DBA Blogs

Partition Storage -- 8 : Manually Sizing Partitions

Hemant K Chitale - Wed, 2016-05-18 10:44
As demonstrated in the first post in this series, the  default size for Table Partitions is to start with an I nitial8MB Extent.  The fourth post demonstrated how to resize a Partition that has already been created and populated.

Here is a method to pre-size the Initial Extent. I start with 3 partitions of different sizes.

SQL> create table my_part_tbl_init_sized (id_column number(6), data_column varchar2(100))
2 partition by range (id_column)
3 (partition p_100 values less than (101) segment creation immediate
4 storage (initial 64K next 64K) tablespace hemant,
5 partition p_200 values less than (201) segment creation immediate
6 storage (initial 1M next 1M) tablespace hemant,
7 partition p_max values less than (maxvalue) segment creation immediate
8 storage (initial 8M next 1M) tablespace hemant)
9 /

Table created.

SQL>
SQL> select partition_name, initial_extent, next_extent, tablespace_name
2 from user_tab_partitions
3 where table_name = upper('my_part_tbl_init_sized')
4 order by partition_position
5 /

PARTITION_NA INITIAL_EXTENT NEXT_EXTENT TABLESPACE_NAME
------------ -------------- ----------- ------------------------------
P_100 65536 65536 HEMANT
P_200 1048576 1048576 HEMANT
P_MAX 8388608 1048576 HEMANT

SQL>
SQL> select partition_name, bytes/1024, extents
2 from user_segments
3 where segment_name = upper('my_part_tbl_init_sized')
4 and segment_type = 'TABLE PARTITION'
5 order by 1
6 /

PARTITION_NA BYTES/1024 EXTENTS
------------ ---------- ----------
P_100 64 1
P_200 1024 1
P_MAX 8192 1

SQL>


However, the caveat about Partition Extent Sizes when SPLIT is used as shown in the third post still applies.

SQL> alter table my_part_tbl_init_sized
2 split partition p_max
3 at (301)
4 into (partition p_300, partition p_max)
5 /

Table altered.

SQL>
SQL> select partition_name, initial_extent, next_extent, tablespace_name
2 from user_tab_partitions
3 where table_name = upper('my_part_tbl_init_sized')
4 order by partition_position
5 /

PARTITION_NA INITIAL_EXTENT NEXT_EXTENT TABLESPACE_NAME
------------ -------------- ----------- ------------------------------
P_100 65536 65536 HEMANT
P_200 1048576 1048576 HEMANT
P_300 8388608 1048576 HEMANT
P_MAX 8388608 1048576 HEMANT

SQL>
SQL> select partition_name, bytes/1024, extents
2 from user_segments
3 where segment_name = upper('my_part_tbl_init_sized')
4 and segment_type = 'TABLE PARTITION'
5 order by 1
6 /

PARTITION_NA BYTES/1024 EXTENTS
------------ ---------- ----------
P_100 64 1
P_200 1024 1
P_300 8192 1
P_MAX 8192 1

SQL>


So, SPLITting P_MAX to create P_300 created P_300 with an 8MB extent because the sizing is inherited from P_MAX.  How do I change the Partition Extent sizes on SPLIT ?

SQL> alter table my_part_tbl_init_sized
2 split partition p_max
3 at (501)
4 into (partition p_500 storage (initial 64K next 64K) tablespace hemant, partition p_max)
5 /

Table altered.

SQL>
SQL> alter table my_part_tbl_init_sized
2 split partition p_500
3 at (401)
4 into (partition p_400, partition p_500)
5 /

Table altered.

SQL>
SQL> select partition_name, initial_extent, next_extent, tablespace_name
2 from user_tab_partitions
3 where table_name = upper('my_part_tbl_init_sized')
4 order by partition_position
5 /

PARTITION_NA INITIAL_EXTENT NEXT_EXTENT TABLESPACE_NAME
------------ -------------- ----------- ------------------------------
P_100 65536 65536 HEMANT
P_200 1048576 1048576 HEMANT
P_300 8388608 1048576 HEMANT
P_400 65536 65536 HEMANT
P_500 65536 65536 HEMANT
P_MAX 8388608 1048576 HEMANT

6 rows selected.

SQL> select partition_name, bytes/1024, extents
2 from user_segments
3 where segment_name = upper('my_part_tbl_init_sized')
4 and segment_type = 'TABLE PARTITION'
5 order by 1
6 /

PARTITION_NA BYTES/1024 EXTENTS
------------ ---------- ----------
P_100 64 1
P_200 1024 1
P_300 8192 1
P_400 64 1
P_500 64 1
P_MAX 8192 1

6 rows selected.

