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Oracle Database In-Memory with Oracle Database 12c Release 2 Technical Overview ORACLE WHITE PAPER
Table of Contents Executive Overview
Oracle Database In-Memory Overview
Row Format vs. Column Format
The In-Memory Column Store
Dynamic Resizing and Automatic Memory Management
Populating the In-Memory Column Store
In-Memory Storage Index
SIMD Vector Processing
In-Memory Virtual Columns
Automatically Detected In-Memory Expressions
JSON Document Support
Join Groups In-Memory Aggregation DML and the In-Memory Column Store
ORACLE DATABASE IN-MEMORY WITH ORACLE DATABASE 12C RELEASE 2
14 16 18
Bulk Data Loads
Partition Exchange Loads
The In-Memory Column Store on RAC Distribute For Service
Support for rolling patches and upgrades
In-Memory Fault Tolerance
In-Memory FastStart on RAC
Controlling the Content of the In-Memory Column Store with ADO User-Defined ADO Policy
The In-Memory Column Store in a Multitenant Environment
The In-Memory Column Store in an Active Data Guard Environment
Restrictions on Active Data Guard
Extending In-Memory Columnar Format to Flash on Exadata
Controlling the Use of Database In-Memory
Core Initialization Parameters
Additional Initialization Parameters
Appendix A - Monitoring and Managing Oracle Database In-Memory
Monitoring Objects in the In-Memory Column Store
Managing IM Column Store Population CPU Consumption
ORACLE DATABASE IN-MEMORY WITH ORACLE DATABASE 12C RELEASE 2
Session Level Statistics
ORACLE DATABASE IN-MEMORY WITH ORACLE DATABASE 12C RELEASE 2
Executive Overview Oracle Database In-Memory is available with Oracle Database 12c Release 2 on-premises, Cloud at Customer and on Oracle Cloud. It adds in-memory functionality to Oracle Database 12c Release 2 for transparently accelerating analytic queries by orders of magnitude, enabling real-time business decisions. Using Database In-Memory, businesses can instantaneously run analytics and reports that previously took hours or days. Businesses benefit from better decisions made in real-time, resulting in lower costs, improved productivity, and increased competitiveness.
Oracle Database In-Memory accelerates both Data Warehouses and mixed workload OLTP databases, and is easily deployed under any existing application that is compatible with Oracle Database 12c Release 2 (12.2). No application changes are required. Database In-Memory uses Oracle’s mature scale-up, scale-out, and storage-tiering technologies to cost effectively run any size workload. Oracle’s industry leading availability and security features all work transparently with Oracle Database In-Memory, making it the most robust offering on the market.
"Oracle was ranked as a leader for its Database In-Memory and Oracle TimesTen in The Forrester WaveTM, Q1 2017. The full report is available on oracle.com.
The ability to easily perform real-time data analysis together with real-time transaction processing on all existing database workloads makes Oracle Database In-Memory ideally suited for the Cloud and on-premises because it requires no additional changes to the application. Oracle Database In-Memory enables organizations to transform into Real-Time Enterprises that quickly make data-driven decisions, respond instantly to customer demands, and continuously optimize all key processes.
Intended Audience Readers are assumed to have hands-on experience with Oracle Database technologies from the perspective of a DBA or performance specialist.
Introduction Today’s information architecture is much more dynamic than it was just a few years ago. Business users now demand more decision-enabling information, sooner. In order to keep up with increases in demand, companies are striving to run analytics directly on their operational systems, in addition to their data warehouses. This leads to a precarious balancing act between transactional workloads, subject to frequent inserts and updates, and reporting style queries that need to scan large amounts of data.
With Oracle Database In-Memory, a single database can now efficiently support mixed workloads, delivering optimal performance for transactions while simultaneously supporting real-time analytics and reporting. This is possible due to a unique "dual-format" architecture that enables data to be maintained in both the existing Oracle row format, for OLTP operations, and a new purely in-memory columnar format, optimized for analytical processing. Oracle Database In-Memory also enables data marts and data warehouses to provide more ad-hoc analytics, giving end-users the ability to run multiple business-driven queries in the same time it previously took to run just one query.
Embedding the in-memory column format into the existing Oracle Database software ensures full compatibility with ALL existing features, and no changes in the application. This makes it an ideal analytics platform in the Cloud. Applications can be moved to the Cloud and seamlessly take advantage of the performance of Oracle Database In-Memory's ability to provide real-time analytics. Companies striving to become real-time enterprises can more easily achieve their goals, regardless of what applications they are running. This paper describes the main components of Oracle Database InMemory and provides simple, reproducible examples to make it easy to get acquainted with them. It also outlines how Oracle Database In-Memory can be integrated into existing operational systems and data warehouse environments to improve both performance and manageability.
Oracle Database In-Memory Overview Row Format vs. Column Format Oracle Database has traditionally stored data in a row format. In a row format database, each new transaction or record stored in the database is represented as a new row in a table. That row is made up of multiple columns, with each column representing a different attribute about that record. A row format is ideal for online transaction systems, as it allows quick access to all of the columns in a record since all of the data for a given record are kept together inmemory and on-storage. A column format database stores each of the attributes about a transaction or record in a separate column structure. A column format is ideal for analytics, as it allows for faster data retrieval when only a few columns are selected but the query accesses a large portion of the data set. But what happens when a DML operation (insert, update or delete) occurs on each format? A row format is incredibly efficient for processing DML as it manipulates an entire record in one operation (i.e. insert a row, update a row or delete a row). A column format is not as efficient at processing DML, to insert or delete a single record in a column format all the columnar structures in the table must be changed. That could require one or more I/O operations per column. Database systems that support only one format suffer the tradeoff of either sub-optimal OLTP or sub-optimal analytics performance. Oracle Database In-Memory (Database In-Memory) provides the best of both worlds by allowing data to be simultaneously populated in both an in-memory row format (the buffer cache) and a new in-memory columnar format: a dual-format architecture. Note that the dual-format architecture does not double memory requirements. The in-memory columnar format should be sized to accommodate the objects that must be stored in memory. This is different than the buffer cache which has been optimized for decades to run effectively with a much smaller size than the size of the database. In practice, it is expected that the dual-format architecture will impose less than a 20% additional memory overhead. This is a small price to pay for optimal performance at all times for all workloads.
With Oracle’s unique approach, there remains a single copy of the table on storage, so there are no additional storage costs or synchronization issues. The database maintains full transactional consistency between the row and the columnar formats, just as it maintains consistency between tables and indexes. The Oracle Optimizer is fully aware of the columnar format: It automatically routes analytic queries to the columnar format and OLTP operations
to the row format, ensuring outstanding performance and complete data consistency for all workloads without any application changes.
The In-Memory Column Store Database In-Memory uses an In-Memory column store (IM column store), which is a new component of the Oracle Database System Global Area (SGA), called the In-Memory Area. Data in the IM column store does not reside in the traditional row format used by the Oracle Database; instead it uses a new columnar format. The IM column store does not replace the buffer cache, but acts as a supplement, so that data can now be stored in memory in both a row and a columnar format. The In-Memory area is sub-divided into two pools: a 1MB pool used to store the actual columnar formatted data populated into memory, and a 64K pool used to store metadata about the objects that are populated into the IM column store. The amount of available memory in each pool is visible in the V$INMEMORY_AREA view. The relative size of the two pools is determined by internal heuristics; the majority of the In-Memory area memory is allocated to the 1MB pool.
Figure 2. Details of the space allocation within the INMEMORY_AREA as seen in V$INMEMORY_AREA
Dynamic Resizing and Automatic Memory Management The size of the In-Memory area, within the SGA, is controlled by the initialization parameter INMEMORY_SIZE (default 0). The In-Memory area must have a minimum size of 100MB. The current size of the In-Memory area is visible in the view V$SGA. Starting in 12.2, it is possible to increase the size of the In-Memory area on the fly, by increasing the INMEMORY_SIZE parameter via an ALTER SYSTEM command, assuming there is spare memory within the SGA. The INMEMORY_SIZE parameter must be increased by 128MB or more in order for this change to take effect. It is not possible to shrink the size of the In-Memory area on the fly. A reduction in the size of the INMEMORY_SIZE parameter will not take effect until the database instance is restarted. It is important to note that the In-Memory area is not impacted or controlled by Oracle Automatic Memory Management (AMM).
Populating the In-Memory Column Store Not all of the objects in an Oracle database need to be populated in the IM column store. This is an advantage over so-called “pure” in-memory databases that require the entire database to be memory-resident. With Oracle Database In-Memory, the IM column store should be populated with the most performance-critical data in the database. Less performance-critical data can reside on lower cost flash or disk. Of course, if your database is small enough, you can populate all of your tables into the IM column store. Database In-Memory adds a new INMEMORY attribute for tables and materialized views. Only objects with the INMEMORY attribute are populated into the IM column store. The INMEMORY attribute can be specified on a tablespace, table, partition, subpartition, or materialized view. If it is enabled at the tablespace level, then all new tables and materialized views in the tablespace will be enabled for the IM column store by default.
