayed only after its preceeding write database transaction is replayed and committed. This is because the result of the first write database transaction, including the row-ID column value update, becomes visible at the replica table after the first write database transaction is committed. And the first write database transaction is committed when the transaction log replayer replays a transaction commit log entry for the same transaction ID as the first write database transaction. If parallel log replayer 214 replays the replication log entry subject to this condition, the row-ID column value of the row in the replica table is updated with the new row-ID column value included in the replication log entry (in case of an update log entry). Because this embodiment in FIG. 2 performs row-ID column value based dynamic detection of conflicting write sets, replication log entries can be freely dispatched by replication log receiver and dispatcher 212 to multiple queues at parallel log replayer 214 without any restriction. In particular, replication log receiver and dispatcher 212 can dispatch the replication log entries without performing table-level serialization. This may significantly accelerate the replication log replay performance, which also will reduce the visibility gap between the source tables and replica tables under asynchronous table replication. FIG. 4 illustrates an example of how the embodiment in FIG. 2 ensures transactional consistency during parallel log replay. Specifically, FIG. 4 includes four write database transactions (e.g. T1, T2, T3, and T4) to the same row at a replica table. These four write transactions are to be replayed by the embodiment described in FIG. 2. The row in the replica table includes a primary key column, a column 1, and a row-ID column. With respect to transaction T3, its replication log entry cannot be replayed before transaction T2's replication log entry even though they both are potentially capable of being replayed in parallel by parallel log replayer 214. This is because transaction T3's replication log entry includes “r2” as its old row-ID column value and “r3” as its new row-ID column value. Therefore, transaction T3 must wait to be replayed until the row-ID column value of “r2” is visible for the row in the replica table. And this possible only after transaction T2's replication log entry is replayed and committed to the replica table. Similarly, transaction T4's replication log entry can be replayed only after transaction T3's replication log entry is replayed and committed. This is because transaction T4's replication log entry represents a delete operation. Specifically, transaction T4's replication log entry includes “r3” as its old row-ID column value. Therefore, transaction T4 must wait to be replayed until the row-ID column value of “r3” becomes visible for the row in the replica table. FIG. 5 illustrates an example of how data integrity is ensured during parallel log replay, according to the embodiment described in FIG. 2. Specifically, FIG. 2 includes three write database transactions (e.g. T1, T2, and T3) to be replayed by the embodiment described in FIG. 2. In FIG. 5, the rows in the replica table include a primary key column, a column 1, and a row-ID column. With respect to transaction T3, its replication log entry can be replayed by parallel log replayer 214 before transaction T2 because transaction T3's insert replication log entry does not have any old row-ID column value and it does not violate any write set conflict rules. Nor does transaction T3's insert replication log entry violate any uniqueness constraint, e.g. each row having a unique primary key column value, assuming the primary key column was checked by the database management system at the source table). This is not an issue because the result of replaying transaction T3's insert replication log entry will not be visible until transaction T3's transaction commit log entry is replayed by transaction log replayer 216, and transaction T3's transaction commit log entry will be replayed by transaction log replayer 216 only after the transaction commit log entries of transactions T1 and T2 are replayed. Therefore, the embodiment of FIG. 2 ensures data integrity during parallel log replay. Another technical problem with the embodiment in FIG. 2 is the possibility of a deadlock during parallel log replay. However, the possibility of a deadlock during parallel log replay is eliminated by combining the embodiment in FIG. 2 with an embodiment of a two-phase locking protocol at the source table. Specifically, the embodiment of a two-phase locking protocol at the source table ensures that the locks held on the changed data set at the source table can be released only after the associated database transaction is committed. FIG. 6 illustrates a deadlock involving the same example illustrated in FIG. 5. FIG. 6 includes replica node 108 from the embodiment in FIG. 1 and parallel log replayer 212 and transaction log replayer 214 from the embodiment in FIG. 2. Parallel log replayer 214 includes queues 602 and 604 which store one or more replication log entries for replay. Transaction log replayer 216 includes transaction queue 606 which stores one or more transaction commit log entries. In FIG. 6, database transaction T1's insert replication log entry 608 is added to queue 602 of parallel log replayer 212. Database transaction T2's delete replication log entry and database transaction T3's insert replication log entry are added to queue 604 of parallel log replayer 212. Transaction queue 606 is ordered as T3's transaction commit log entry 614, T1's transaction commit log entry 616, and T2's transaction commit log entry 618. This creates a deadlock because there is a cyclic wait for dependency among the database transactions. Specifically, T2's delete replication log entry 610 in queue 604 will wait for the replay of T1's transaction commit log entry 616 because the old row-ID column value for T2's delete replication log entry 610 must be become visible at the replica table first. T3's insert replication log entry 612 in queue 604 will wait for T2's delete