SQL>


This time, when I split P_MAX I deliberately specified a size for P_500.  Then, when I split P_500 further, the new P_400 inherited the sizing from P_500.
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Categories: DBA Blogs

Another reason why you should use the Data Guard Broker for your #Oracle Standby

The Oracle Instructor - Wed, 2016-05-18 02:00

The Data Guard Broker is recommended for various reasons, this one is less obvious: It prevents a Split-Brain problem that may otherwise occur in certain situations. Let me show you:

[oracle@uhesse ~]$ dgmgrl sys/oracle@prima
DGMGRL for Linux: Version 12.1.0.2.0 - 64bit Production

Copyright (c) 2000, 2013, Oracle. All rights reserved.

Welcome to DGMGRL, type "help" for information.
Connected as SYSDBA.
DGMGRL> show configuration;

Configuration - myconf

  Protection Mode: MaxAvailability
  Members:
  prima - Primary database
    physt - Physical standby database 

Fast-Start Failover: DISABLED

Configuration Status:
SUCCESS   (status updated 18 seconds ago)

This is my setup with 12c, but the demonstrated behavior is the same with 11g already. I will cause a crash of the primary database now, without damaging any files – like a power outage on the primary site:

[oracle@uhesse ~]$ ps -ef | grep smon
oracle    6279     1  0 08:30 ?        00:00:00 ora_smon_prima
oracle    6786     1  0 08:32 ?        00:00:00 ora_smon_physt
oracle    7168  3489  0 08:43 pts/0    00:00:00 grep --color=auto smon
[oracle@uhesse ~]$ kill -9 6279

Don’t do that at home:-) Now the primary is gone, but of course I can failover to the standby:

[oracle@uhesse ~]$ dgmgrl sys/oracle@physt
DGMGRL for Linux: Version 12.1.0.2.0 - 64bit Production

Copyright (c) 2000, 2013, Oracle. All rights reserved.

Welcome to DGMGRL, type "help" for information.
Connected as SYSDBA.
DGMGRL> failover to physt;
Performing failover NOW, please wait...
Failover succeeded, new primary is "physt"

So far so good, my end users can continue to work now on the new primary. But what happens when the power outage is over and the ex-primary comes back up again?

[oracle@uhesse ~]$ sqlplus sys/oracle@prima as sysdba

SQL*Plus: Release 12.1.0.2.0 Production on Wed May 18 08:47:30 2016

Copyright (c) 1982, 2014, Oracle.  All rights reserved.

Connected to an idle instance.

SQL> startup
ORACLE instance started.

Total System Global Area 1258291200 bytes
Fixed Size		    2923920 bytes
Variable Size		  452985456 bytes
Database Buffers	  788529152 bytes
Redo Buffers		   13852672 bytes
Database mounted.
ORA-16649: possible failover to another database prevents this database from
being opened

The DMON background process of the new primary communicates with the DMON on the ex-primary, telling it that there cannot be two primary databases within the same Data Guard Broker configuration! Try the same scenario without the broker and you will observe the ex-primary coming up until status OPEN. Just wanted to let you know:-)


Tagged: Data Guard
Categories: DBA Blogs

An Effective Approach to Migrate Dynamic Thrift Data to CQL, Part 1

Pythian Group - Tue, 2016-05-17 16:00
1. Introduction

Recently Pythian’s Cassandra team worked on one customer’s request to copy data of several Cassandra tables between two Cassandra clusters (not Cassandra data centers). The original approach we used to copy data is through Cassandra COPY TO/FROM commands because the size of the data to be copied is not large (several hundred mega-bytes per table). The execution of the commands was successful, but for one of the tables that we did data copy, the application complained about missing data. We examined the data for that table using Cassandra cqlsh utility and found no discrepancy. After a further discussion with the customer, we realized that the source tables were created and manipulated by a Thrift based application and the application can dynamically create different columns for different rows, although each row does share a common set of statically defined columns. It is the data in these dynamic columns that are missing during the data copy process.

We addressed the issue, but in the end we felt that we should write something about Cassandra data migration from Thrift to CQL because this is quite a common problem faced by many existing Cassandra users right now, considering that Cassandra is gradually phasing out Thrift and replacing it with CQL.

This post is the first part of a three-post series. In this post (Part 1), we’re going to dive into the details of Cassandra storage engine (pre-3.0 version) and explore some fundamental concepts that are key to better understanding the discussion in the following posts. In the next post (Part 2), we’re going to explore how Cassandra tables can be defined statically, dynamically, or in a mixed mode in Thrift and what the corresponding table definition in CQL are. In the last post (Part 3), we’ll present an effective approach to migrate dynamically generated data in Thrift into a statically defined CQL table, without suffering any data loss.

