1 Introduction to Oracle Database In-Memory

Oracle Database In-Memory (Database In-Memory) is a suite of features that greatly improves performance for real-time analytics and mixed workloads. The In-Memory Column Store (IM column store) is the key feature of Database In-Memory.

Note:

Database In-Memory features require the Oracle Database In-Memory option. For the Database In-Memory Base Level, the IM column store size is limited to 16 GB at the CDB level. See Oracle Database Licensing Information User Manual for details on which features are supported for different editions and services.

1.1 Changes in Oracle Database Release 21c for the In-Memory Guide

The following major features are new in this release.

• Database In-Memory Base Level

  Enable the Database In-Memory Base Level by setting the INMEMORY_FORCE initialization parameter to BASE_LEVEL. The Base Level enables you to experiment with In-Memory features without purchasing the Oracle Database In-Memory option.

  When the Base Level is enabled, the IM column store size is limited to 16 GB for a CDB and for every database instance in an Oracle RAC database. Also, the compression level for all objects and columns is set to QUERY LOW automatically and transparently, and Automatic In-Memory is disabled.

  See "Enabling the IM Column Store for a CDB or PDB" and Oracle Database Licensing Information User Manual.

• Automatic In-Memory enhancements

  When the INMEMORY_AUTOMATIC_LEVEL initialization parameter is set to HIGH, all segments that do not have a pre-existing INMEMORY setting are automatically marked INMEMORY MEMCOMPRESS AUTO. You do not need to have a thorough knowledge of the workload, decide which objects to enable for In-Memory access, and then populate them manually. The IM column store is largely self-managing.

  See "Automating Management of In-Memory Objects".

• Database In-Memory external table enhancements

  The INMEMORY clause is supported at the table level and partition level of a partitioned external table or hybrid external table. For hybrid tables, the table-level INMEMORY attribute applies to all partitions, whether internal or external.

  See "In-Memory External Tables" and "Creating an In-Memory Partitioned External Table: Example".

• In-Memory full text columns

  You can apply the INMEMORY TEXT clause to non-scalar columns in an In-Memory table. This clause enables fast In-Memory searching of text, XML, or JSON documents using the CONTAINS() or JSON_TEXTCONTAINS() operators. When the IM column store contains both scalar and non-scalar columns, OLAP applications that access both types of data can avoid accessing row-based storage, thereby improving performance.

  See "IM Full Text Columns".

• In-Memory hybrid scans

  Oracle Database supports In-Memory hybrid scans on tables populated when not all columns have been populated in the IM column store. A query is eligible for an In-Memory hybrid scan when some columns in the SELECT list are NO INMEMORY and all columns in the predicate are INMEMORY. In-Memory hybrid scans access some data from the IM column store, and some data from the row store, potentially improving performance by orders of magnitude over pure row store queries.

  See "In-Memory Hybrid Scans".

• In-Memory deep vectorization

  The In-Memory deep vectorization framework optimizes complex SQL operators such as joins using SIMD vector processing techniques. This feature is enabled by default, but can be disabled by setting the INMEMORY_DEEP_VECTORIZATION initialization parameter to false.

  See "Optimizing Joins Using In-Memory Deep Vectorization".

• JSON data type

  The JSON data type represents a JSON document in an Oracle proprietary binary format. This format is optimized for query and DML processing and can yield performance improvements for JSON processing in the IM column store.

  See "SIMD Access for JSON Data" and "Static Expressions: Binary JSON Columns".

• Spatial Support for Database In-Memory

  You can perform spatial filter operations (SDO_FILTER) on spatial tables stored in the IM column store. To achieve faster query performance, you no longer need to create and maintain spatial indexes for In-Memory spatial tables.

  See "Enabling IM Virtual Columns".

1.2 Challenges for Analytic Applications

Traditionally, obtaining good performance for analytic queries meant satisfying several requirements.

In a typical data warehouse or mixed-use database, requirements include the following:

• You must understand user access patterns.

• You must provide good performance, which typically requires creating indexes, materialized views, and OLAP cubes.

