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d in which that column is set tagged columns are columns that take no space whatsoever if they are not set in a record they may be single valued but can also be multi valued the same tagged column may have multiple values in a single record when tagged columns are set in a record each instance of a tagged column takes approximately 4 bytes of space in addition to the size of the tagged column instance value when the number of instances of a single tagged column is large the overhead per tagged column instance is approximately 2 bytes tagged columns are ideal for sparse columns because they take no space whatsoever if they are not set if a multi valued tagged column is indexed the index will contain one entry for the record for each value of the tagged column for a given table columns fall into one of two categories those which either occur exactly once in each of the records with possibly a few null values and those which occur rarely or which may have multiple occurrences in a single record fixed and variable columns belong to the former category while tagged columns belong to the latter the internal representation of the two column categories is different and it is important to understand the trade offs between the column categories fixed and variable columns are typically represented in every record even when the occurrence has a null value these columns can be quickly addressed via an offset table tagged column occurrences are preceded by a column identifier and the column is located by binary searching the set of tagged columns long values edit column types of long text and long binary are large binary objects they are stored in separate b tree from the clustered index keyed by long value id and byte offset ese supports append byte range overwrite and set size for these columns also ese has a single instance store feature where multiple records may reference the same large binary object as though each record had its own copy of the information i e without inter record locking conflicts the maximum size of a long text or long binary column value is 2 gb version auto increment and escrow columns edit version columns are automatically incremented by ese each time a record containing this column is modified via an update operation this column cannot be set by the application but can only be read applications of version columns include being used to determine if an in memory copy of a given record needs to be refreshed if the value in a table record is greater than the value in a cached copy then the cached copy is known to be out of date version columns must be of type long auto increment columns are automatically set by ese such that the value contained in the column is unique for every record in the table these columns like version columns cannot be set by the application auto increment columns are read only and are automatically set when a new record is inserted into a table via an update operation the value in the column remains constant for the life of the record and only one auto increment column is allowed per table auto increment columns may be of type long or type currency escrow columns can be modified via an escrowupdate operation escrowed updates are numeric delta operations escrow columns must be of type long examples of numeric delta operations include adding 2 to a value or subtracting 1 from a value ese tracks the change in a value rather than the end value of an update multiple sessions may each have outstanding changes made via escrowupdate to the same value because ese can determine the actual end value regardless of which transactions commit and which transactions rollback this allows multiple users to concurrently update a column by making numeric delta changes optionally database engine can erase records with zero value of the column a common use for such escrow column is reference counter many threads increment decrement the value without locks and when the counter reaches zero the record automatically gets deleted indexes edit an index is a persisted ordering of records in a table indexes are used for both sequential access to rows in the order defined and for direct record navigation based on indexed column values the order defined by an index is described in terms of an array of columns in precedence order this array of columns is also called the index key each column is called an index segment each index segment may be either ascending or descending in terms of its ordering contribution any number of indexes may be defined for a table ese provides a rich set of indexing features clustered indexes edit one index may be specified as the clustered or primary index in ese the clustered index must be unique and is referred to as the primary index other indexes are described as non clustered or secondary indexes primary indexes are different from secondary indexes in that the index entry is the record itself and not a logical pointer to the record secondary indexes have primary keys at their leaves to logically link to the record in the primary index in other words the table is physically clustered in primary index order retrieval of non indexed record data in primary index order is generally much faster than in secondary index order this is because a single disk access can bring into memory multiple records that will be access close together in time the same disk access satisfies multiple record access operations however the insertion of a record into the middle of an index as determined by the primary index order may be very much slower than appending it to the end of an index update frequency must be carefully considered against retrieval patterns when performing table design if no primary index is defined for a table then an implicit primary index called a database key dbk index is created the dbk is simply a unique ascending number incremented each time a record is inserted as a result the physical order of records in a dbk index is chronological insertion order and new records are always added at the end of the table if an application wishes to cluster data on a non unique index this is possible by adding an autoincrement column to the end of the non unique index definition indexing over multi valued columns edit indexes can be defined over multi valued columns multiple entries may exist in these indexes for records with multiple values for the indexed column multi valued columns may be indexed in conjunction with single valued columns when two or more multi valued columns are indexed together then the multi valued property is only honored for the first multi value column in the index lower precedence columns are treated as though they were single valued sparse indexes edit indexes can also be defined to be sparse sparse indexes do not have at least one entry for each record in the table there are a number of options in defining a sparse index options exist to exclude records from indexes when an entire index key is null when any key segment is null or when just the first key segment is null indexes can also have conditional columns these columns never appear within an index but can cause a record not to be indexed when the conditional column is either null or non null tuple indexes edit indexes can also be defined to include one entry for each sub string of a text or long text column these indexes are called tuple indexes they are used to speed queries with sub string matching predicates tuple indexes can only be defined for text