ALTER TABLESPACE ts_data DEFAULT INMEMORY Figure 3. Enabling the INMEMORY attribute on the ts_data tablespace by specifying the INMEMORY attribute
By default, all of the columns in an object with the INMEMORY attribute will be populated into the IM column store. However, it is possible to populate only a subset of columns if desired. For example, the following statement sets the In-Memory attribute on the table SALES, in the SH sample schema, but it excludes the column PROD_ID. ALTER TABLE sales INMEMORY NO INMEMORY(prod_id) Figure 4. Enabling the In-Memory attribute on the sales table but excluding the prod_id column
Similarly, for a partitioned table, all of the table's partitions inherit the in-memory attribute but it is possible to populate just a subset of the partitions or subpartitions. To indicate an object is no longer a candidate, and to instantly remove it from the IM column store, simply specify the NO INMEMORY clause. ALTER TABLE sales MODIFY PARTITION SALES_Q1_1998 NO INMEMORY Figure 5. Disabling the In-Memory attribute on one partition of the sales table by specifying the NO INMEMORY clause
The IM column store is populated by a set of background processes referred to as worker processes (e.g. ora_w001_orcl). The database is fully active and accessible while this occurs. With a pure in-memory database, the database cannot be accessed until all of the data is populated into memory, which blocks availability until the population is complete. Each worker process is given a subset of database blocks from the object to populate into the IM column store. Population is a streaming mechanism, simultaneously columnizing and compressing the data. Just as a tablespace on disk is made up of multiple extents, the IM column store is made up of multiple In-Memory Compression Units (IMCUs). Each worker process allocates its own IMCU and populates its subset of database blocks in it. Data is not sorted or ordered in any specific way during population. It is read in the same order it appears in the row format. Objects are populated into the IM column store either in a prioritized list immediately after the database is opened or after they are scanned (queried) for the first time. The order in which objects are populated is controlled by the keyword PRIORITY, which has five levels (see figure 7). The default PRIORITY is NONE, which means an object is populated only after it is scanned for the first time. All objects at a given priority level must be fully populated before the population of any objects at a lower priority level can commence. However, the population order can be superseded if an object without a PRIORITY is scanned, triggering its population into IM column store. ALTER TABLE customers INMEMORY PRIORITY CRITICAL Figure 6. Enabling the In-Memory attribute on the customers table with a priority level of critical
Object is populated immediately after the database is opened
Object is populated after all CRITICAL objects have been populated, if space remains available in the IM column store
Object is populated after all CRITICAL and HIGH objects have been populated, and space remains available in the IM column store
Object is populated after all CRITICAL, HIGH, and MEDIUM objects have been populated, if space remains available in the IM column store
Objects only populated after they are scanned for the first time (Default), if space is available in the IM column store
Figure 7. Different priority levels controlled by the PRIORITY sub clause of the INMEMORY clause
Restrictions Almost all objects in the database are eligible to be populated into the IM column but there are a small number of exceptions. The following database objects cannot be populated in the IM column store:
Any object owned by the SYS user and stored in the SYSTEM or SYSAUX tablespace
Index Organized Tables (IOTs)
The following data types are also not supported in the IM column store:
LONGS (deprecated since Oracle Database 8i)
Out of line LOBS
All of the other columns in an object that contain these datatypes are eligible to be populated into the IM column store. Any query that accesses only the columns residing in the IM column store will benefit from accessing the table data via the column store. Any query that requires data from columns with a non-supported column type will be executed via the row store. Objects that are smaller than 64KB are not populated into memory, as they will waste a considerable amount of space inside the IM column store as memory is allocated in 1MB chunks.
In-Memory Compression In general, compression is considered only as a space-saving mechanism. However, data populated into the IM column store is compressed using a new set of compression algorithms that not only help save space but also improve query performance. The new Oracle In-Memory compression format allows queries to execute directly against the compressed columns. This means all scanning and filtering operations will execute on a much smaller amount of data. Data is only decompressed when it is required for the result set. In-memory compression is specified using the keyword MEMCOMPRESS, a sub-clause of the INMEMORY attribute. There are six levels, each of which provides a different level of compression and performance.
MEMCOMPRESS FOR QUERY LOW MEMCOMPRESS FOR QUERY HIGH MEMCOMPRESS FOR CAPACITY LOW MEMCOMPRESS FOR CAPACITY HIGH
Optimized for query performance (default) Optimized for query performance as well as space saving Balanced with a greater bias towards space saving Optimized for space saving
Figure 8. Different compression levels controlled by the MEMCOMPRESS sub-clause of the INMEMORY clause
By default, data is compressed using the FOR QUERY LOW option, which provides the best performance for queries. This option utilizes common compression techniques such as Dictionary Encoding, Run Length Encoding and BitPacking. The FOR CAPACITY options apply an additional compression technique on top of FOR QUERY compression, which can have a significant impact on performance as each entry must be decompressed before the WHERE clause predicates can be applied. The FOR CAPACITY LOW option applies a proprietary compression technique called OZIP that offers extremely fast decompression that is tuned specifically for Oracle Database. The FOR CAPACITY HIGH option applies a heavier-weight compression algorithm with a larger penalty on decompression in order to provide higher compression. Compression ratios can vary from 2X – 20X, depending on the compression option chosen, the datatype, and the contents of the table. The compression technique used can vary across columns, or partitions within a single table. For example, you might optimize some columns in a table for scan speed, and others for space saving. CREATE TABLE employees ( c1 NUMBER, c2 NUMBER, c3 VARCHAR2(10), c4 CLOB ) INMEMORY MEMCOMPRESS FOR QUERY NO INMEMORY(c4) INMEMORY MEMCOMPRESS FOR CAPACITY HIGH(c2) Figure 9. A create table command that indicates different compression techniques for different columns
Oracle Compression Advisor Oracle Compression Advisor (DBMS_COMPRESSION) has been enhanced to support in-memory compression. The advisor provides an estimate of the compression ratio that can be realized through the use of MEMCOMPRESS. This estimate is based on analysis of a sample of the table data and provides a good estimate of the actual results obtained once the table is populated into the IM column store. As the advisor actually applies the new MEMCOMPRESS algorithms to the data it can only be run for Database In-Memory in an Oracle Database 12c environment. DECLARE l_blkcnt_cmp l_blkcnt_uncmp l_row_cmp
PLS_INTEGER; PLS_INTEGER; VARCHAR2(100); NUMBER := -1;
BEGIN dbms_compression.get_compression_ratio ( -- Input parameters scratchtbsname => 'TS_DATA', ownname => 'SSB', objname => 'LINEORDER', subobjname => NULL, comptype => dbms_compression.comp_inmemory_query_low, -- Output parameter blkcnt_cmp => l_blkcnt_cmp, blkcnt_uncmp => l_blkcnt_uncmp, row_cmp => l_row_cmp, row_uncmp => l_row_uncmp, cmp_ratio => cmp_ratio, comptype_str => l_comptype_str, subset_numrows => dbms_compression.comp_ratio_allrows); dbms_output.put_line('The IM compression ratio is '|| cmp_ratio); dbms_output.put_line('Size in-mem 1 byte for every '|| cmp_ratio || 'bytes on disk'); ); END; Figure 10. Using the Oracle Compression Advisor (DBMS_COMPRESSION) to determine the compressed size and compression ratio of the LINEORDER table in memory
Note: When you set the comptype to any of the MEMCOMPRESS types the blkcnt_cmp output value is always set to 0 as there are no data blocks in the IM column store. Also, changing the compression clause of columns with an ALTER TABLE statement results in a repopulation of any existing data in the IM column store.
In-Memory FastStart In-Memory population is a CPU bound operation, involving reformatting data into a columnar format and compressing that data before placing it in memory. New in 12.2, it is possible to checkpoint IMCUs to disk to relieve the CPU overhead of population, at the cost of additional disk space and IO bandwidth. When In-Memory FastStart (IM FastStart) is enabled, the system checkpoints the IMCUs from the IM column store to the FastStart area on disk. On subsequent database restarts, data is populated via the FastStart area rather than from the base tables. The FastStart area is a designated tablespace where In-Memory objects are stored and managed. The IM FastStart service is database specific, such that only one FastStart area is permitted for each database or Pluggable Database (PDB) in a Container Database (CDB) environment and is automatically enabled for all In-Memory objects except for objects compressed with “NO MEMCOMPRESS”, “MEMCOMPRESS FOR DML” or with Join Groups defined on them. The following PL/SQL procedure enables IM FastStart, and designates the tablespace FS_TBS as the FastStart area.
BEGIN dbms_inmemory_admin.faststart_enable('FS_TBS'); END; Figure 11. New PL/SQL procedure FASTSTART_ENABLE to turn on In-Memory FastStart
When IM FastStart is enabled, the IMCO (In-Memory Coordinator) background process designates one of the background worker processes as the FastStart coordinator process. The FastStart coordinator maintains an ordered list of IMCUs to be written to the FastStart area. IMCUs that have not been written to the FastStart area and ones that are not changing frequently are given the highest positions on the list. If one or more of the IMCUs of an object are changing rapidly then the writing out of those IMCUs will be delayed until the frequency of the changes slows down. In order to reduce the overhead of IM FastStart, the FastStart coordinator schedules the writing of IMCUs to the FastStart area based on the ordered list as described above. Additionally, the IMCUs are written lazily to the FastStart area with the new version of an IMCU replacing its previous version in the FastStart area. This helps ensure that the overhead to maintain the FastStart area is balanced with the benefit of having the most up to date copy of each IMCU for the object in the FastStart area.
Figure 12. Populating the column store from the FastStart area
In order to populate the IM column store from the FastStart area, all transactional consistency checks need to be performed. This ensures that the data populated into the IM Column Store is consistent as of the population time. Transactional consistency checks involve comparing the System Change Number (SCN) at which the IM FastStart checkpoint was taken for the IMCU with the most recent modification SCN. Depending on the result of this check and internal thresholds, the IMCU will be populated into the IM Column Store entirely from the FastStart area, populated from the FastStart area with some rows marked invalid (due to data modification after the IMCU was written to the FastStart area) or completely discarded and populated from disk.
In-Memory Scans Analytic queries typically reference only a small subset of the columns in a table. Oracle Database In-Memory accesses only the columns needed by a query, and applies any WHERE clause filter predicates to these columns directly without having to decompress them first. This greatly reduces the amount of data that needs to be accessed and processed.