For example, if you create 1 to 3 indexes for a table (1 primary key and 2 foreign key indexes) to provide good performance for an OLTP application, then you may need to create additional indexes to provide good performance for analytic queries.

Figure 1-1 Multiple Indexes

Description of "Figure 1-1 Multiple Indexes"

Meeting the preceding requirements creates manageability and performance problems. Additional access structures cause performance overhead because you must create, manage, and tune them. For example, inserting a single row into a table requires an update to all indexes on this table, which increases response time.

The demand for real-time analytics means that more analytic queries are being executed in a mixed-workload database. The traditional approach is not sustainable.

1.3 The Single-Format Approach

Traditionally, relational databases store data in either row or columnar formats. Memory and disk store data in the same format.

An Oracle database stores rows contiguously in data blocks. For example, in a table with three rows, an Oracle data block stores the first row, and then the second row, and then the third row. Each row contains all column values for the row. Data stored in row format is optimized for transaction processing. For example, updating all columns in a small number of rows may modify only a small number of blocks.

To address the problems relating to analytic queries, some database vendors have introduced a columnar format. A columnar database stores selected columns—not rows—contiguously. For example, in a large sales table, the sales IDs reside in one column, and sales regions reside in a different column.

Analytical workloads access few columns while scanning, but scan the entire data set. For this reason, the columnar format is the most efficient for analytics. Because columns are stored separately, an analytical query can access only required columns, and avoid reading inessential data. For example, a report on sales totals by region can rapidly process many rows while accessing only a few columns.

Database vendors typically force customers to choose between a columnar and row-based format. For example, if the data format is columnar, then the database stores data in columnar format both in memory and on disk. Gaining the advantages of one format means losing the advantages of the alternate format. Applications either achieve rapid analytics or rapid transactions, but not both. The performance problems for mixed-use databases are not solved by storing data in a single format.

1.4 The Oracle Database In-Memory Solution

The Oracle Database In-Memory (Database In-Memory) feature set includes the In-Memory Column Store (IM column store), advanced query optimizations, and availability solutions.

The Database In-Memory optimizations enable analytic queries to run orders of magnitude faster on data warehouses and mixed-use databases.

1.4.1 What Is Database In-Memory?

The Database In-Memory feature set includes the IM column store, advanced query optimizations, and availability solutions.

Database In-Memory features combine to accelerate analytic queries by orders of magnitude without sacrificing OLTP performance or availability.

See Also:

Oracle Database Licensing Information User Manual to learn about the Database In-Memory option

1.4.1.1 IM Column Store

The IM column store maintains copies of tables, partitions, and individual columns in a compressed columnar format that is optimized for rapid scans.

The IM column store stores the data for each table or view by column rather than by row. Each column is divided into separate row subsets. A container called an In-Memory Compression Unit (IMCU) stores all columns for a subset of rows in a table segment.

Video:

Storage in the SGA

The IM column store resides in the In-Memory Area, which is an optional portion of the system global area (SGA). The IM column store does not replace row-based storage or the database buffer cache, but supplements it. The database enables data to be in memory in both a row-based and columnar format, providing the best of both worlds. The IM column store provides an additional transaction-consistent copy of table data that is independent of the disk format.

Figure 1-2 Dual-Format Database

Description of "Figure 1-2 Dual-Format Database"

Note:

Objects populated in the IM column store do not also need to be loaded into the buffer cache.

Population of Objects in the IM Column Store

In-Memory population is the automatic transformation of row-based data on disk into columnar data in the IM column store. When the INMEMORY_AUTOMATIC_LEVEL initialization parameter is set to HIGH, the database automatically decides the optimal segments and columns to populate in the IM column store, evicting infrequently accessed segments. No user decision-making is required.

Alternatively, you can manage the IM column store manually, specifying the INMEMORY clause at the object or column level, and then choosing when to populate objects. You can specify the INMEMORY clause at any of the following levels, listed from lowest level to highest level:

• Column (nonvirtual or virtual)

• Table partition (internal or external)

• Table (internal or external) or materialized view

• Tablespace

For any object, you can configure all or a subset of its columns for population. Similarly, for a partitioned table or materialized view, you can configure all or a subset of the partitions for population.