replication log entry 610 to be replayed first because its in the same queue. T3's transaction commit log entry 614 will wait for the replay of T3's insert replication log entry. T1's transaction commit log entry 616 will wait for T3's transaction commit log entry 614 to be replayed first because its in the same queue. However, this scenario is impossible because the following two rules are kept by the combination of this embodiment and an embodiment for a two-phase locking protocol. Specifically, a transaction commit log entry cannot be replayed by transaction log replayer 214 before its replication write log entry is replayed by parallel log replayer 212. Second, if any two replication log entries have a common write set, then the second replication log entry cannot be replayed before the first replication log entry's corresponding transaction commit log entry. Thus, because these conditions cannot hold at the same time in the example of FIG. 6, there can be no deadlock. FIG. 7 is a flowchart for a method 700 for transactional and parallel log replay, according to an embodiment. Method 700 is described with reference to FIG. 2. Method 700 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In step 702 of FIG. 7, replication log receiver and dispatcher 212 at replica node 108 of FIG. 2 receives a replication log entry or transaction commit log entry from source node 106. The replication log entry includes the operation type, table ID, transaction ID, the new row image, and one or more row-ID column values representing the associated write database transaction. The transaction commit log entry includes a transaction ID that represents a write database transaction that the database management system committed to the corresponding source table in source node 106. In step 704 of FIG. 7, replication log receiver and dispatcher 212 determines the type of log entry received in step 702. Specifically, replication log receiver and dispatcher 212 determines whether the log entry is a replication log entry or transaction commit log entry. If the type of log entry received is a replication log entry, then step 706 is performed. If the type of log entry received is a transaction commit log entry, then step 708 of FIG. 7 is performed. In step 706 of FIG. 7, replication log receiver and dispatcher 212 dispatches the replication log entry to parallel log replayer 214 for replay. Parallel log replayer 214 may comprise multiple queues, and the replication log entry may be assigned to a queue among the multiple queues for replay. Parallel log replayer 214 replays the replication log entry assigned to the queue once any previous replication log entries in the queue have been replayed. As discussed above, parallel log replayer 214 may replay multiple replication log entries in parallel, one assigned to each queue. As would be appreciated by a person of ordinary skill in the art, parallel log replayer 214 may replay the replication log entries in parallel by assigning a thread of execution for replay of each replication log entry. Parallel log replayer 214 replays the replication log entry subject to the transactional guarantees discussed above. Specifically, parallel log replayer 214 replays an update or delete replication log entry after its old row-ID column value is visible in the replica table. This ensures that the second one of two conflicting write transactions to be replayed to the same row is replayed only after the first write database transaction is replayed and committed. This is because the result of first write transaction, including the update of row-ID column value, becomes visible at the replica table after the first write transaction is committed. And the first write database transaction is committed when transaction log replayer 216 replays a transaction commit log entry for the same transaction ID as the first write database transaction. In the case of an update operation, if replaying the replication log entry satisfies this condition, the row-ID column value of the row in the replica table is updated with the new row-ID column value included in the replication write log entry. In step 710 of FIG. 7, replication log receiver and dispatcher 212 dispatches the transaction commit log entry to transaction log replayer 216 for replay of the transaction commit operation. Specifically, by replaying the transaction commit operation for the transaction ID in the transaction commit log entry, transaction log replayer 216 commits the associated write database transaction corresponding to a replication log entry that has been replayed by parallel log replayer 214 to the replica table. As discussed above, transaction log replayer 216 replays the transaction commit log entries serially. In order words, transaction log replayer 216 may process transaction commit log entries using a single queue. This is done to ensure transactional consistency at the replica node. Moreover, transaction log replayer 216 replays transaction commit log entries independently and simultaneously with the replication log entries being replayed by parallel log replayer 214. A technical problem with table replication occurs when there is a restart of the database management system at the source node or replica node. A restart may be required because of a crash of the source node or replica node. But when there is a restart due to a crash or node failure, there may exist not yet replicated changes from the source tables to the corresponding replica tables. This is especially true in asynchronous table replication because the table replication take place after the write database transaction commits at the source table. In order for distributed read transactions to return current and accurate data from the source or replica tables, the database management system must be able to detect such lost replication change as well as resynchronize the replica tables with the corresponding source tables. In an example embodiment for lost change detection and resynchronization, the database management system maintains a persistent replication log queue at the source node. The database management system then iterates