In-Memory Storage Index A further reduction in the amount of data accessed is possible due to the In-Memory Storage Indexes that are automatically created and maintained on each of the columns in the IM column store. Storage Indexes allow data pruning to occur based on the filter predicates supplied in a SQL statement. An In-Memory Storage Index keeps track of minimum and maximum values for each column in an IMCU. When a query specifies a WHERE clause predicate, the In-Memory Storage Index on the referenced column is examined to determine if any entries with the specified column value exist in each IMCU by comparing the specified value(s) to the minimum and maximum values maintained in the Storage Index. If the column value is outside the minimum and maximum range for an IMCU, the scan of that IMCU is avoided. For equality, in-list, and some range predicates an additional level of data pruning is possible via the metadata dictionary created for each IMCU when dictionary-based compression is used. The metadata dictionary contains a list of the distinct values for each column within that IMCU. Dictionary based pruning allows Oracle Database to determine if the value being searched for actually exists within an IMCU, ensuring only the necessary IMCUs are scanned. SIMD Vector Processing For the data that does need to be scanned in the IM column store, Database In-Memory uses SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. The columnar format used in the IM column store has been specifically designed to maximize the number of column entries that can be loaded into the vector registers on the CPU and evaluated in a single CPU instruction. SIMD vector processing enables Database In-Memory to scan billion of rows per second. For example, let’s use the SALES table in the SH sample schema (see Figure 13), and let’s assume we are asked to find the total number of sales orders that used the PROMO_ID value of 9999. The SALES table has been fully populated into the IM column store. The query begins by scanning just the PROMO_ID column of the SALES table. The first 8 values from the PROMO_ID column are loaded into the SIMD register on the CPU and compared with 9999 in a single CPU instruction (the number of values loaded will vary based on datatype & memory compression used). The number of entries that match 9999 is recorded, then the entries are discarded and another 8 entries are loaded into the register for evaluation. And so on until all of the entries in the PROMO_ID column have been evaluated.
Figure 13. Using SIMD vector processing enables the scanning of billions of rows per second
To determine if a SQL statement is scanning data in the IM column store examine the execution plan.
Figure 14. New INMEMORY keyword in the execution plan indicates operations that are candidates for In-Memory
You will notice that the execution plan shows a new set of keywords "IN MEMORY". These keywords indicate that the LINEORDER table has been marked for IN MEMORY and Oracle Database may use the column store in this query.
In-Memory Expressions Analytic queries often contain complex expressions in the select list or where clause predicates that need to be evaluated for every row processed by the query. The evaluation of these complex expressions can be very resource intensive and time consuming. New in 12.2, In-Memory Expressions provide the ability to materialize commonly used expressions in the IM column store. Materializing these expressions not only improves the query performance by preventing the re-computation of the expression for every row but it also enables us to take advantage of all of the In-Memory query performance optimizations when we access them. An In-Memory Expression can be a combination of one or more values, operators, and SQL or PL/SQL functions (deterministic only) that resolve to a value. They must be derived only from the table they are associated with, which means that they cannot access column values in a different table. In-Memory Expressions can be created either manually via virtual columns or automatically via the Expression Statistics Store (ESS). In-Memory Virtual Columns New in 12.2, user-defined virtual columns can now be populated in the IM column store. Virtual columns will be materialized as they are populated and since the expression is evaluated at population time it can be retrieved repeatedly without re-evaluation. The initialization parameter INMEMORY_VIRTUAL_COLUMNS must be set to ENABLE or MANUAL to create user-defined In-Memory virtual columns. When set to ENABLE all user-defined virtual columns on a table with the INMEMORY attribute, will be populated into the IM column store. However, it is possible to have just a subset of virtual columns be populated. Let’s look at an analytic query, which contains a number of expressions:
SELECT l_returnflag, l_linestatus, SUM(l_extendedprice * (1 - l_discount)) AS sum_disc_price, SUM(l_extendedprice * (1 - l_discount) * (1 + l_tax)) AS sum_charge, COUNT(*) as count_order FROM
WHERE l_shipdate <= to_date ('1998-12-01','YYYY-MM-DD') - 90
GROUP BY l_returnflag, l_linestatus; Figure 15. Analytic query containing a number of commonly used expressions
Imagine the expressions SUM(l_extendedprice * (1 - l_discount))and SUM(l_extendedprice * (1 l_discount) * (1 + l_tax)) are commonly used expressions for a given application, which makes them good
candidates for In-Memory Expressions. Below are the steps necessary to define these expressions as virtual columns and have them populated in the IM column store: -- Create virtual columns for the two expressions ALTER TABLE lineorder ADD sum_disc_price AS (lo_extendedprice * (1 - lo_discount)) ALTER TABLE lineorder ADD sum_charge AS (lo_extendedprice * (1 - lo_discount) * (1 + lo_tax)) -- Enable the INMMEORY attribute on the Lineorder table ALTER TABLE lineorder INMEMORY PRIORITY HIGH Figure 16. Steps required to manually populate two commonly used expressions into the IM column store
Automatically Detected In-Memory Expressions In-Memory Expressions can also be automatically detected using the ESS and the new procedure in the DBMS_INMEMORY_ADMIN package. When you execute the IME_CAPTURE_EXPRESSIONS procedure, the 20 most
frequently executed expressions, as determined by the Optimizer, are captured from the ESS and populated automatically into the IM column store. Automatically added expressions are created as hidden virtual columns and a full list of the expressions captured can be found in the view USER_IM_EXPRESSIONS. Below is an example demonstrating the steps for capturing the 20 most frequently executed expressions from the ESS for the past 24 hours (i.e. the CURRENT parameter value) and then populating these expressions into the IM column store. Note, if the second command is not used then the database will not populate the captured In-Memory Expressions until the associated table is repopulated. -- Capture the expressions for ESS BEGIN dbms_inmemory_admin.ime_capture_expressions('CURRENT'); END; -- Check what expressions were captured SELECT * FROM user_im_expressions -- Populate the captured expression in the IM column store BEGIN dbms_inmemory_admin.ime_populate_expressions; END; Figure 17. Steps required to populate the 20 most frequently executed expressions from the ESS into the IM column store
This feature also requires the setting of the initialization parameter INMEMORY_EXPRESSIONS_USAGE, to determine what type of In-Memory Expressions are eligible to be populated. See the Managing and Monitoring section below for more details on this parameter and other parameters used to manage In-Memory Expressions. Currently in 12.2, In-Memory Expressions and In-Memory Virtual Columns are not candidates to be check-pointed to disk using In-Memory FastStart. Only the user defined columns of a table are written to the FastStart area.
JSON Document Support Although JSON documents have always been supported in the IM column store, new in 12.2 JSON documents can be stored in a special binary JSON format, which enables JSON functions, such as JSON_TABLE, JSON_VALUE or JSON_EXISTS to perform much more efficiently.
The example below shows how to enable JSON documents in the IM column store using a table called RETAIL, which contains a column, JDOC, which contains JSON documents (not shown) and then add a new column TAXAMOUNT based on the JSON_VALUE function to enable fast retrieval of the taxable amount in the JSON
document. -- Create the RETAIL table with a JSON column CREATE TABLE retail (jdoc VARCHAR2(2000) INMEMORY CONSTRAINT json_con_1 CHECK (jdoc IS json) ) -- Load JSON documents into the RETAIL table (not shown) -- Create binary JSON column ALTER TABLE retail ADD (taxamount as (json_value(jdoc,'$.TaxSummary.Summaries.TaxableAmount'))) Figure 18. Steps required for adding a JSON binary column to a table and then populating the table into the IM column store
The following query that uses the JSON_VALUE function to aggregate taxable amounts from the RETAIL table will now be automatically rewritten to take advantage of the JSON binary column in the IM column store, dramatically improving the performance of the query: SELECT MAX(JSON_VALUE(jdoc, '$.TaxSummary.Summaries.TaxableAmount')) AS
SUM(JSON_VALUE(jdoc, '$.TaxSummary.Summaries.TaxableAmount')) AS
AVG(JSON_VALUE(jdoc, '$.TaxSummary.Summaries.TaxableAmount')) AS
Figure 19. Example of a query that uses the JSON_VALUE function
In-Memory Joins SQL statements that join multiple tables can also be processed very efficiently in the IM column store as they can take advantage of Bloom Filters. A Bloom filter transforms a join into a filter that can be applied as part of the scan of the larger table. Bloom filters were originally introduced in Oracle Database 10g to enhance hash join performance and are not specific to Database In-Memory. However, they are very efficiently applied to column format data via SIMD vector processing. When two tables are joined via a hash join, the first table (typically the smaller table) is scanned and the rows that satisfy the WHERE clause predicates (for that table) are used to create an in-memory hash table stored in the Program Global Area (PGA). During the hash table creation, a bit vector or Bloom filter is also created based on the join column. The bit vector is then sent as an additional predicate to the scan of the second table. After the WHERE clause predicates have been applied to the second table scan, the resulting rows will have their join column hashed and it will be compared to values in the bit vector. If a match is found in the bit vector that row will be sent to the hash join. If no match is found then the row will be discarded It’s easy to identify Bloom filters in the execution plan. They will appear in two places, at creation time and again when it is applied. Let’s take a simple two-table join between the DATE_DIM and LINEORDERS table as an example:
SELECT SUM(lo_extendedprice * lo_discount) revenue FROM
lineorder l, date_dim d
l.lo_orderdate = d.d_datekey
l.lo_discount BETWEEN 2 AND 3
d.d_date='December 24, 2013'
Figure 20. Simple two-table join that will benefit from Bloom filters in the In-Memory column store
Below is the plan for this query with the Bloom filter highlighted. The first step executed in this plan is actually line 4; an in-memory full table scan of the DATE_DIM table. The Bloom filter (:BF0000) is created immediately after the scan of the DATE_DIM table completes (line 3). The Bloom filter is then applied as part of the in-memory full table scan of the LINEORDER table (line 5 & 6).