See Also:

• "In-Memory Column Store Architecture"

• "Enabling the IM Column Store for a CDB or PDB"

• "Automating Management of In-Memory Objects"

• Oracle Database SQL Language Reference for more information about the INMEMORY clause

1.4.1.2 Advanced Query Optimizations

Database In-Memory includes several performance optimizations for analytic queries.

Optimizations include:

• An expression is a combination of one or more values, operators, and SQL functions (DETERMINISTIC only) that resolve to a value. By default, the In-Memory Expression (IM expression) optimization enables the DBMS_INMEMORY_ADMIN.IME_CAPTURE_EXPRESSIONS procedure to identify and populate “hot” expressions in the IM column store. An IM expression is materialized as a hidden virtual column, but is accessed in the same way as a non-virtual column.

• A join group is a user-defined object that specifies a set of columns frequently used to join a set of tables. In certain queries, join groups enable the database to eliminate the performance overhead of decompressing and hashing column values.

• For aggregation queries that join small dimension tables to a large fact table, In-Memory Aggregation (IM aggregation) uses the VECTOR GROUP BY operation to enhance performance. This optimization aggregates data during the scan of the fact table rather than afterward.

• In the IM column store, repopulation is the automatic update of IMCUs after the data within them has been significantly modified. If an IMCU has stale entries but does not meet the staleness threshold, then background processes may instigate trickle repopulation, which is the gradual repopulation of the IM column store.

1.4.1.3 High Availability Support

Availability is the degree to which an application, service, or function is accessible on demand.

Database In-Memory supports the following availability features:

• In-Memory FastStart (IM FastStart) reduces the time to populate data into the IM column store when a database instance restarts. IM FastStart achieves this by periodically saving a copy of the data currently populated in the IM column store on the disk in its compressed columnar format.

• Each node in an Oracle Real Application Clusters (Oracle RAC) environment has its own IM column store. It is possible to have completely different objects populated on every node, or to have larger objects distributed across all IM column stores in the cluster. In Engineered Systems, it is also possible to have the same objects appear in the IM column store on every node.

• Starting in Oracle Database 12c Release 2 (12.2), an IM column store is supported on a standby database in an Active Data Guard environment.

1.4.2 Improved Performance for Analytic Queries

The compressed columnar format enables faster scans, queries, joins, and aggregates.

1.4.2.1 Improved Performance for Data Scans

The columnar format provides fast throughput for scanning large amounts of data.

The IM column store enables you to analyze data in real time, enabling you to explore different possibilities and perform iterations. Specifically, the IM column store can drastically improve performance for queries that do the following:

• Scan many rows and applies filters that use operators such as <, >, =, and IN

• Select few columns from a table or a materialized view that has many columns, such as a query that accesses 5 out of 100 columns

• Enable fast In-Memory searching of text, XML, or JSON documents when queries specify the CONTAINS() or JSON_TEXTCONTAINS() operators

Note:

"IM Full Text Columns"

1.4.2.1.1 Pure In-Memory Scans

In a pure In-Memory scan, all data is accessed from the IM column store.

Scans of the IM column store are faster than scans of row-based data for the following reasons:

• Elimination of buffer cache overhead

  The IM column store stores data in a pure, In-Memory columnar format. The data does not persist in the data files or generate redo, so the database avoids the overhead of reading data from disk into the buffer cache.

• Data pruning

  The database scans only the columns necessary for the query rather than entire rows of data. Furthermore, the database uses storage indexes and an internal dictionary to read only the necessary IMCUs for a specific query. For example, if a query requests all sales for a store with a store ID less than 8, then the database can use IMCU pruning to eliminate IMCUs that do not contain this value.

• Compression

  Traditionally, the goal of compression is to save space. In the IM column store, the goal of compression is to accelerate scans. The database automatically compresses columnar data using algorithms that allow WHERE clause predicates to be applied against the compressed formats. Depending on the type of compression applied, Oracle Database can scan data in its compressed format without decompressing it first. Therefore, the volume of data that the database must scan in the IM column store is less than the corresponding volume in the database buffer cache.