1.1. Cassandra Transition from Thrift to CQL API

Apache Thrift is a software framework developed at Facebook for “scalable cross-language services development”. In early days of Cassandra, Thrift base API was the only method to develop Cassandra client applications. But with the maturity of CQL (Cassandra query language), Cassandra is gradually moving away from Thrift API to CQL API. Along with this trend,

  • Thrift based client drivers are not officially supported.
  • Thrift API will not get new Cassandra features; it exists simply for backward compatibility purpose.
  • CQL based “cqlsh” utility is replacing thrift based “cassandra-cli” utility as the main command-line tool to interact with Cassandra.

Please note that the latest version of CQL is 3.x (3.4 as of May, 2016). CQL2 is deprecated and removed for Cassandra 2.2 and later.  In the discussion below, we will simply use CQL to refer to CQL 3.

2. Overview of Internal Cassandra Data Storage Structure

Please note that since Cassandra 3.0, the underlying storage engine for Cassandra has gone through a lot of changes. The discussion in this post series is for pre-3.0 Cassandra (v 2.2 and before).

At very high level, a Cassandra table (or column family by old term) can be seen as a map of sorted map in the following format *:

     Ma<RowKey, SortedMap<ColumnKey, ColumnValue>>

A graphical representation looks like below:

Cassanra_thrift-to-cql-scrnsht1

Please note that although there are more complex structures such as Super Column and Composite Column, the basic idea remains the same and the representation above is good enough for us to describe the problem in this document.

Internally, such a storage structure is where both Thrift and CQL APIs are based. The difference is that Thrift API manipulates the storage structure directly, but CQL API does so through an abstraction layer and expresses the data to user in a tabular form similar to what SQL does for a relational database.

In order to make this clearer, let’s use an example to compare the outputs between cassandra-cli and cqlsh command utilities, which are based on Thrift and CQL protocols separately. The table schema is defined as below (in CQL format):

CREATE TABLE song_tags (
   id uuid,
   tag_name text,
   PRIMARY KEY (id, tag_name)
) WITH COMPACT STORAGE

This simple table is used to maintain the song tags. After inserting several rows in this table, we examined the table content using both Thirft based “cassandra-cli” utility and CQL based “cqlsh” utility. The result is as below:

Cassanra_thrift-to-cql-scrnsht2

From the example above, it can be easily noticed that between Thrift and CQL, the terms “row” and “column” don’t share the same meaning and this causes some confusion when people do Thrift to CQL migration. For example,

  • In Thrift, one row means one data partition that is determined by the partition key definition. Each row has multiple columns, or more precisely “cells”. Each cell contains the time-stamp of when it is created. The name/key of the cell/column is not necessarily the name as defined in the table definition, especially when there are clustering column(s) defined (just as in the example above)
  • In CQL, one row could be one partition, or could be one part of a partition. It really depends on how the partition key and cluster key are designed

Due to such differences, I’m going to use the following terms in this document for clarification:

  • “CQL Row”, or simply “Row”, refers to a row in CQL context
  • “Storage Row”, refers to a row in Thrift context
  • “Column”, refers to a column in both CQL or Thrift contexts
  • “Cell” particularly refers to a column in Thrift context
  • “Table” refers to a table in CQL context, or a column family in Thrift context
  • “Partition” refers to a data partition determined by the hash key. In Thrift context, “Partition” and “Storage Row” has the same meaning. In CQL context, one “Partition” includes multiple “CQL Rows”.
3. Compact Storage

In CQL, a table property, called for COMPACT STRORAGE, is created for backward compatibility.  As the name suggests, tables created with this directive consumes less storage space compared with those created without this directive.

To make this clear, we also uses an example to explain. Basically, two tables are created to keep track of the average student grades for classes in a school. The table definition for them are exactly the same except that one (avg_grade) is defined without COMPACT STORAGE property, but another (avg_grade2) does. The same records of data are also inserted into both tables, as below:

CREATE TABLE avg_grade (
    student_id int,
    class_id int,
    grade double,
    PRIMARY KEY (student_id, class_id)
)

CREATE TABLE avg_grade2 (
    student_id int,
    class_id int,
    grade double,
    PRIMARY KEY (student_id, class_id)
) WITH COMPACT STORAGE

insert into avg_grade(student_id, class_id, grade) values (1, 1, 75.2);
insert into avg_grade(student_id, class_id, grade) values (2, 1, 81.3);

insert into avg_grade2(student_id, class_id, grade) values (1, 1, 75.2);
insert into avg_grade2(student_id, class_id, grade) values (2, 1, 81.3);

The statements are executed in cqlsh utility. The data is then flushed from memory to disk with “nodetool flush” command. After that, sstable2json utility is used to examine the contents of the SSTable data files for both tables. Below is the output:

Cassanra_thrift-to-cql-scrnsht3

From the output above, we can see that tables NOT in compact storage mode has more cells within each Storage Row (e.g. the extra cell with empty value for clustering column “class”) and each Cell stores more metadata (e.g. the name of the “grade” column is added in each of the row). So just from storage perspective, having a table defined in compact storage could save quite some storage space, especially when we’re dealing with many columns and/or with complex column types like collections.