Figure 21. Creation and use of a Bloom filter in a two-table join between the DATE_DIM and LINEORDER tables
It is possible to see what join condition was used to build the Bloom filter by looking at the predicate information under the plan. Look for 'SYS_OP_BLOOM_FILTER' in the filter predicates. You may be wondering why a HASH JOIN appears in the plan (line 2) if the join was converted to a Bloom filter. The HASH JOIN is there because a
Bloom filter has the potential to return a false positive. The HASH JOIN confirms that all of the rows returned from the scan of the LINEORDER table are true matches for the join condition. Typically this consumes very little work. What happens for a more complex query where there are multiple tables being joined? This is where Oracle’s 30+ years of database innovation kicks in. By seamlessly building the IM column store into Oracle Database we can take advantage of all of the optimizations that have been added to the database since the first release. Using a series of optimizer transformations, multiple table joins can be rewritten to allow multiple Bloom filters to be created and used as part of the scan of the large table or fact table. Note: With Database In-Memory, Bloom filters can be used on serial queries when executed against a table that is populated into the IM column store. Not all of the tables in the query need to be populated into the IM column store in order to create and use Bloom filters. Join Groups If there is no filter predicate on the dimension table (smaller table on the left hand side of the join) then a bloom filter won’t be generated and the join will be executed as a standard HASH JOIN. New in 12.2, Join Groups have been introduced to improve the performance of standard HASH JOINS in the IM column store. Join Groups allow the join columns from multiple tables to share a single compression dictionary, enabling the HASH JOINS to be conducted
on the compressed values in the join columns rather than having to decompress the data and then hash it before conducting the join. Let’s look at an example of a two table join between the VEHICLES and SALES table to see the impact of Join Groups: SELECT v.year, v.name, s.sales_price FROM vehicles v, sales s WHERE v.name = s.name Figure 22. Simple two-table join that won’t benefit from Bloom filters in the In-Memory column store
In this statement there is no filter predicate on the VEHICLES table so a Bloom filter won’t be generated. There are also no indexes on these tables, so the optimizer will select a standard HASH JOIN plan (shown below):
Figure 23. Standard Hash Join Plan
This plan starts on line 2, with a full table scan of the VEHICLES table via the IM column store. The data from the VEHICLES table will be read from the IM column store, decompressed and the values in the NAME column (the join
column) will be hashed to create a hash table in memory to help complete the HASH JOIN on line 1 of the plan. Next, line 4 of the plan will be executed resulting in a full table scan of the SALES table. Again the data will be read from the IM column store, decompressed and values in the NAME column will be hashed so they can be used to probe into the hash table and complete the join.
Figure 24. Steps necessary to complete a standard Hash Join Plan
Let’s now create a Join Group to help improve the performance of this HASH JOIN. Below is the syntax you need to create the join group: CREATE INMEMORY JOIN GROUP jgroup_name (sales(name), vehicles(name)) Figure 25. Syntax for creating an In-Memory Join Group
The Join Group tells the IM column store that the NAME column in both the VEHICLES and SALES tables should share the same compression dictionary. If you recall, the compression dictionary contains a list of the distinct values for a column and the corresponding compression symbol. By using the same compression dictionary for the join column in both the VEHICLES and SALES tables, we are able to conduct the join on the compressed values, saving the effort of decompressing and hashing the data and reducing the amount of memory required to complete the join. Although the Join Group has been created it won’t begin to help until both the VEHICLES and SALES tables are repopulated into the IM column store, as we need to create and use the shared compression dictionary, known as a common dictionary. You can confirm that a Join Group has been created and common dictionary exists by querying the view USER_JOINGROUPS. Note that the Join Group details will be available along with the dictionary address once the tables involved in the Join Group have been repopulated:
Figure 26. Query to confirm that a Join Group has been created and populated
If we return our focus to our simple query (Figure 22), we will see that the execution plan (figure 23) won’t change even with the presence of the Join Group but how it’s executed will. The execution begins just as it did before, with a full table scan of the VEHICLES table via the IM column store. The data from the VEHICLES table will be read from the IM column store, and the values in the NAME column (the join column) will be used to create an array of compressed values to help complete the HASH JOIN on line 1 of the plan. Next, line 4 of the plan will be executed resulting in a full table scan of the SALES table via the IM column store. The compressed values in the NAME column will be used to probe into the array to see if there is a match, thus completing the join.
Figure 27. Steps necessary to complete a Hash Join Plan with a Join Group
Currently in 12.2, if a Join Group has been defined between tables, these tables are not candidates to be checkpointed to disk using In-Memory FastStart.
In-Memory Aggregation Analytic style queries often require more than just simple filters and joins. They require complex aggregations and summaries. A new optimizer transformation, called Vector Group By, was introduced with Database In-Memory to ensure more complex analytic queries can be processed using new CPU-efficient algorithms. The Vector Group By transformation is a two-part process not dissimilar to that of star transformation. Let’s take the following business query as an example: Find the total sales of footwear products in outlet stores.
The query will begin by scanning the two dimension tables (smaller tables) STORES and PRODUCTS (lines 5 & 10 in the plan below).
A new data structure called a Key Vector is created based on the results of each of these scans (lines 4, 9, & 13 in the plan below). A key vector is similar to a Bloom filter as it allows the join predicates to be applied as additional filter predicates during the scan of the SALES table (largest table). Unlike a Bloom filter a key vector will not return a false positive.
The key vectors are also used to create an additional structure called an In-Memory Accumulator. The accumulator is a multi-dimensional array built in the PGA that enables Oracle Database to conduct the aggregation or GROUP BY during the scan of the SALES table instead of having to do it afterwards.
At the end of the first phase temporary tables are created to hold the payload columns (columns referenced in the SELECT list) from the smaller dimension table (lines 2, 6, & 11 in the plan below). Starting with 12.2, the temporary tables used are in-memory only and no IO is required. Note this step is not depicted in Figure 28 below.
Figure 28. In-Memory aggregation example - Find the total sales of footwear in our outlet stores
Phase 2 5.
The second part of the execution plan begins with the scan of the SALES table and the application of the key vectors (line 24-29 in the plan below). For each entry in the SALES table that matches the join conditions (is an outlet store and is a footwear product), the corresponding sales amount will be added to the appropriate cell in the In-Memory Accumulator. If a value already exists in that cell, the two values will be added together and the resulting value will be put in the cell.
Finally, the results of the large table scan are then joined back to the temporary tables created as part of the scan of the dimension tables (lines 16, 18, & 19). Remember these temporary tables contain only the payload columns. Note this step is not depicted in Figure 28 above.
The combination of these two phases dramatically improves the efficiency of a multiple table join with complex aggregations. -----------------------------------------------------------------------------| Id | Operation | Name | -----------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | 1 | TEMP TABLE TRANSFORMATION | | | 2 | LOAD AS SELECT (CURSOR DURATION MEMORY)| SYS_TEMP_0FD9D75EA_4AB70D | | 3 | HASH GROUP BY | | | 4 | KEY VECTOR CREATE BUFFERED | :KV0000 |
| 5 | TABLE ACCESS INMEMORY FULL | STORES | <== PHASE 1 | 6 | LOAD AS SELECT (CURSOR DURATION MEMORY)| SYS_TEMP_0FD9D75EB_4AB70D | | 7 | HASH GROUP BY | | | 8 | KEY VECTOR CREATE BUFFERED | :KV0001 | | 9 | TABLE ACCESS INMEMORY FULL | PRODUCTS | | 10 | HASH GROUP BY | | |* 11 | HASH JOIN | | | 12 | TABLE ACCESS FULL | SYS_TEMP_0FD9D75EA_4AB70D | |* 13 | HASH JOIN | | | 14 | TABLE ACCESS FULL | SYS_TEMP_0FD9D75EB_4AB70D | | 15 | VIEW | VW_VT_0737CF93 | | 16 | VECTOR GROUP BY | | | 17 | HASH GROUP BY | | | 18 | KEY VECTOR USE | :KV0000 | | 19 | KEY VECTOR USE | :KV0001 | <== PHASE2 | 20 | PARTITION RANGE ALL | | |* 21 | TABLE ACCESS INMEMORY FULL | SALES | ----------------------------------------------------------------------------Figure 29. Execution plan for query that benefits from In-Memory aggregation
The VECTOR GROUP BY transformation is a cost based transformation, which means the optimizer will compare the execution plan with and without the transformation and pick the one with the lowest cost. For example, the VECTOR GROUP BY transformation may be selected in the following scenarios:
» The join columns between the tables contain "mostly" unique keys or numeric keys » The fact table (largest table in the query) is at least 10X larger than the other tables » The tables are populated into the IM column store The VECTOR GROUP BY transformation is unlikely to be chosen in the following scenarios: » Joins are performed between two or more very large tables » The dimension tables contain more than 2 billion rows » The system does not have sufficient memory resources
DML and the In-Memory Column Store It’s clear that the IM column store can dramatically improve the performance of all types of queries but very few database environments are read only. For the IM column store to be truly effective in modern database environments it has to be able to handle both bulk data loads AND online transaction processing.