the persistent replication log queue when restarting after a crash or node failure to find out if there is any lost data, e.g. data not replicated). Specifically, the database management system checks the persistent replication log queue and determines which replication log entries are presents and collects them. The database management system then sends the collected replication log entries to the replica node for replay. This example embodiment for lost change detection and resynchronization proceeds as follows. First, the database management system captures any changes (e.g. write database transactions) to the source table at the source node. This may performed by write set extractor as discussed above. For example, the database management system may capture write database transactions such as an insert, update, or delete operation. Second, the database management system stores the captured changes as replication log entries in the persistent replication log queue. While the database management system may store the replication log entries in the persistent replication log queue, the captured changes may not have been committed to the source table by the database management system. Third, if database management system commits the database transaction associated with the replication log entry to the source table by performing a transaction commit operation, the database management system makes the corresponding replication log entry in the persistent replication log queue persistent. In other words, even if the database management system crashes or the source node fails, the replication log entry is recoverable from the persistent replication log queue upon restart. Fourth, the database management system propagates the replication log entry and the transaction commit log entry to the replica node as discussed above, e.g. using the log send buffer. The database management system removes the replication log entry or transaction commit log entry from the persistent replication log queue upon propagation. Fifth, the database management system replays the replication log entry and transaction commit log entry on the corresponding replica table at the replica node as discussed above. Because the replicated log entries are stored persistently in the persistent replication log queue at the source node, if there is a crash or node failure, any lost change that was not replicated by the database management system to the replica node can be detected by checking the persistent replication log queue. Moreover, the replica node can be resynchronized by iterating over the replication log entries persistently stored in the persistent replication log queue and propagating those replication log entries to the replica node. While the above method can detect lost change and resynchronize the replica tables, there is often high overhead to the database management system in maintaining the persistent replication log queue. Moreover, if there is a crash between third and fourth steps above, the replica node will not receive the replication log entries resulting in data loss. In order to overcome these technical problems, an example embodiment uses a row-ID column value based lost change detection and resynchronization protocol. Specifically, this embodiment can use the same row-ID column value described in with respect to the embodiment in FIG. 2. By using the same row-ID column described above, a persistent replication log queue becomes unnecessary for lost change detection and resynchronization and may be eliminated. This eliminates the high overhead to the database management system in maintaining the persistent replication log queue. In this embodiment, instead of a persistent replication log queue, if there is restart due a crash or node failure, lost change detection and resynchronization can be performed by the database management system by comparing the source table to the replica table using the row-ID column values and determining which replication log entries were not replayed. More specifically, this embodiment includes source and replica tables having row-ID columns like the embodiment in FIG. 2. During a crash or node failure, the database management system restarts. During the restart, the database management system compares the row-ID column values of the source table to the row-ID column values of the corresponding replica table. If the database management system detects a mismatch of row-ID column values between the source and replica tables, the database management system identifies a lost change. The database management system can resolve the lost change by resending the corresponding row from the source table or by deleting the corresponding row at the replica table. In this embodiment, the database management system resolves a mismatch of row-ID column values between source and replica tables according to three cases. First, if the database management system find the same row-ID column value for a row at the both the source and replica tables, the database management does not have to perform any resynchronization because the tables are consistent. Second, if the database management system finds the row-ID column value for a row only at the source table, the database management system resends the corresponding row from the source table to the replica table. Finally, if the database management system finds the row-ID column value for a row only at the replica table, the database management system deletes the row from the replica table. FIG. 8 illustrates an example of how row-ID based lost change detection and resynchronization works, according to the embodiment. Specifically, FIG. 8 includes a source table 802 and replica table 804 that are setup for asynchronous table replication by the embodiment described in FIG. 2. Both source table 802 and replica table 804 include a row-ID column as described by the embodiment in FIG. 2. Both source table 802 and replica table 804 include a primary key column, a column 1 containing a data value, and a row-ID column. In the example of FIG. 8, the row-ID based lost change detection and resynchronization embodiment applies