columns for example if a text column value is i love jet blue and the index is configured to have a minimum tuple size of 4 characters and a maximum tuple length of 10 characters then the following sub strings will be indexed i love jet love jet love jet b ove jet bl ve jet blu e jet blue jet blue jet blue et blue t blue blue blue even though tuple indexes can be very large they can significantly speed queries of the form find all records containing jet blue they can be used for sub strings longer than the maximum tuple length by dividing the search sub string into maximum tuple length search strings and intersecting the results they can be used for exact matches for strings as long as the maximum tuple length or as short as the minimum tuple length with no index intersection for more information on performing index intersection in ese see index intersection tuple indexes cannot speed queries where the search string is shorter than the minimum tuple length transactions edit a transaction is a logical unit of processing delimited by begintransaction and committransaction or rollback operations all updates performed during a transaction are atomic they either all appear in the database at the same time or none appear any subsequent updates by other transactions are invisible to a transaction however a transaction can update only data that has not changed in the meantime else the operation fails at once without waiting read only transactions never need to wait and update transactions can interfere only with one another updating transaction transactions which are terminated by rollback or by a system crash leave no trace on the database in general the data state is restored on rollback to what it was prior to begintransaction transactions may be nested up to 7 levels with one additional level reserved for ese internal use this means that a part of a transaction may be rolled back without need to roll back the entire transaction a committransaction of a nested transaction merely signifies the success of one phase of processing and the outer transaction may yet fail changes are committed to the database only when the outermost transaction is committed this is known as committing to transaction level 0 when the transaction commits to transaction level 0 data describing the transaction is synchronously flushed to the log to ensure that the transaction will be completed even in the event of a subsequent system crash synchronously flushing the log makes ese transactions durable however in some cases application wish to order their updates but not immediately guarantee that changes will be done here applications can commit changes with jet_bitindexlazyflush ese supports a concurrency control mechanism called multi versioning in multi versioning every transaction queries a consistent view of the entire database as it was at the time the transaction started the only updates it encounters are those made by it in this way each transaction operates as though it was the only active transaction running on the system except in the case of write conflicts since a transaction may make changes based on data read that has already been updated in another transaction multi versioning by itself does not guarantee serializable transactions however serializability can be achieved when desired by simply using explicit record read locks to lock read data that updates are based upon both read and write locks may be explicitly requested with the getlock operation in addition an advanced concurrency control feature known as escrow locking is supported by ese escrow locking is an extremely concurrent update where a numeric value is changed in a relative fashion i e by adding or subtracting another numeric value escrow updates are non conflicting even with other concurrent escrow updates to the same datum this is possible because the operations supported are commutable and can be independently committed or rolled back as a result they do not interfere with concurrent update transactions this feature is often used for maintained aggregations ese also extends transaction semantics from data manipulation operations to data definition operations it is possible to add an index to a table and have concurrently running transactions update the same table without any transaction lock contention whatsoever later when these transactions are complete the newly created index is available to all transactions and has entries for record updates made by other transactions that could not perceive the presence of the index when the updates took place data definition operations may be performed with all the features expected of the transaction mechanism for record updates data definition operations supported in this fashion include addcolumn deletecolumn createindex deleteindex createtable and deletetable cursor navigation and the copy buffer edit a cursor is a logical pointer within a table index the cursor may be positioned on a record before the first record after the last record or even between records if a cursor is positioned before or after a record there is no current record it is possible to have multiple cursors into the same table index many record and column operations are based on the cursor position cursor position can be moved sequentially by move operations or directly using index keys with seek operations cursors can also be moved to a fractional position within an index in this way the cursor can be quickly moved to a thumb bar position this operation is performed with the same speed as a seek operation no intervening data must be accessed each cursor has a copy buffer in order to create a new record or modify an existing record column by column this is an internal buffer whose contents can be changed with setcolumns operations modifications of the copy buffer do not automatically change the stored data the contents of the current record can be copied into the copy buffer using the prepareupdate operation and update operations store the contents of the copy buffer as a record the copy buffer is implicitly cleared on a transaction commit or rollback as well as on navigation operations retrievecolumns may be used to retrieve column data either from the record or from the copy buffer if one exists query processing edit ese applications invariably query their data this section of the document describes features and techniques for applications to write query procession logic on ese sorts and temporary tables edit ese provides a sort capability in the form of temporary tables the application inserts data records into the sort process one record at a time and then retrieves them one record at a time in sorted order sorting is actually performed between the last record insertion and the first record retrieval temporary tables can be used for partial and complete result sets as well these tables can offer the same features as base tables including the ability to navigate sequentially or directly to rows using index keys matching the sort definition temporary tables can also be updatable for computation of complex aggregates simple aggregates can be computed automatically with a feature similar to sorting where the desired aggregate is a natural result of the sort process covering indexes edit retrieving column data directly from secondary indexes is an important performance optimization columns may be retrieved directly from secondary indexes without accessing the data records via the retrievefromindex...
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