Bulk Data Loads Bulk data loads occur most commonly in Data Warehouse environments and are typically conducted as a direct path load. A direct path load parses the input data, converts the data for each input field to its corresponding Oracle data type, and then builds a column array structure for the data. These column array structures are used to format Oracle data blocks and build index keys. The newly formatted database blocks are then written directly to the database, bypassing the standard SQL processing engine and the database buffer cache. A direct path load operation is an all or nothing operation. This means that the operation is not committed until all of the data has been loaded. Should something go wrong in the middle of the operation, the entire operation will be aborted. To meet this strict criterion, a direct path load inserts data into database blocks that are created above the segment high water mark (i.e. the maximum number of database blocks used so far by an object or segment). Once the direct path load is committed, the high water mark is moved to encompass the newly created blocks into the segment and the blocks will be made visible to other SQL operations on the same table. Up until this point the IM column store is not aware that any data change occurred on the segment.
Once the operation has been committed, the IM column store is instantly aware it does not have all of the data populated for the object. The size of the missing data will be visible in the BYTES_NOT_POPULATED column of the v$IM_SEGMENTS view (see the Monitoring section). If the object has a PRIORITY specified on it then the newly added data will be automatically populated into the IM column store. Otherwise the next time the object is queried, the background worker processes will be triggered to begin populating the missing data, assuming there is free space in the IM column store.
Partition Exchange Loads It is strongly recommended that the larger tables or fact tables in a data warehouse be partitioned. One of the benefits of partitioning is the ability to load data quickly and easily with minimal impact on users by using the exchange partition command. The exchange partition command allows the data in a non-partitioned table to be swapped into a particular partition in a partitioned table. The command does not physically move data; instead it updates the data dictionary to exchange a pointer from the partition to the table and vice versa. Because there is no physical movement of data, an exchange does not generate redo and undo, making it a sub-second operation and far less likely to impact performance than any traditional data-movement approaches such as INSERT. As with any direct path operation, the IM column is not aware of a partition exchange load until the operation has been completed. At that point, the data in the temporary table is now part of the partitioned table. If the temporary table had the INMEMORY attribute set and all of its data has been populated into the IM column store, nothing else will happen. The data that was in the temporary table will simply be accessed via the IM column store along with the rest of the data in the partitioned table the next time it is scanned. However, if the temporary table did not have the INMEMORY attribute set, then all subsequent accesses to the data in the newly exchanged partition will be done via the row store. Remember that the INMEMORY attribute is a physical attribute of an object. If you wish the partition to have that attribute after the exchange it must be specified on the temporary table before the exchange takes place. Specifying the attribute on the empty partition is not sufficient.
Figure 30. Five steps necessary to complete a partition exchange load on an INMEMORY table
Transaction Processing Single row data change operations (DML) execute via the row store (OLTP style changes), just as they do without Database In-Memory enabled. If the object in which the DML operations occur is populated in the IM column store, then the changes are reflected in the IM column store as they occur. The row store and the column store are always kept transactionally consistent, similarly to the way indexes are kept consistent. All serialization and logging is done on the base table just as it was before. No additional locks or logging are needed for the In-Memory Column store.
For each IMCU in the IM column store, a Snapshot Metadata Unit (SMU) is automatically created and maintained (see figure 31). When a DML statement changes a row in an object that is populated into the IM column store, the corresponding column entries for that row are marked stale in the IMCU and the rowid is added to the metadata in the SMU. The original entries in the IMCU are not immediately replaced in order to provide read consistency and maintain data compression. Any transaction executing against the object in the IM column store, that started before the DML occurred, can still see the originial version of the entries in the IMCU. Read consistency in the IM column store is managed via System Change Numbers (SCNs) just as it is without Database In-Memory enabled.
Figure 31. Each IMCU in the IM column store contains a subset of rows from an object and a corresponding SMU
When a query with a newer SCN is executed against the object, it will read all of the entries for the columns in the IMCU except the stale entries. The stale entries will be retrieved from the base table (i.e. the row store). Repopulation The more stale entries there are in an IMCU, the slower the scan of the IMCU will become. Therefore Oracle Database will repopulate an IMCU when the number of stale entries in an IMCU reaches a staleness threshold. The staleness threshold is determined by heuristics that take into account the frequency of IMCU access and the number of stale rows in the IMCU. Repopulation is more frequent for IMCUs that are accessed frequently or have a higher percentage of stale rows. The repopulation of an IMCU is an online operation executed by the background worker processes. The data is available at all times and any changes that occur to rows in the IMCU during repopulation are automatically recorded. In addition to the standard repopulation algorithm, there is another algorithm that attempts to clean all stale entries using a low priority background process. The IMCO (In-Memory Coordinator) background process may also instigate trickle repopulation for any IMCU in the IM column store that has some stale entries but does not currently meet the staleness threshold. Trickle repopulate is a constant background activity. The IMCO wakes up every two minutes and checks to see if any population tasks need to be completed. For example, the INMEMORY attribute has just been specified with a PRIORITY sub-clause on a new object. The IMCO will also check to see if there are any IMCUs with stale entries in the IM column store. If it finds some it will trigger the worker processes to repopulate them. The number of IMCUs repopulated via trickle repopulate in a given 2 minute window is limited by the new initialization parameter INMEMORY_TRICKLE_REPOPULATE_SERVERS_PERCENT. This parameter controls the maximum percentage of time that worker processes can participate in trickle repopulation activities. The more worker processes that participate, the more IMCUs that can be trickle repopulated. However, the more worker processes that participate the higher the CPU consumption. You can disable trickle repopulation altogether by setting INMEMORY_TRICKLE_REPOPULATE_SERVERS_PERCENT to 0.
Overhead of Keeping the IM Column Store Transactionally Consistent The overhead of keeping the IM column store transactionally consistent will vary by application based on a number of factors, including: the rate of change, the in-memory compression level chosen for a table, the location of the changed rows, and the type of operations being performed. Tables with higher compression levels will incur more overhead than tables with lower compression levels. Changed rows that are co-located in the same block will incur less overhead than changed rows that are spread randomly across a table. Examples of changed rows that are co-located in the same blocks are newly inserted rows since the database will usually group these together. Another example is data that is loaded using a direct path load operation. For tables that have a high rate of DML, MEMCOMPRESS FOR DML is recommended, and, where possible, it is also recommended to use partitioning to localize changes within the table. For example, range partitioning can be used to localize data in a table by date so most changes will be confined to data stored in the most recent partition. Date range partitioning also provides many other manageability and performance advantages.
The In-Memory Column Store on RAC Each node in a RAC environment has its own IM column store. It is highly recommended that the IM column stores be equally sized on each RAC node. Any RAC node that does not require an IM column store should have the INMEMORY_SIZE parameter set to 0. By default all objects populated into memory will be distributed across all of the
IM column stores in the cluster. It is also possible to have the same objects appear in the IM column store on every node (Engineered Systems only). The distribution of objects across the IM column stores in a cluster is controlled by two additional sub-clauses to the INMEMORY attribute: DISTRIBUTE and DUPLICATE. In a RAC environment, an object that only has the INMEMORY attribute specified on it will be distributed across all of the IM column stores in the cluster, effectively making the IM column store a shared-nothing architecture. How an object is distributed across the cluster is controlled by the DISTRIBUTE sub-clause. By default, Oracle decides the best way to distribute the object across the cluster given the type of partitioning used (if any). Alternatively, you can specify DISTRIBUTE BY ROWID RANGE to distribute by rowid range, DISTRIBUTE BY PARTITION to distribute partitions to different nodes, or DISTRIBUTE BY SUBPARTITION to distribute sub-partitions to different nodes. ALTER TABLE lineorder INMEMORY DISTRIBUTE BY PARTITION Figure 32. This command distributes the lineorder table across the IM column stores in the cluster by partition.
DISTRIBUTE BY PARTITION or SUBPARTITION is recommended if the tables are partitioned or sub-partitioned by HASH and a partition-wise join plan is expected. This will allow each partition join to be co-located within a single
node. DISTRIBUTE BY ROWID RANGE can be used for non-partitioned tables or for partitioned tables where DISTRIBUTE BY PARTITION would lead to data skew.
If the object is very small (consists of just 1 IMCU), it will be populated into the IM column store on just one node in the cluster. Since data populated in-memory in a RAC environment is affinitized to a specific RAC node, parallel server processes must be employed to execute a query on each RAC node against the piece of the object that resides in that node’s IM column store. The query coordinator aggregates the results from each of the parallel server processes together before returning them to the end user’s session. In order to ensure the parallel server processes are distributed appropriately across the RAC cluster, the location of the data needs to be known. Previously, Automatic Degree of Parallelism (Auto DOP) was required so that the query coordinator could ensure that the
degree of parallelism (DOP) was at least as great as the number of IM column stores involved in the query based on IMCU locations. In 12.2 this restriction has been lifted, which means the onus is now on the user to ensure that the DOP of the query is greater than or equal to the number of IM column stores involved. If this is not the case, then the data residing in IM column stores that do not get a parallel server process assigned to them will have to be read from the row store since IMCUs are not shipped across a RAC cluster. If a DML statement is issued in a RAC environment, then the mechanism to provide read consistency is essentially the same as what was described earlier in the Transaction Processing section. The main difference is that in a RAC environment the row values that are changed are marked stale in the corresponding IMCU, whether it resides in the local node (i.e. where the DML is issued) or in another node in the cluster. In either case the IM column store is always kept transactionally consistent and no IMCUs are shipped between nodes. This ensures that DML in a RAC environment is as efficient as possible.