• Vector processing

  Each CPU core scans local in-memory columns. To process data as an array, the scans use SIMD (single instructional, multiple data) vector instructions. For example, a query can read a set of values in a single CPU instruction rather than read the values one by one. Vector scans by a CPU core are orders of magnitude faster than row scans.

For example, suppose a user executes the following ad hoc query:

SELECT cust_id, time_id, channel_id
FROM   sales
WHERE  prod_id BETWEEN 14 and 29
ORDER BY 1, 2, 3;

When using the buffer cache, the database would typically scan an index to find the product IDs, use the rowids to fetch the rows from disk into the buffer cache, and then discard the unwanted column values. Scanning data in row format in the buffer cache requires many CPU instructions, and can result in suboptimal CPU efficiency.

When using the IM column store, the database can scan only the requested sales columns, avoiding disk altogether. Scanning data in columnar format pipelines only necessary columns to the CPU, increasing efficiency. Each CPU core scans local in-memory columns using SIMD vector instructions.

Video:

1.4.2.1.2 In-Memory Hybrid Scans

An In-Memory hybrid scan retrieves rows from both the IM column store and row store.

Using the selective columns feature, you can enable a subset of columns in an object for In-Memory access. For example, if the only sales columns specified in application queries are prod_id, cust_id, and amount_sold, then you might decide to save memory by applying the INMEMORY attribute to only these columns. However, a user might issue the following ad hoc query:

SELECT prod_id, time_id FROM sales WHERE cust_id IN (100,200,300);

Because time_id is a NO INMEMORY column, the query must retrieve data from the row store, possibly reducing performance. However, the optimizer can consider an In-Memory hybrid scan because the following conditions are met:

• All columns in the predicate are INMEMORY. In this example, cust_id is the only predicate column, and it is INMEMORY.

• The SELECT list contains an arbitrary mix of NO INMEMORY and INMEMORY columns. In this example, prod_id is INMEMORY, but time_id is NO INMEMORY.

Within a single table scan of sales, an In-Memory hybrid scan filters data in the IM column store and projects data from the row store. In this way, an In-Memory hybrid scan can increase response time by orders of magnitude.

Note:

"In-Memory Hybrid Scans"

1.4.2.2 Improved Performance for Joins

A Bloom filter is a low-memory data structure that tests membership in a set. The IM column store takes advantage of Bloom filters to improve the performance of joins.

Bloom filters speed up joins by converting predicates on small dimension tables to filters on large fact tables. This optimization is useful when performing a join of multiple dimensions with one large fact table. The dimension keys on fact tables have many repeat values. The scan performance and repeat value optimization speeds up joins by orders of magnitude.

See Also:

"About In-Memory Joins"

1.4.2.3 Improved Performance for Aggregation

An important aspect of analytics is to determine patterns and trends by aggregating data. Aggregations and complex SQL queries run faster when data is stored in the IM column store.

In Oracle Database, aggregation typically involves a GROUP BY clause. Traditionally, the database used SORT and HASH operators. Starting in Oracle Database 12c Release 1 (12.1), the database offered VECTOR GROUP BY transformations to enable efficient in-memory, array-based aggregation.

During a fact table scan, the database accumulates aggregate values into in-memory arrays, and uses efficient algorithms to perform aggregation. Joins based on the primary key and foreign key relationships are optimized for both star schemas and snowflake schemas.

See Also:

• "Optimizing In-Memory Aggregation with VECTOR GROUP BY"

• Oracle Database Data Warehousing Guide to learn more about SQL aggregation

1.4.3 Improved Performance for Mixed Workloads

Although OLTP applications do not benefit from accessing data in the IM column store, the dual-memory format can indirectly improve OLTP performance.

When all data is stored in rows, improving analytic query performance requires creating access structures. The standard approach is to create analytic indexes, materialized views, and OLAP cubes. For example, a table might require 3 indexes to improve the performance of the OLTP application (1 primary key and 2 foreign key indexes) and 10-20 additional indexes to improve the performance of the analytic queries. While this technique can improve analytic query performance, it slows down OLTP performance. Inserting a row into the table requires modifying all indexes on the table. As the number of indexes increases, insertion speed decreases.