In Thrift, tables are always stored in compact storage mode. In CQL, tables by default are stored in non-compact storage mode, unless the tables are defined with “COMPACT STRORAGE” property. As a result of this, CQL tables without “COMPACT STORAGE” property are not visible in Thrift based utilities like cassandra-cli.

When a CQL table is defined with COMPACT STORAGE property, it gets the benefit of saving some disk space. However, there are also some caveats that need to pay attention to. For example:

  • It cannot have new columns added or existing columns dropped.
  • If it has a compound primary key (multiple columns), then at most one column can be defined as not part of the key.
  • It cannot have a column defined with non-frozen collection types.
    • Note that for people who are not familiar with the concept of “frozen” vs. “non-frozen” collection, a frozen collection serializes all sub-components of the collection into one single value when stored, which is treated as a blob and the whole value must be updated once. On the contrary, A non-frozen collection allows updates on individual fields.
Categories: DBA Blogs

Characterset Conversion Conundrums

Pythian Group - Mon, 2016-05-16 11:30

Every now and then a database needs to be migrated from one characterset to another. The driver behind this is ususally to either synchronize all databases to a single standard or to support new characters or symbols like €,

Categories: DBA Blogs

Replication between Tungsten clusters

Pythian Group - Mon, 2016-05-16 10:52
Replication between Tungsten clusters

The process I will describe in this post will allow you to configure replication between Tungsten clusters. The most common use case I have seen for this is a dedicated ETL cluster.

The setup will look like this:

 

Replication between Tungsten clusters

So we will have a composite datasource (compositeprod) composed of east and west clusters, and two ETL clusters, one on each side.

The described setup allows failover or switchover within a single datacenter (e.g. db1.east -> db2.east) or to the Disaster Recovery Site, (e.g. db1.east -> db1.west) for the core cluster.

At the time of this article’s publication it is not possible to replicate between two composite clusters, so each ETL cluster needs to be standalone. This feature is expected for Tungsten 5.0 release.

We will install a standalone replicator on the ETL hosts to bring data in from the core cluster. This assumes you have already completed the required prerequisites.

Configuration

Using the .ini files installation method, the config file for the core cluster hosts would be:

[defaults]
application-password=****
application-port=9999
application-user=app
home-directory=/opt/continuent
mysql-connectorj-path=/opt/continuent/connector-java/mysql-connector-java-5.1.24/mysql-connector-java-5.1.24-bin.jar
replication-password=****
replication-port=3306
replication-user=tungsten
start-and-report=true

[east]
master=db1.east
members=db1.east,db2.east,db3.east

[west]
master=db1.west
members=db1.west,db2.west,db3.west

[compositeprod]
dataservice-composite-datasources=east,west

The config file for etl.east hosts would be:

[defaults]
application-password=****
application-port=9999
application-user=etl
home-directory=/opt/continuent
mysql-connectorj-path=/opt/continuent/connector-java/mysql-connector-java-5.1.24/mysql-connector-java-5.1.24-bin.jar
replication-password=****
replication-port=3306
replication-user=tungsten
start-and-report=true

[defaults.replicator]
home-directory=/opt/continuent/replicator
executable-prefix=tr
rmi-port=10002
thl-port=2113
property=replicator.log.slave.updates=false

[etl_east]
topology=clustered
master=etl1.east
members=etl1.east,etl2.east,etl3.east

[compositeprod]
topology=cluster-alias
master=db1.east
members=db1.east,db2.east,db3.east

[core_etl_east]
topology=cluster-slave
master-dataservice=compositeprod
slave-dataservice=etl_east

For etl.west hosts:

[defaults]
application-password=****
application-port=9999
application-user=etl
home-directory=/opt/continuent
mysql-connectorj-path=/opt/continuent/connector-java/mysql-connector-java-5.1.24/mysql-connector-java-5.1.24-bin.jar
replication-password=****
replication-port=3306
replication-user=tungsten
start-and-report=true

[defaults.replicator]
home-directory=/opt/continuent/replicator
executable-prefix=tr
rmi-port=10002
thl-port=2113
property=replicator.log.slave.updates=false

[etl_west]
topology=clustered
master=etl1.west
members=etl1.west,etl2.west,etl3.west

[compositeprod]
topology=cluster-alias
master=db1.west
members=db1.west,db2.west,db3.west

[core_etl_west]
topology=cluster-slave
master-dataservice=compositeprod
slave-dataservice=etl_west
Installation

Now we need to install the cluster by running the Tungsten installer on each core and ETL host:

./install/continuent-tungsten-4.0.2-6072082/tools/tpm install

Also on ETL hosts only, we need to install the standalone replicator:

./install/tungsten-replicator-4.0.0-18/tools/tpm install

Each ETL host will end up with 2 different replicator processes:

  1. The replicator that brings data in from the core cluster
  2. The replicator that reads data from the ETL cluster master to replicate to the ETL slaves

With the ‘executable-prefix‘ option, it is easy to reference each one:

  1. tr_trepctl status will show info from the core -> ETL replicator
  2. trepctl status will show info from the ETL cluster replicator
Tips & tricks

One thing to keep in mind is that for aliases to actually work, we need to add the folllowing line to .bashrc:

. "/opt/continuent/replicator/share/env.sh"

as there is a bug where the installer only adds the aliases from the cluster installation, and ignores the ones from the standalone replicator installation.

The [defaults.replicator] tag is helpful to pass options to the standalone replicator (it is ignored by the cluster installation), as the same tungsten.ini file will be read by both the cluster and standalone replicator products.

In this case I am using it to specify different THL and RMI ports, so the standalone replicator does not collide with the cluster replicator.

We also need to tell the core -> ETL replicator not to log its updates to the binlog, otherwise the changes will be picked up by the ETL cluster replicator as well, and end up being applied two times on the ETL slaves, resulting in consistency errors.

Do you have any questions about replication between Tungsten clusters? Let me know in the comments!

 

Categories: DBA Blogs

Links for 2016-05-15 [del.icio.us]

Categories: DBA Blogs

Partner Webcast – Oracle Database In-Memory: Accelerate Business

Businesses must compete in today’s high-speed, always-on world where requirements are more demanding than ever. That’s easier said than done, especially when decision-makers must wait hours—in some...

We share our skills to maximize your revenue!
Categories: DBA Blogs

Analyze Index Validate Structure – The Dark Side

Pythian Group - Fri, 2016-05-13 09:38

Recently a co-worker wanted to discuss a problem he had encountered after upgrading a database.

The upgrade plan included steps to verify object integrity; this was being done with analyze table <tablename> validate structure cascade. All was fine until one particular table was being analyzed.  Suddenly it seemed the process entered a hung state.

The job was killed and separate commands were created to analyze each object individually.  That went well up until one of the last indexes was reached.

Me: How long has it been running?

Coworker: Three days.

Yes, you read that correctly, it had been running for three days.

My friend ran a 10046 trace to see what the process was doing; nearly all the work was ‘db file sequential read’ on the table.

At this time I suspected it was related to the clustering_factor for the index in question.  The analyze process for an index verifies each row in the index.  If the cluster is well ordered then the number of blocks read from the table will be similar to the number of blocks making up the table.

If however the table is not well ordered relative to the columns in the index the number of blocks read from the table can be many times the total number of blocks that are actually in the table.

Consider for a moment that we have rows with an  ID of 1,2,3,4 and 5.  Let’s assume that our index is created on the ID column.

If these rows are stored in order in the table, it is very likely these rows will all be in the same block, and that a single block read will fetch all of these rows.

If however the rows are stored in some random order, it may be that a separate block read is required for each lookup.

IDBlock Number122275316425104

In this case 5 separate blocks must be read to retrieve these rows.

In the course of walking the index, some  minutes later these rows must also be read:

IDBlock Number104857622104857775104857816104857921048580104

The blocks where these rows reside are the same blocks as the earlier example. The problem of course is that quite likely the blocks have been removed from cache by this time, and must be read again from disk.

Now imagine performing this for millions of rows. With a poor clustering factor the analyze command on an index could take quite some time to complete.

This seemed worthy of a test so we could get a better idea of just how bad this issue might be.

The test was run with 1E7 rows. The SQL shown below creates 1E7 rows, but you can simply change the value of level_2 to 1e3 to reduce the total rows to 1E6, or even smaller if you like.

 


-- keep this table small and the rows easily identifiable
-- or not...

-- 1e3 x 1e4 = 1e7
def level_1=1e3
def level_2=1e4

drop table validate_me purge;

create table validate_me
tablespace
   alloctest_a -- EXTENT MANAGEMENT LOCAL AUTOALLOCATE SEGMENT SPACE MANAGEMENT AUTO
   --alloctest_m -- EXTENT MANAGEMENT LOCAL AUTOALLOCATE SEGMENT SPACE MANAGEMENT MANUAL
   --alloctest_u -- EXTENT MANAGEMENT LOCAL UNIFORM SIZE 65536 SEGMENT SPACE MANAGEMENT AUTO
pctfree 0
as
select
   -- for a good clustering factor
   --id
   --
   -- for a bad clustering factor
   floor(dbms_random.value(1,1e6)) id
   , substr('ABCDEFGHIJKLMNOPQRSTUVWXYZ',mod(id,10),15) search_data
   , to_char(id,'99') || '-' || rpad('x',100,'x') padded_data
from (
   select rownum id
   from
   (
      select null
      from dual
      connect by level <= &level_1
   ) a,
   (
      select null
      from dual
      connect by level <= &level_2
   ) b
)
/

create index validate_me_idx1 on validate_me(id,search_data);

exec dbms_stats.gather_table_stats(user,'VALIDATE_ME',method_opt => 'for all columns size 1')


 

Let’s see just what the clustering factor is for this index. The following script cluster-factor.sql will get this information for us.