Distribute For Service In addition to distributing data across all IM column stores in the RAC cluster, new in 12.2 it is now possible to selectively distribute objects to specific IM column stores using the FOR SERVICE subclause of the DISTRIBUTE clause. This now makes it simpler to distribute an object to a subset of IM column stores based on service. This also facilitates the placement of objects between primary and standby databases in an Active Data Guard environment (see section below for more details). If the service is stopped then the objects distributed for that service will be removed from the IM column store(s). The following syntax shows adding a service to the SALES table: ALTER TABLE sales INMEMORY DISTRIBUTE FOR SERVICE sales_ebiz Figure 33. This command distributes sales table across the IM column stores in the cluster by service.
The SALES table will now be distributed in IM column stores for instances that run the sales_ebiz service. The *_TABLES views have been modified to add in-memory service information.
Figure 34. In-memory service information for the SALES table.
Other benefits of the DISTRIBUTE FOR SERVICE subclause include support for rolling patches and upgrades, and application affinity. Support for rolling patches and upgrades Using the DUPLICATE subclause and the FOR SERVICE subclause allows a node to be taken out of service without affecting application availability, assuming that there is enough capacity to support the workload on the remaining nodes. Application affinity Some applications require one or more dedicated nodes and the FOR SERVICE subclause makes it simpler to direct specific objects to a specific node(s).
In-Memory Fault Tolerance Given the shared nothing architecture of the IM column store in a RAC environment, some performance sensitive applications may require a fault tolerant solution. On an Engineered System it is possible to mirror the data populated into the IM column store by specifying the DUPLICATE sub-clause of the INMEMORY attribute. This means that each IMCU populated into the IM column store will have a mirrored copy placed on one of the other nodes in the RAC cluster. Mirroring the IMCUs provides in-memory fault tolerance as it ensures data is still accessible via the IM column store even if a node goes down. It also improves performance, as queries can access both the primary and the backup copy of the IMCU at any time.
Figure 35. Objects in the IM column store on Engineered Systems can be mirrored to improve fault tolerance
Should a RAC node go down and remain down for some time, the only impact will be the re-mirroring of the primary IMCUs located on that node. Only if a second node were to go down and remain down for some time would the data have to be redistributed. If additional fault tolerance is desired, it is possible to populate an object into the IM column store on each node in the cluster by specifying the DUPLICATE ALL sub-clause of the INMEMORY attribute. This will provide the highest level of redundancy and provide linear scalability, as queries will be able to execute completely within a single node. ALTER TABLE lineorder INMEMORY DUPLICATE ALL Figure 36. This command ensures each IMCU of the lineorder table will appear in all IM column stores in the cluster
The DUPLICATE ALL option may also be useful to co-locate joins between large distributed fact tables and smaller dimension tables. By specifying the DUPLICATE ALL option on the smaller dimension tables a full copy of these tables will be populated into the IM column store on each node. If a RAC node should go down on a non-Engineered System, the data populated into the IM column store on that node will no longer be available in-memory on the cluster. Queries issued against the missing pieces of the objects will not fail. Instead they will access the data either from the buffer cache or storage, which will impact the performance of these queries. Should the node remain down for some time, the objects or pieces of the objects that resided in the IM column store on that node will be populated on the remaining nodes in the cluster (assuming there is available space). In order to minimize the impact on performance due to a downed RAC node, it is recommended that some space be left free in the IM column store on each of the other nodes in the cluster. Note that data is not redistributed to other nodes of the cluster immediately upon a node or instance failure because it is very likely that the node or instance will be quickly brought back into service. If data was immediately redistributed, the redistribution process would add extra workload to the system that then would be undone when the node or instance returns to service. In order to avoid this, the system waits for a few minutes before initiating data redistribution, allowing the node or instance time to rejoin the cluster.
When the node rejoins the cluster data will be redistributed to the newly joined node. The distribution is done on an IMCU basis and the objects are fully accessible during this process.
In-Memory FastStart on RAC In a RAC environment, in 12.2 the FastStart area is global (visible by all instances). The IMCUs from one instance can be used by another instance to populate its IM column store. When the DUPLICATE ALL option is enabled, only the primary instance persists the IMCUs to the FastStart area. On instance restarts or IMCU population on a different RAC instance, the FastStart area is checked for availability of the IMCU before it is populated from disk. If the IMCU exists in the FastStart area it can be used to efficiently populate the IMCU into the IM column store. Alternatively, the regular population mechanism will populate the data from disk.
Controlling the Content of the In-Memory Column Store with ADO Automatic Data Optimization 1(ADO) was introduced in Oracle Database 12c Release 1 to enable the automation of Information Lifecycle Management (ILM) tasks. ADO supports both compression tiering and storage tiering using policies defined at the row or segment level on tables and partitions. The Heat Map feature of ADO tracks the access of segments (reads & writes) at the row level (aggregated to block-level statistics) and at the segment level. This allows the automatic management of database segments using policies based on how database segments are being used. New in 12.2, ADO has been extended to encompass the IM column store. ADO manages the content of the IM column store by executing user-defined policies to move tables or partitions in and out of the IM column store, and adjusting the compression level of objects within the IM column store from a lower compression level to a higher compression level. Three new policies have been added that enable managing objects in the IM column store: POLICY NAME
Enables the INMEMORY attribute on a specified segment
Changes the compression level of an object from a lower level of compression to a higher level Removes, or evicts, an object from the IM column store
Figure 37. ADO policies for the IM column store
The criteria for ADO policy evaluation remains the same as it is for segment based policies which is the number of days since the object was modified, accessed or created, or with a user-defined function. Policies will be executed as part of the automated database maintenance task’s maintenance window, and since all ADO policies for Database In-Memory are segment level policies they execute only one time and are then disabled.
More information Automatic Data Optimization can be found in the paper Automatic Data Optimization with Oracle Database 12c Release 2
Let’s look at an example of how to specify a policy on the SALES table to enable the INMEMORY attribute 5 days after it was created. Delaying the population of newly created objects can be useful when those objects experience a high rate of change initially, but then are used mostly for queries. ALTER TABLE sales ilm ADD policy SET INMEMORY AFTER 5 days OF creation Figure 38. Command to specify the INMEMORY attribute on the SALES table via an ADO policy
Alternatively, we could have enabled the INMEMORY attribute on the SALES table using the MEMCOMPRESS FOR DML sub-clause and then specified an ADO policy to increase the compression level, 3 days after it stops being modified. ALTER TABLE sales ilm ADD policy MODIFY INMEMORY memcompress FOR query high AFTER 3 days OF no modification Figure 39. Command to increase the in-memory compression level attribute on the SALES table via an ADO policy
Using the second approach we can maximize the space allocated within the IM column store without incurring additional compression overhead for data that is being changed frequently. Finally let’s look at a policy that will evict the SALES table from the IM column store after it has not been accessed for 30 days. ALTER TABLE sales ilm ADD policy NO INMEMORY SEGMENT AFTER 30 days OF no ACCESS Figure 40. Command to specify the NO INMEMORY attribute on the SALES table via an ADO policy
This type of policy provides the ability to automatically remove unused objects from the IM column store based on Heat Map data, and eliminates the chances that a frequently accessed object will be inadvertently removed from the IM column store.
User-Defined ADO Policy You can also customize policies with the ON PL/SQL FUNCTION option using customized PL/SQL to determine when the policy should be executed. The function must return a BOOLEAN value and accept a NUMBER as an input parameter. The following is a simple example that always returns TRUE and then creates a policy based on the function: CREATE OR REPLACE FUNCTION custom_im_ado (objn IN NUMBER) RETURN BOOLEAN; ALTER TABLE sales ilm ADD policy NO INMEMORY SEGMENT ON custom_im_ado; Figure 41. An example of a PL/SQL function used for an ADO eviction policy on the SALES table
The In-Memory Column Store in a Multitenant Environment Oracle Multitenant2 is a database consolidation model in which multiple Pluggable Databases (PDBs) are consolidated within a Container Database (CDB). While keeping many of the isolation aspects of single databases, it allows PDBs to share the System Global Area (SGA) and background processes of a common CDB. Therefore, PDBs also share a single IM column store.
More information on Oracle Multitenant can be found in the white paper Oracle Multitenant: New Capabilities in Release 12.2
Figure 42. Three PDBs in a single Oracle Database 12c Container Database
The total size of the IM column store is controlled by the INMEMORY_SIZE parameter setting in the CDB. Each PDB specifies how much of the shared IM column store it can use by setting the INMEMORY_SIZE parameter. Not all PDBs in a given CDB need to use the In-Memory column store. Some PDBs can have the INMEMORY_SIZE parameter set to 0, which means they won't use the In-Memory column store at all. It is not necessary for the sum of the PDBs’ INMEMORY_SIZE parameters to be less than or equal to the size of the INMEMORY_SIZE parameter on the CDB. It is possible for the PDBs to over subscribe to the IM column store. Over
subscription is allowed to ensure that valuable space in the IM column store is not wasted should one of the pluggable databases be shutdown or unplugged. However, it is possible for one PDB to starve another PDB of space in the IM column store due to this over subscription. If you don’t expect any PDBs to be shut down for extended periods or any of them to be unplugged it is recommended that you don’t over subscribe.
Figure 43. PDBs specify how much of the shared IM column store they can use by setting INMEMORY_SIZE parameter
Each pluggable database (PDB) is a full Oracle database in its own right, so the data populated into the IM column store by one PDB is not visible or accessible by another PDB and each PDB will have its own priority list. When a PDB starts up the objects on its priority list will be populated into the In-Memory column store in order of its own priority list.