When you populate data into the IM column store, you can drop analytic access structures. This technique reduces storage space and processing overhead because fewer indexes, materialized views, and OLAP cubes are required. For example, an insert results in modifying 1-3 indexes instead of 11-23 indexes.

While the IM column store can drastically improve performance for analytic queries in business applications, ad hoc analytic queries, and data warehouse workloads, pure OLTP databases that perform short transactions using index lookups benefit less. The IM column store does not improve performance for the following types of queries:

• A query with complex predicates

• A query that selects many columns

• A query that returns many rows

See Also:

Oracle Database Data Warehousing Guide to learn more about physical data warehouse design

1.4.4 In-Memory Support for Exadata Flash Cache

Not all objects marked INMEMORY may fit in DRAM memory at the same time. If you use Oracle Exadata Storage Server Software, then Exadata Smart Flash Cache can serve as supplemental memory.

When the IM column store is enabled, Exadata Smart Flash Cache reformats data automatically into In-Memory columnar format. In previous Exadata releases, only Hybrid Column Compressed data was eligible for flash storage in IM columnar format. The reformatting occurs for both compressed (including OLTP compression) and uncompressed tables.

Note:

If Database In-Memory Base Level is enabled, then the CELLMEMORY feature is disabled for Oracle Exadata.

With this format, most Database In-Memory performance enhancements are supported in Smart Scan, including joins and aggregation. Also, reformatting uncompressed and OLTP-compressed data blocks into IM columnar format can significantly reduce the amount of flash memory required.

Exadata Smart Flash Cache transforms the data in the following stages:

1. Oracle Exadata caches data from eligible scans in a legacy columnar format so that the data is available immediately. This format is columnar, but it is not the same format used by the IM column store.

2. In the background, Oracle Exadata reformats data into the pure IM column store format at a lower priority. The background writes prevent interference with the main workload.

If the database is not running an OLTP workload, then a data warehousing workload can consume 100% of the flash cache. However, an OLTP workload limits the data warehouse workload to no more than 50% of the flash cache. This optimization ensures that OLTP workload performance is not sacrificed for analytic scans.

By default, Exadata Smart Flash Cache compresses data using the level MEMCOMPRESS FOR CAPACITY LOW. To change the compression level or disable the columnar format altogether, use the ALTER TABLE ... NO CELLMEMORY statement.

See Also:

• "Enabling the IM Column Store for a CDB or PDB"

• "CPU Architecture: SIMD Vector Processing"

• Oracle Exadata Database Machine System Overview to learn more about the CELLMEMORY attribute

• Oracle Database Licensing Information User Manual for details on which features are supported for different editions and services

1.4.5 High Availability Support

The IM column store is fully integrated into Oracle Database. All High Availability features are supported.

The columnar format does not change the Oracle database on-disk storage format. Thus, buffer cache modifications and redo logging function in the same way. Features such as RMAN, Oracle Data Guard, and Oracle ASM are fully supported.

In an Oracle Real Application Clusters (Oracle RAC) environment, each node has its own IM column store by default. Depending on your requirements, you can populate objects in different ways:

• Different tables are populated on every node. For example, the sales fact table is on one node, whereas the products dimension table is on a different node.

• A single table is distributed among different nodes. For example, different partitions of the same hash-partitioned table are on different nodes, or different rowid ranges of a single nonpartitioned table are on different nodes.

• Some objects appear in the IM column store on every node. For example, you might populate the products dimension table in every node, but distribute partitions of the sales fact table across different nodes.

See Also:

"High Availability and the IM Column Store"

1.4.6 Ease of Adoption

Database In-Memory is simple to implement, and requires no application changes.

Key aspects of Database In-Memory adoption include:

• Ease of deployment

  No user-managed data migration is required. The database stores data in row format on disk and automatically converts row data into columnar format when populating the IM column store.

• Compatibility with existing applications

  No application changes are required. The optimizer automatically takes advantage of the columnar format. If your application connects to the database and issues SQL, then it can benefit from Database In-Memory features.

• Full SQL compatibility

  Database In-Memory places no restrictions on SQL. Analytic queries can benefit whether they use Oracle analytic functions or customized PL/SQL code.