 


col v_tablename new_value v_tablename noprint
col v_owner new_value v_owner noprint

col table_name format a20 head 'TABLE NAME'
col index_name format a20 head 'INDEX NAME'
col index_rows format 9,999,999,999 head 'INDEX ROWS'
col table_rows format 9,999,999,999 head 'TABLE ROWS'
col clustering_factor format 9,999,999,999 head 'CLUSTERING|FACTOR'
col leaf_blocks format 99,999,999 head 'LEAF|BLOCKS'
col table_blocks format 99,999,999 head 'TABLE|BLOCKS'


prompt
prompt Owner:
prompt

set term off feed off verify off
select upper('&1') v_owner from dual;
set term on feed on

prompt
prompt Table:
prompt

set term off feed off verify off
select upper('&2') v_tablename from dual;
set term on feed on


select
   t.table_name
   , t.num_rows table_rows
   , t.blocks table_blocks
   , i.index_name
   , t.num_rows index_rows
   , i.leaf_blocks
   , clustering_factor
from all_tables t
   join all_indexes i
      on i.table_owner = t.owner
      and i.table_name = t.table_name
where t.owner = '&v_owner'
   and t.table_name = '&v_tablename'

/

undef 1 2

 

Output from the script:

 

SQL> @cluster-factor jkstill validate_me

Owner:

Table:


                                          TABLE                                            LEAF     CLUSTERING
TABLE NAME               TABLE ROWS      BLOCKS INDEX NAME               INDEX ROWS      BLOCKS         FACTOR
-------------------- -------------- ----------- -------------------- -------------- ----------- --------------
VALIDATE_ME              10,000,000     164,587 VALIDATE_ME_IDX1         10,000,000      45,346     10,160,089

1 row selected.

Elapsed: 00:00:00.05

 

On my test system creating the table for 1E7 rows required about 2 minutes and 15 seconds, while creating the index took 28 seconds.

You may be surprised at just how long it takes to analyze that index.

 

SQL> analyze index jkstill.validate_me_idx1 validate structure online;

Index analyzed.

Elapsed: 00:46:06.49

 

Prior to executing this command a 10046 trace had been enabled, so there is a record of how Oracle spent its time on this command.

 

If you are wondering how much of the 46 minutes was consumed by the tracing and writing the trace file, it was about 6 minutes:

 

$>  grep "WAIT #48004569509552: nam='db file sequential read'"; oravm1_ora_2377_VALIDATE.trc  | awk '{ x=x+$8 } END { printf ("%3.2f\n",x/1000000/60) }'
  40.74

A Well Ordered Table

Now lets see how index analyze validate structure performs when the table is well ordered. The table uses the DDL as seen in the previous example, but rather than use dbms_random to generate the ID column, the table is created with the rows loaded in ID order.  This is done by uncommenting id in the DDL and commenting out the call to dbms_random.

 

SQL> analyze index jkstill.validate_me_idx1 validate structure online;

Index analyzed.

Elapsed: 00:01:40.53

That was a lot faster than previous.  1 minute and 40 seconds whereas previously the same command ran for 40 minutes.

 

Using some simple command line tools we can see how many times each block was visited.

 

First find the cursors and verify this cursor used only once in the session

$> grep -B1 '^analyze index' oravm1_ora_19987_VALIDATE.trc
PARSING IN CURSOR #47305432305952 len=64 dep=0 uid=90 oct=63 lid=90 tim=1462922977143796 hv=2128321230 ad='b69cfe10' sqlid='318avy9zdr6qf'
analyze index jkstill.validate_me_idx1 validate structure online


$> grep -nA1 'PARSING IN CURSOR #47305432305952' oravm1_ora_19987_VALIDATE.trc
63:PARSING IN CURSOR #47305432305952 len=64 dep=0 uid=90 oct=63 lid=90 tim=1462922977143796 hv=2128321230 ad='b69cfe10' sqlid='318avy9zdr6qf'
64-analyze index jkstill.validate_me_idx1 validate structure online
--
276105:PARSING IN CURSOR #47305432305952 len=55 dep=0 uid=90 oct=42 lid=90 tim=1462923077576482 hv=2217940283 ad='0' sqlid='06nvwn223659v'
276106-alter session set events '10046 trace name context off'

As this cursor was reused, we need to limit the lines we considered from the trace file.