The In-Memory Column Store in an Active Data Guard Environment Oracle Active Data Guard3 is the most comprehensive solution available to eliminate single points of failure for mission critical Oracle Databases. It prevents data loss and downtime in the simplest and most economical manner by maintaining a synchronized physical replica of a production database at a remote location. If the production database is unavailable for any reason, client connections can quickly, and in some configurations transparently, failover to the synchronized replica to restore service. It also eliminates the high cost of idle redundancy by allowing
More information on Active Data Guard can be found in the paper Oracle Active Data Guard
reporting applications, ad-hoc queries, and data extracts to be offloaded to read-only copies of the production database. Active Data Guard is unique in using a highly parallelized process to apply changes to a standby database for best performance while enforcing the same read consistency model as the primary database. New in 12.2, Active Data Guard has been tightly integrated with Database In-Memory, providing users the ability to enable the IM column store on the primary, standby or both environments. With synchronized physical replication and read-consistency, in-memory processing on Active Data Guard is a viable solution for running read-only workloads instead of running those on the primary. It makes it possible to run real-time analytics on the standby database with no impact on the production database, making productive use of the standby database resources, and at the same time increasing the total columnar capacity of the system.
Figure 44. Example of how the IM column store on the standby database can have very different content to the primary
By considering the standby environment as a separate database, Database In-Memory makes it possible to populate the same or a different set of tables or table partitions in-memory on the primary and on the standby database. Just as Active Data Guard maintains a synchronized physical replica of the production database, it also maintains the contents of the IM column store ensuring transactionally consistent results as of the query SCN. As described in the RAC section above, the DISTRIBUTE FOR SERVICE clause can be used to specify the placement and population of the in-memory objects across instances in a RAC cluster. This clause can also be used for populating objects into the IM column store on the standby database by providing the appropriate service name, active only on the standby. When a service name is specified, the table or table partition is populated into memory only on the database instances where the specified service is active. In an event of a role change or switchover, the tables will be repopulated on the instance(s) where the new services are active. Below is an example of syntax required to populate the EMPLOYEES table into memory only on the database instances where the "REPORTING_STANDBY_SVC" is allowed to run. CREATE TABLE employees (c1 NUMBER, c2 NUMBER, c3 VARCHAR2(10), c4 CLOB ) INMEMORY MEMCOMPRESS FOR QUERY DISTRIBUTE AUTO FOR SERVICE reporting_standby_svc Figure 45. Using DISTRIBUTE AUTO FOR SERVICE sub-clause to populate a table on the standby instance in Active Data Guard
Restrictions on Active Data Guard In an Active Data Guard environment, the standby is opened in a read-only mode, which has an impact on some of the new In-Memory functionality as outlined below:
It is not possible to maintain the In-Memory FastStart area on the standby database, therefore In-Memory FastStart is not supported on the standby database.
It is also not possible to maintain the Expression Statistics Store (ESS) on the standby database. Therefore all automatically detected expressions will be based on the workload seen on the primary. If the workload on the primary database is not representative of the workload on the standby we do not recommend using automatically detected In-Memory Expressions. Instead In-Memory virtual columns should be used to materialize the commonly used expressions on the standby into the IM column store.
In-Memory Join Groups are not supported on the standby.
With Automatic Data Optimization, INMEMORY policies are only evaluated on the primary database. However, since ILM INMEMORY policies are implemented with ALTER TABLE statements, an object residing in-memory on the standby database will be affected should a policy specified on it be executed. For example, if an object is only populated in-memory on the standby database and an ILM INMEMORY policy based on number of days since the object was accessed is specified on it, that policy would be evaluated on the primary database. If the object is only accessed on the standby and never on the primary then the outcome of that policy may not be what was expected. It is for this reason that we recommend caution when specifying ILM INMEMORY policies based on days for objects that will reside in the standby database only.
Extending In-Memory Columnar Format to Flash on Exadata The Oracle Exadata Database Machine uses a unique set of software algorithms to implement database intelligence in storage, PCI based flash, and InfiniBand networking. A full rack Exadata X6-2 Database Machine offers 180TB of flash, which is nearly 10X the capacity of DRAM and can deliver up to 300GB/second of bandwidth from flash. New in 12.2, it is possible to store data in the In-Memory columnar format in the flash cache in an Exadata environment. This enables all of the In-Memory optimizations (accessing only the compressed columns required, SIMD vector processing, storage indexes, etc.) to be used on a potentially much larger amount of data. When the INMEMORY_SIZE parameter is set to a non-zero value, objects compressed using Hybrid Columnar Compression4 (HCC) brought into Exadata flash cache will be automatically converted into the In-Memory columnar format.
More information on Hybrid Columnar Compression can be found in the paper Exadata Hybrid Columnar Compression
Figure 46. All of the benefit of In-Memory columnar now available on Exadata Flash
A new segment-level attribute, CELLMEMORY, has also been introduced to help control which objects should not be populated into flash using the In-Memory columnar format and which type of compression should be used. Just like the INMEMORY attribute you can specify different compression levels as sub-clauses to the CELLMEMORY attribute. However, not all of the INMEMORY compression levels are available; only MEMCOMPRESS FOR QUERY LOW and MEMCOMPRESS FOR CAPACITY LOW (default).
The PRIORTY sub-clause is also not available, as there are no background worker processes active on the Exadata storage cells (where the flash cache resides) to do the population on startup. Therefore, only on demand population is possible. When an object that is compressed using HCC is accessed for the first time, the process accessing the data will do the population of the data into the flash cache. This will increase the elapsed time for the initial access of the data, but all subsequent accesses will be able to benefit from all of the In-Memory optimizations when they retrieve data from the flash cache. ALTER TABLE trades CELLMEMORY MEMCOMPRESS FOR QUERY LOW Figure 47. New CELLMMEORY segment-level attribute indicates that the TRADES table should be populated into Exadata flash cache using MEMCOMPRESS FOR QUERY LOW compression
Note, only objects that are compressed using Hybrid Columnar Compression on disk are eligible to be populated into the flash cache in the In-Memory columnar format. This restriction is to ensure that objects populated into flash in the In-Memory columnar format are unlikely to be changing rapidly and therefore the overhead to maintain the InMemory columnar format in flash will be minimal.
Controlling the Use of Database In-Memory There are several initialization parameters and optimizer hints that allow you to control when and how the IM column store will be used. This section describes them all and provides guidance on which ones are core and which are optional.
Core Initialization Parameters Nine new initialization parameters with the INMEMORY prefix have been introduced to directly control the different aspects of the new in-memory functionality. There is also a new optimizer parameter that can have an effect on whether queries use the IM column store or not.
INMEMORY_SIZE As described earlier in this document, the INMEMORY_SIZE parameter controls the amount of memory allocated to the IM column store. The default size is 0 bytes. This parameter is only modifiable at the system level and will require a database restart to take effect. The minimum size required for the INMEMORY_SIZE parameter is 100 MB. Starting in 12.2 if the IM column store has been enabled (i.e. inmemory_size > 0) then it is possible to increase the size of the IM column store dynamically. The IM column store must be increased in increments of 128MB or more, and there must be enough memory available in the SGA to accommodate the increased size. INMEMORY_QUERY The Oracle Optimizer is aware of the objects populated in the IM column store and will automatically direct any queries it believes will benefit from the in-memory column format to the IM column store. Setting INMEMORY_QUERY to DISABLE either at the session or system level disables the use of the IM column store completely. It will blind the Optimizer to what is in the IM column store and it will prevent the execution layer from scanning and filtering data in the IM column store. The default value is ENABLE. INMEMORY_MAX_POPULATE_SERVERS The maximum number of worker processes that can be started is controlled by the INMEMORY_MAX_POPULATE_SERVERS, which is set to 0.5 X CPU_COUNT by default. Reducing the number of worker
processes will reduce the CPU resource consumed during population but it will likely extend the amount of time it takes to do the population of the IM column store.
Additional Initialization Parameters INMEMORY_CLAUSE_DEFAULT The INMEMORY_CLAUSE_DEFAULT parameter allows you to specify a default mode for in-memory tables by specifying a valid set of values for all of the INMEMORY sub-clauses not explicitly specified in the syntax. The default value is an empty string, which means that only explicitly specified tables are populated into the IM column store. ALTER SYSTEM SET inmemory_clause_default = 'INMEMORY PRIORITY LOW' Figure 49. Using the INMEMORY_CLAUSE_DEFAULT parameter to mark all new tables as candidates for the IM column store
The parameter value is parsed in the same way as the INMEMORY clause, with the same defaults if one of the subclauses is not is specified. Any table explicitly specified for in-memory will inherit any unspecified values from this parameter. INMEMORY_TRICKLE_REPOPULATE_SERVERS_PERCENT This parameter controls the maximum percentage of time that worker processes can perform trickle repopulation. The value of this parameter is a percentage of the INMEMORY_MAX_POPULATE_SERVERS parameter. Setting this parameter to 0 disables trickle repopulation; the default is 1 meaning that the worker processes will spend one percent of their time performing trickle repopulate. INMEMORY_FORCE By default any object with the INMEMORY attribute specified on it is a candidate to be populated into the IM Column Store. However, if INMEMORY_FORCE is set to OFF, then even if the in-memory area is configured, no tables are put in memory. The default value is DEFAULT. INMEMORY_VIRTUAL_COLUMNS The INMEMORY_VIRTUAL_COLUMNS parameter controls the use of virtual columns in the IM column store. Three parameter values are available (i.e. ENABLE, MANUAL, DISABLE). The ENABLE value specifies that all virtual columns for the table or partition will be populated in the IM column store at the default table or partition memcompress level unless the virtual column has been explicitly excluded or a different memcompress level has been specified. The MANUAL value is the default and specifies that no virtual columns will be populated in-memory unless they have been explicitly marked for inmemory or they have been marked for inmemory with a different memcompress level. The DISABLE value disables the use of virtual columns in the IM column store. INMEMORY_EXPRESSIONS_USAGE The INMEMORY_EXPRESSIONS_USAGE parameter controls which In-Memory Expressions are populated in the IM column store. There are four values for this parameter (i.e. STATIC_ONLY, DYNAMIC_ONLY, ENABLE, DISABLE). The STATIC_ONLY value allows the population of JSON columns in a special binary JSON format. The DYNAMIC_ONLY value will populate automatically created In-Memory Expressions in the IM column store when used
with the DBMS_INMEMORY.IME_CAPTURE_EXPRESSIONS procedure. The ENABLE value combines both the STATIC_ONLY and DYNAMIC_ONLY parameter values, and the DISABLE value prevents any In-Memory Expressions
from being populated in the IM column store. OPTIMIZER_INMEMORY_AWARE As mentioned above, the optimizer is aware of the IM column store and uses in-memory specific costs when it costs the alternative in-memory plans for a SQL statement. It is possible to disable all of the in-memory enhancements made to the optimizer’s cost model by setting the OPTIMIZER_INMEMORY_AWARE parameter to FALSE. Please note that even with the Optimizer in-memory enhancements disabled, you may still get an In-Memory plan.