• Ease of setup

  No complex setup is required. The INMEMORY_SIZE initialization parameter specifies the amount of memory reserved for use by the IM column store. By configuring the IM column store, you can immediately improve the performance of existing analytic workloads and ad hoc queries.

• Ease of object management

  Automatic In-Memory uses access tracking and column statistics to manage objects in the IM column store. When the INMEMORY_AUTOMATIC_LEVEL initialization parameter is set to HIGH, the database automatically decides the optimal segments and columns to retain in the IM column store, evicting "cold" (infrequently accessed) segments. No user decision-making is required.

  Note:

  If the INMEMORY_FORCE initialization parameter is set to BASE_LEVEL, then Automatic In-Memory is disabled even if INMEMORY_AUTOMATIC_LEVEL is set. Even if tables have a compression level of AUTO, Automatic In-Memory background operations do not run.

• Optional fine-grained control of In-Memory objects and columns

  When INMEMORY_AUTOMATIC_LEVEL is not set to HIGH, the INMEMORY clause in DDL statements specifies the objects or columns to be populated into the IM column store. You can specify that only certain objects or certain columns are eligible for In-Memory population.

See Also:

• "Enabling and Sizing the IM Column Store" to learn how to enable the IM column store

• "Configuring Automatic In-Memory"

• Oracle Database Reference to learn about the INMEMORY_SIZE, INMEMORY_FORCE, and INMEMORY_AUTOMATIC_LEVEL initialization parameters

1.5 Requirements for Database In-Memory

The Oracle Database In-Memory option is required for all Database In-Memory features. The Database In-Memory Base Level is available for an IM column store that is 16 GB or less.

Requirements include:

• To use the Database In-Memory Base Level, the INMEMORY_FORCE initialization parameter must be set to BASE_LEVEL in the initialization parameter file at the CDB level. You cannot set this parameter dynamically, or set it at the PDB level. The BASE_LEVEL setting has the following consequences:

  • All INMEMORY objects and columns automatically and transparently use the compression level of QUERY LOW.

  • Automatic In-Memory is disabled.

• To use the CellMemory feature without incurring the overhead of creating an IM column store, set this parameter to CELLMEMORY_LEVEL.

  Note that if the value of INMEMORY_SIZE is greater than 0, then setting INMEMORY_FORCE=CELLMEMORY_LEVEL is equivalent to setting INMEMORY_FORCE=DEFAULT. In this case, the Database In-Memory option is enabled, even if you use CellMemory only.

• For the Base Level, the IM column store size must not exceed 16 GB.

• The IM column store requires a minimum of 100 MB of memory. The store size is included in MEMORY_TARGET.

• For Oracle RAC databases, if the INMEMORY_FORCE initialization parameter is set to BASE_LEVEL, then the column store size of each database is limited to 16 GB.

No special hardware is required for an IM column store.

See Also:

• "Estimating the Required Size of the IM Column Store"

• "Deploying IM Column Stores in Oracle RAC"

• Oracle Database Licensing Information User Manual for all licensing-related information for Database In-Memory

1.6 Principal Tasks for Database In-Memory

For queries to benefit from the IM column store, the only required task is sizing the IM column store. Query optimization and availability features require additional configuration.

Principal Tasks for Configuring the IM Column Store

The following table lists the principal configuration tasks.