 

One wait line appears like this:

WAIT #47305432305952: nam=’db file sequential read’ ela= 317 file#=8 block#=632358 blocks=1 obj#=335456 tim=1462923043050233

As it is already known the entire table resides in one file, it is not necessary to check the file.

From the following command it is clear that no block was read more than once during the analyze index validate structure when the table was well ordered in relation to the index.

 

$> tail -n +64 oravm1_ora_19987_VALIDATE.trc| head -n +$((276105-64)) | grep "WAIT #47305432305952: nam='db file sequential read'" | awk '{ print $10 }' | awk -F= '{ print $2 }' | sort | uniq -c | sort -n | tail
      1 742993
      1 742994
      1 742995
      1 742996
      1 742997
      1 742998
      1 742999
      1 743000
      1 743001
      1 743002

 

That command line may look a little daunting, but it is really not difficult when each bit is considered separately.

From the grep command that searched for cursors we know that the cursor we are interested in first appeared at line 64 in the trace file.

tail -n +64 oravm1_ora_19987_VALIDATE.trc

The cursor was reused at line 276105, so tell the tail command to output only the lines up to that point in the file.

head -n +$((276105-64))

The interesting information in this case is for ‘db file sequential read’ on the cursor of interest.

grep “WAIT #47305432305952: nam=’db file sequential read'”

Next awk is used to output the block=N portion of each line.

awk ‘{ print $10 }’

awk is again used, but this time to split the block=N output at the ‘=’ operator, and output only the block number.

awk -F= ‘{ print $2 }’

The cut command could have been used here as well. eg. cut -d= -f2

Sort the block numbers

sort

Use the uniq command to get a count of how many times each value appears in the output.

uniq -c

Use sort -n to sort the output from uniq.  If there are any counts greater than 1, they will appear at the end of the output.

sort -n

And pipe the output through tail. We only care if any block was read more than once.

tail

Now for the same procedure on the trace file generated from the poorly ordered table.

 


$> grep -B1 '^analyze index' oravm1_ora_2377_VALIDATE.trc
PARSING IN CURSOR #48004569509552 len=64 dep=0 uid=90 oct=63 lid=90 tim=1462547433220254 hv=2128321230 ad='aad620f0' sqlid='318avy9zdr6qf'
analyze index jkstill.validate_me_idx1 validate structure online

$> grep -nA1 'PARSING IN CURSOR #48004569509552' oravm1_ora_2377_VALIDATE.trc
51:PARSING IN CURSOR #48004569509552 len=64 dep=0 uid=90 oct=63 lid=90 tim=1462547433220254 hv=2128321230 ad='aad620f0' sqlid='318avy9zdr6qf'
52-analyze index jkstill.validate_me_idx1 validate structure online
--
6076836:PARSING IN CURSOR #48004569509552 len=55 dep=0 uid=90 oct=42 lid=90 tim=1462550199668869 hv=2217940283 ad='0' sqlid='06nvwn223659v'
6076837-alter session set events '10046 trace name context off'

 

The top 30 most active blocks were each read 53 or more times when the table was not well ordered in relation to the index.

$> tail -n +51 oravm1_ora_2377_VALIDATE.trc | head -n +$((6076836-51)) | grep "WAIT #48004569509552: nam='db file sequential read'" | awk '{ print $10 }' | awk -F= '{ print $2 }' | sort | uniq -c | sort -n | tail -30
     53 599927
     53 612399
     53 613340
     53 633506
     53 640409
     53 644099
     53 649054
     53 659198
     53 659620
     53 662600
     53 669176
     53 678119
     53 682177
     53 683409
     54 533294
     54 533624
     54 537977
     54 549041
     54 550178
     54 563206
     54 568045
     54 590132
     54 594809
     54 635330
     55 523616
     55 530064
     55 532693
     55 626066
     55 638284
     55 680250

 

Use RMAN

There is a feature of RMAN that allows checking for logical and physical corruption of an Oracle database via the  command backup check logical validate database.  This command does not actually create a backup, but just reads the database looking for corrupt blocks. Following is an (edited) execution of running this command on the same database where the analyze index commands were run.

A portion of the block corruption report is included.