Optimizer Hints The different aspects of In-Memory - in-memory scans, joins and aggregations - can be controlled at a statement or a statement block level via the use of optimizer hints. As with most optimizer hints, the corresponding negative hint for each of the hints described below is preceded by the word 'NO_'. Remember that an optimizer hint is a directive that will be followed when applicable.
INMEMORY Hint The only thing the INMEMORY hint does is enables the IM column store to be used when the INMEMORY_QUERY parameter is set to DISABLE. It won’t force a table or partition without the INMEMORY attribute to be populated into the IM column store. If you specify the INMEMORY hint in a SQL statement where none of the tables referenced in the statement are populated into memory, the hint will be treated as a comment since it will not be applicable to the SQL statement. Nor will the INMEMORY hint force a full table scan via the IM column store to be chosen, if the default plan (lowest cost plan) is an index access plan. You will need to specify the FULL hint to see that plan change take effect. The NO_INMEMORY hint does the same thing in reverse. It will prevent the access of an object from the IM column store; even if the object is fully populated into the column store and the plan with the lowest cost is a full table scan. In-Memory Scan As stated above, if you wish to force an In-Memory full table scan you will need to use the FULL hint to change the access method for an object (i.e. table, partition or subpartition). The (NO_)INMEMORY_PRUNING hint can also influence the performance of an In-Memory scan as it controls the use of In-Memory storage indexes. By default every query executed against the IM column store can take advantage of the In-Memory storage indexes, which enable data pruning to occur based on the filter predicates supplied in a SQL statement. As with most hints, the INMEMORY_PRUNING hint was introduced to help test the new functionality. In other words, the hint was originally introduced to disable the IM storage indexes. In-Memory Joins The use of a Bloom filter to convert a join into a filter is a cost-based decision. If the Optimizer doesn’t choose a Bloom filter, it is possible to force it by using the PX_JOIN_FILTER hint. In-Memory Aggregation The new in-memory aggregation feature (VECTOR GROUP BY) is a cost-based query transformation, which means it’s possible to force the transformation to occur even when the Optimizer does not consider it to be the cheapest execution plan. A VECTOR GROUP BY plan can be forced by specifying the VECTOR_TRANSFORM hint.
Conclusion Oracle Database In-Memory transparently accelerates analytic queries by orders of magnitude, enabling real-time business decisions. It dramatically accelerates data warehouses and mixed workload OLTP environments. The unique "dual-format" approach automatically maintains data in both the existing Oracle row format for OLTP operations, and in a new purely in-memory column format optimized for analytical processing. Both formats are simultaneously active and transactionally consistent. Embedding the column store into Oracle Database ensures it is fully compatible with ALL existing features, and requires absolutely no changes in the application layer. This means you can start taking full advantage of it on day one, regardless of the application.
Appendix A - Monitoring and Managing Oracle Database In-Memory Monitoring Objects in the In-Memory Column Store There are two new v$ views, v$IM_SEGMENTS and v$IM_USER_SEGMENTS that indicate what objects are currently populated in the IM column store.
Figure 50. New v$IM_SEGMENTS view
These views not only show which objects are populated in the IM column store, they also indicate how the objects are distributed across a RAC cluster and whether the entire object has been populated (BYTES_NOT_POPULATED). It is also possible to use this view to determine the compression ratio achieved for each object populated in the IM column store, assuming the objects were not compressed on disk. SELECT v.owner, v.segment_name, v.bytes orig_size, v.inmemory_size in_mem_size, v.bytes / v.inmemory_size comp_ratio FROM v$im_segments v; Figure 51. Determining the compression ratio achieved for the objects populated into the IM column store
Another new view, v$IM_COLUMN_LEVEL, contains details on the columns populated into the column store, as not all columns in a table need to be populated into the column store.
Figure 52. The PROD_ID column was not populated into the IM column store
USER_TABLES A new Boolean column called INMEMORY has been added to the *_TABLES dictionary tables to indicate which tables have the INMEMORY attribute specified on them.
Figure 53. New INMEMORY column added to *_TABLES to indicate which tables have INMEMORY attribute
In the example above you will notice that two of the tables – COSTS and SALES – don’t have a value for the INMEMORY column. The INMEMORY attribute is a segment level attribute. Both COSTS and SALES are partitioned tables and are therefore logical objects. The INMEMORY attribute for these tables will be recorded at the partition or sub-partition level in *_TAB_(SUB)PARTITIONS. Three additional columns – INMEMORY_PRIORITY, INMEMORY_DISTRIBUTE, and INMEMORY_COMPRESSION – have also been added to the *_TABLES views to indicate the current In-Memory attributes for each table. Two additional columns - INMEMORY_SERVICE, INMEMORY_SERVICE_NAME - have been added to the *_TABLES views to indicate the In-Memory attributes associated with the FOR SERVICE subclause of the DISTRIBUTE clause. Finally an additional column, CELLMEMORY has been added to the *_TABLES views to indicate that the table is a candidate to be brought into the flash cache on Exadata with non-default values. USER_IM_EXPRESSIONS A new set of views has been added USER_IM_EXPRESSIONS and DBA_IM_EXPRESSIONS to allow the easy display of In-Memory Expressions.
Figure 54. New USER_IM_EXPRESSIONS view
In the example above you will notice that the table and column name is available along with the SQL expression and object number. The DBA_IM_EXPRESSIONS view adds the OWNER column.
Managing IM Column Store Population CPU Consumption The initial population of the IM column store is a CPU intensive operation, which can affect the performance of other workloads running concurrently. You can use Resource Manager5 to control the CPU usage of IM column store population operations and change their priority as needed. To do this, enable CPU Resource Manager by enabling one of the out-of-box resource plans, such as default_plan, or by creating your own resource plan. By default, in-memory population is run in the ora$autotask consumer group, except for on-demand population, which runs in the consumer group of the user that triggered the population. If the ora$autotask consumer group doesn’t exist in the resource plan, then the population will run in OTHER_GROUPS. The other operations in ora$autotask include automated maintenance operations like gathering statistics and segment analysis. The SET_CONSUMER_GROUP_MAPPING procedure can be used to change the consumer group for in-memory population. BEGIN dbms_resource_manager.set_consumer_group_mapping( attribute => 'ORACLE_FUNCTION', value => 'INMEMORY', consumer_group => 'BATCH_GROUP'); END; Figure 55. Changing the Resource Manager consumer group of the INMEMORY operation
Session Level Statistics It is also possible to monitor what is happening with Database In-Memory by querying the session level statistics. Below is a list of the most commonly queried In-Memory session level statistics an explanation of what they represent. Statistics Name
Description Number of rows that were skipped (because of storage
IM scan rows optimized
index pruning) or that weren't accessed due to aggregations with predicate push downs
IM scan rows projected
Number of rows returned to the upper layer
IM scan rows
Number of rows scanned in all IMCUs
IM scan rows valid
IM scan CUs no memcompress IM scan CUs memcompress for * IM scan CUs columns accessed
Number of rows scanned in all IMCUs after applying valid vector Number of times IMCUs of each memcompress type were touched
Number of columns accessed by a scan
More information on using Oracle Database Resource Manager can be found in the white paper Using Oracle Resource Manager
IM scan CUs invalid or missing revert to on disk extent
Number of on disk extents accessed due to missing or invalid IMCUs
IM scan CUs pruned
Number of IMCUs with no rows passing min/max
IM scan segments minmax eligible
Number of IMCUs that are eligible for min/max pruning
IM scan segments disk
IM scan CUs predicates applied
IM scan CUs predicates optimized
Number of times a segment marked for in-memory was accessed entirely from the buffer cache/direct read Number of min/max predicates applied Number of IMCUs where either all rows passed min/max or no rows passed min/max
IM scan CUs predicates received
Number of min/max predicates received
IM scan EU rows
Number of rows scanned from EUs in the IM column store before where clause predicate applied
IM scan EUs no memcompress Number of times IMEUs of each memcompress type IM scan EUs memcompress for *
IM scan EUs columns accessed
Number of columns in the EUs accessed by scans
IM scan EUs columns decompressed
Number of columns in the EUs decompressed by scans
IM scan EU bytes in-memory
Size in bytes of in-memory EU data accessed by scans
IM scan EU bytes uncompressed
Uncompressed size in bytes of in-memory EU data accessed by scans
IM scan EUs columns theoretical max
Number of columns that would have been accessed from the EU if the scans looked at all columns
IM scan EUs split pieces
Number of split EU pieces among all IM EUs
IM populate segments requested
Number of population tasks for in-memory segments
table scan disk IMC fallback
Number of rows in blocks scanned from buffer cache/direct read where an IM scan was possible