Table 1-1 Configuration Tasks

Task	Notes	When Required	To Learn More
Enable the IM column store by specifying its size.	Set INMEMORY_SIZE to a minimum of 100 MB. For the Database In-Memory Base Level only, the size must be less than or equal to 16 GB for the entire CDB, and for each database instance in an Oracle RAC database. The COMPATIBLE initialization parameter must be set to 12.1.0 or higher.	Required for all Database In-Memory features	"Enabling the IM Column Store for a CDB or PDB"
For the Database In-Memory Base Level, perform additional configuration.	For the Database In-Memory Base Level only, the INMEMORY_FORCE initialization parameter must be set to BASE_LEVEL at the CDB level, and INMEMORY_SIZE must be less than or equal to 16 GB.	Required only for the Database In-Memory Base Level	"Enabling the IM Column Store for a CDB or PDB"
Configure Automatic In-Memory to enable, populate, and evict cold segments to ensure that the working data set is always populated	When the INMEMORY_AUTOMATIC_LEVEL initialization parameter is set to HIGH, Oracle Database uses internal usage statistics to manage the workload. For example, if the database determines that certain partitions of the sales table are frequently queried, then it enables them as INMEMORY and populates them. As the workload changes, and segments become "cold," they are replaced by hot segments. Note: If the INMEMORY_FORCE initialization parameter is set to BASE_LEVEL, then Automatic In-Memory is disabled even if INMEMORY_AUTOMATIC_LEVEL is set. Even if tables have a compression level of AUTO, Automatic In-Memory background operations do not run.	Required for fully automated management of Database In-Memory objects	"Configuring Automatic In-Memory"
Enable columns, partitions, tables or materialized views, or tablespaces for population into the IM column store.	Unless INMEMORY_AUTOMATIC_LEVEL is set to HIGH, all objects are NO INMEMORY by default. This means that they cannot be populated in the IM column store. Manually specifying the INMEMORY clause in a DDL statement enables an object for In-Memory access, that is, makes it eligible to be populated. Note: If the INMEMORY_FORCE initialization parameter is set to BASE_LEVEL, then INMEMORY objects and columns automatically use QUERY LOW compression. The data dictionary views may continue to show pre-existing compression settings, but the Base Level always transparently compresses objects and columns at the QUERY LOW level.	Required when INMEMORY_AUTOMATIC_LEVEL is not HIGH	"Enabling Objects for In-Memory Population Manually"
Populate objects into to the IM column store manually	Enabling an object for In-Memory access is a separate step from populating it. Unless INMEMORY_AUTOMATIC_LEVEL is set to HIGH, the population of an object depends on its INMEMORY ... PRIORITY setting. When set to NONE (default), you must manually populate the object using a query or PL/SQL call. It will not be populated otherwise. When INMEMORY ... PRIORITY is not set to NONE, the database automatically populates INMEMORY objects after instance startup based on their position in the queue. For example, objects with HIGH priority are populated before objects of LOW priority. In this case, you do not need to manually populate an object unless you want to override the queue.	Required when the PRIORITY setting is NONE	"Populating the IM Column Store Manually"
Create Automatic Data Optimization (ADO) policies to set INMEMORY attributes on objects in the IM column store.	For example, a policy can evict the sales table from the IM column store after 10 days of no access. In-Memory ADO features require that HEAT_MAP=ON is set to ON and INMEMORY_SIZE is set to a nonzero value.	Optional	"Enabling ADO for the IM Column Store"

Principal Tasks for Optimizing In-Memory Queries

In-Memory query optimizations are not required for the IM column store to function. The following optimization tasks are optional.

Table 1-2 Query Optimization Tasks

Task	Notes	To Learn More
Manage automatic detection of IM expressions in the IM column store by using the DBMS_INMEMORY_ADMIN package.	For example, invoke the IME_CAPTURE_EXPRESSIONS procedure to define the period in which the database can identify “hot” expressions, and then gradually populate them. The INMEMORY_EXPRESSIONS_USAGE initialization parameter controls the type of IM expression that the database can populate: static, dynamic, or both.	"INMEMORY_EXPRESSIONS_USAGE"
Define join groups using the CREATE INMEMORY JOIN GROUP statement.	Candidates are columns that are frequently paired in a join predicate, for example, a column joining a fact and dimension table.	"Creating Join Groups"
If necessary for a query block, specify the VECTOR_TRANSFORM hint to enable in-memory aggregation, or NO_VECTOR_TRANSFORM to disable it.	In-memory aggregation is an automatically enabled feature that cannot be controlled with initialization parameters or DDL.	"Controls for IM Aggregation"
Limit the number of IMCUs updated through trickle repopulation within a two minute interval by setting the initialization parameter INMEMORY_TRICKLE_REPOPULATE_SERVERS_PERCENT.	You can disable trickle repopulation by setting this initialization parameter to 0.	"Threshold-Based and Trickle Repopulation"

Principal Tasks for Managing Availability

The principal tasks are shown in the following table.