the above mismatch rules after a restart operation. For example, the database management system will compare the row-ID column value set of source table 802 (e.g. r1, r3, r5, r9) with the row-ID column value set of replica table 804 (e.g. r1, r2, r4, r8). For row 1 at source table 802, the row-ID column value of r1 is found at both at source table 802 and replica table 804 so there is nothing for the database management system to do. For row 2 at source table 802, the row-ID column value of r3 is found only at source table 802. Similarly, for rows 3 and 4 at source table 802, the row-ID column values of r5 and r9 are found only at source table 802. Therefore, the database management system must reinsert rows 2, 3, and 4 from source table 802 to replica table 804. For row 2 at replica table 804, the row-ID column value of r2 is found only at replica table 804. Similarly, for rows 3 and 5 at replica table 804, the row-ID column values of r4 and r8 are found only at replica table 804. Therefore, the database management system must delete rows 2, 3, and 5 at replica table 804. As a result, both source table 802 and replica table 804 will be synchronized by the database management system. This embodiment can therefore detect lost change and resynchronize the lost change without maintaining any persistent replication log queue. However, the database management system pays a performance penalty in comparing the row-ID column values of the source table with the row-ID column values of the replica table. Table comparison is often very expensive in terms of CPU time for the database management system. However, this embodiment's usage of a row-ID column may table comparison inexpensive for the database management system. This is because of several reasons. First, the row-ID column value is on every row of a table. Second, the row-ID column value can already propagated to the replica table in form replication log entries as discussed in the embodiment in FIG. 2. Third, the database management system only has to compare the row-ID comparison values during comparison of the source and replica tables. Fourth, the comparison is performance inexpensive for the database management system where the database is a column-oriented database. This is because only the row-ID column needs to be compared and a column-oriented database management system only needs to scan the relevant column (e.g. row-ID column) when performing an operation. This is in contrast to a row-oriented database management system which must scan the columns of the rows it is reading. In other words, column oriented storage minimizes overhead because it's quite fast to extract values (e.g. row-ID column values) of only a specific column in a table. Finally, performing table comparison according to this embodiment does not create significant overhead for the database management system because a replication restart happens very rarely. Specifically, a replication restart being performed by the database management system according to this row-ID embodiment is rare because it is triggered on a node restart or after a node enters into an exceptional state. Therefore, it is better to optimize the common case (e.g. reducing the run-time overhead by removing a persistent replication log queue) by adding reasonable overhead to a rare case (e.g. node restart or node enters in an exceptional state). FIG. 9 is a flowchart for a method 900 for row-ID based lost change detection and synchronization, according to an embodiment. Method 900 can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In step 902 of FIG. 9, the database management system detects a restart at either the source node or replica node. For example, the database management system may detect that the source node or replica node crashed. In step 904 of FIG. 9, the database management system compares the row-ID column value of each row at the source table to the corresponding row-ID column value of the corresponding row at the replica table. In step 906 of FIG. 9, if the database management system finds the same row-ID column value at a row in the source table and the corresponding row in the replica table, the database management system will perform no action. In step 908 of FIG. 9, if the database management system finds a row-ID column value for a row only at the source table, the database management system will reinsert the row from the source table to the replica table. As would be appreciated by a person of ordinary skill in the art, the database management system may perform this reinsertion according to various methods. In step 910 of FIG. 9, if the database management system finds a row-ID column value for a row only at the replica table, the database management system will delete the row at the replica table. Another technical problem with lost change detection and re-synchronization is that it can happen while the database management system is accepting new write database transactions at the source table. More specifically, if there are any incoming writes at the source table during lost change detection and re-synchronization, the incoming changes and existing lost change become mixed. This is a problem because the database management system cannot determine the ordering of these changes. In an example embodiment for synchronizing incoming write database transactions during lost change detection and resynchronization, the database management system can lock the source table until the lost change and resynchronization is complete. However, this is often disruptive for a heavily utilized database management system because the database becomes unavailable to
{'application_number': '<application-reference appl-type="utility">\n<document-id>\n<country>US</country>\n<doc-number>14657854</doc-number>\n<date>20150313</date>\n</document-id>\n</application-reference>', 'publication_number': '<publication-reference>\n<document-id>\n<country>US</country>\n<doc-number>20160147858</doc-number>\n<kind>A1</kind>\n<date>20160526</date>\n</document-id>\n</publication-reference>', 'source_file': './application/2016/ipa160526.xml', 'source_data': 'USPTO-Application'}