Table 1-3 Availability Tasks

Task	Notes	To Learn More
Specify an In-Memory FastStart (IM FastStart) tablespace using the DBMS_INMEMORY_ADMIN.ENABLE_FASTSTART procedure.	IM FastStart optimizes the population of database objects in the IM column store when the database is restarted. IM FastStart stores information on disk for faster population of the IM column store.	"Enabling IM FastStart for the IM Column Store"
For an object or tablespace, specify INMEMORY in DDL statement with the DISTRIBUTE or DUPLICATE keywords to control the distribution of data in Oracle RAC.	By default, each In-Memory object is distributed among the Oracle RAC instances, effectively employing a share-nothing architecture for the IM column store.	"Deploying IM Column Stores in Oracle RAC"
In an Oracle Data Guard environment, you can use the same Database In-Memory initialization parameters and statements on a primary or standby database.	For example, you can enable the IM column store on both a primary and standby database by setting INMEMORY_SIZE. Optionally, use the INMEMORY DISTRIBUTE FOR SERVICE clause in DDL to populate a different set of data in the IM column store on the primary and standby databases.	"About Manually Enabling Objects for In-Memory Population"

1.7 Tools for the IM Column Store

No special tools or utilities are required to manage the IM column store or other Database In-Memory features. Administrative tools such as SQL*Plus, SQL Developer, and Oracle Enterprise Manager (Enterprise Manager) are fully supported.

This section describes tools that have specific Database In-Memory feature support.

1.7.1 In-Memory Advisor

The In-Memory Advisor is a downloadable PL/SQL package that analyzes the analytical processing workload in your database.

The In-Memory Advisor differentiates analytics processing from other database activity based on SQL plan cardinality, Active Session History (ASH), parallel query usage, and other statistics. The In-Memory Advisor estimates the size of objects in the IM column store based on statistics and heuristic compression factors.

The advisor estimates analytic processing performance improvement factors based on the following:

• Elimination of wait events such as user I/O waits, cluster transfer waits, and buffer cache latch waits

• Query processing advantages related to specific compression types

• Decompression cost heuristics for specific compression types

• SQL plan cardinality, number of columns in the result set, and so on

The output is a report that recommends a size for the IM column store and a list of objects that would benefit from In-Memory population. The advisor also generates a SQL*Plus script that alters the recommended objects with the INMEMORY clause.

The In-Memory Advisor is not included in the stored PL/SQL packages. You must download the package from Oracle Support.

See Also:

My Oracle Support note 1965343.1 to learn more about the In-Memory Advisor

1.7.2 Cloud Control Pages for the IM Column Store

Enterprise Manager Cloud Control (Cloud Control) provides the In-Memory Column Store Central Home page. This page gives a dashboard interface to the IM column store.

Use this page to monitor in-memory support for database objects such as tables, indexes, partitions and tablespaces. You can view In-Memory functionality for objects and monitor their In-Memory usage statistics. Unless otherwise stated, this manual describes the command-line interface to Database In-Memory features.

See Also:

"Using IM Column Store in Cloud Control" explains how to use Cloud Control to manage the IM column store.

1.7.3 Oracle Compression Advisor

Oracle Compression Advisor estimates the compression ratio that you can realize using the MEMCOMPRESS clause. The advisor uses the DBMS_COMPRESSION interface.

See Also:

• "Oracle Compression Advisor"

• Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_COMPRESSION

1.7.4 Oracle Data Pump and the IM Column Store

You can import database objects that are enabled for the IM column store using the TRANSFORM=INMEMORY:y option of the impdp command.

With this option, Oracle Data Pump keeps the IM column store clause for all objects that have one. When the TRANSFORM=INMEMORY:n option is specified, Data Pump drops the IM column store clause from all objects that have one.

You can also use the TRANSFORM=INMEMORY_CLAUSE:string option to override the IM column store clause for a database object in the dump file during import. For example, you can use this option to change the IM column store compression for an imported database object.

Video:

See Also:

Oracle Database Utilities for more information about the TRANSFORM impdb parameter