RMAN> backup check logical validate database;
2>
Starting backup at 06-MAY-16
allocated channel: ORA_DISK_1
channel ORA_DISK_1: SID=29 instance=oravm1 device type=DISK
channel ORA_DISK_1: starting full datafile backup set
channel ORA_DISK_1: specifying datafile(s) in backup set
input datafile file number=00008 name=+DATA/oravm/datafile/alloctest_a.273.789580415
input datafile file number=00009 name=+DATA/oravm/datafile/alloctest_u.272.789582305
input datafile file number=00024 name=+DATA/oravm/datafile/swingbench.375.821472595
input datafile file number=00023 name=+DATA/oravm/datafile/swingbench.374.821472577
input datafile file number=00019 name=+DATA/oravm/datafile/bh08.281.778786819
input datafile file number=00002 name=+DATA/oravm/datafile/sysaux.257.770316147
input datafile file number=00004 name=+DATA/oravm/datafile/users.259.770316149
input datafile file number=00001 name=+DATA/oravm/datafile/system.256.770316143
input datafile file number=00011 name=+DATA/oravm/datafile/alloctest_m.270.801310167
input datafile file number=00021 name=+DATA/oravm/datafile/ggs_data.317.820313833
input datafile file number=00006 name=+DATA/oravm/datafile/undotbs2.265.770316553
input datafile file number=00026 name=+DATA/oravm/datafile/undotbs1a.667.850134899
input datafile file number=00005 name=+DATA/oravm/datafile/example.264.770316313
input datafile file number=00014 name=+DATA/oravm/datafile/bh03.276.778786795
input datafile file number=00003 name=+DATA/oravm/datafile/rcat.258.861110361
input datafile file number=00012 name=+DATA/oravm/datafile/bh01.274.778786785
input datafile file number=00013 name=+DATA/oravm/datafile/bh02.275.778786791
input datafile file number=00022 name=+DATA/oravm/datafile/ccdata.379.821460707
input datafile file number=00007 name=+DATA/oravm/datafile/hdrtest.269.771846069
input datafile file number=00010 name=+DATA/oravm/datafile/users.271.790861829
input datafile file number=00015 name=+DATA/oravm/datafile/bh04.277.778786801
input datafile file number=00016 name=+DATA/oravm/datafile/bh05.278.778786805
input datafile file number=00017 name=+DATA/oravm/datafile/bh06.279.778786809
input datafile file number=00018 name=+DATA/oravm/datafile/bh07.280.778786815
input datafile file number=00020 name=+DATA/oravm/datafile/bh_legacy.282.778787059
input datafile file number=00025 name=+DATA/oravm/datafile/baseline_dat.681.821717827
input datafile file number=00027 name=+DATA/oravm/datafile/sqlt.668.867171675
input datafile file number=00028 name=+DATA/oravm/datafile/bh05.670.878914399

channel ORA_DISK_1: backup set complete, elapsed time: 00:25:27

List of Datafiles
=================
File Status Marked Corrupt Empty Blocks Blocks Examined High SCN
---- ------ -------------- ------------ --------------- ----------
1    OK     0              75632        256074          375655477
  File Name: +DATA/oravm/datafile/system.256.770316143
  Block Type Blocks Failing Blocks Processed
  ---------- -------------- ----------------
  Data       0              158478
  Index      0              17160
  Other      0              4730

File Status Marked Corrupt Empty Blocks Blocks Examined High SCN
---- ------ -------------- ------------ --------------- ----------
2    OK     0              36332        394240          375655476
  File Name: +DATA/oravm/datafile/sysaux.257.770316147
  Block Type Blocks Failing Blocks Processed
  ---------- -------------- ----------------
  Data       0              170007
  Index      0              138603
  Other      0              49298

 

As shown in the report, only 25 minutes were required to check the entire database for physically or logically corrupt blocks, as opposed to the 40 minutes needed to analyze index validate structure.

While the RMAN corruption check is not the same as the check performed by analyze index validate structure, it is a test that can be completed in a much more timely manner, particularly if some indexes are both large and have a high value for the clustering factor.

Rebuild the Index?

If you have strong suspicions that a large index with an unfavorable clustering factor has corrupt blocks, it may be more expedient to just rebuild the index.  If the database is on Oracle Enterprise Edition, the rebuild can also be done with the ONLINE option.

Consider again the index on the test table with 1E7 rows.  Creating the index required 28 seconds, while validating the structure required 40 minutes.

 

 SQL> alter index validate_me_idx1 rebuild online;

Index altered.

Elapsed: 00:00:59.88


 

The conclusion is quite clear; the use of analyze index validate structure needs to be carefully considered when its use it contemplated for large indexes. The use of this command could be very resource intensive and take quite some time to complete. It is worthwhile to consider alternatives that my be much less resource intensive and time consuming.

Categories: DBA Blogs

MySQL encrypted streaming backups directly into AWS S3

Pythian Group - Fri, 2016-05-13 09:25
Overview

Cloud storage is becoming more and more popular for offsite storage and DR solutions for many businesses. This post will help with those people that want to perform this process for MySQL backups directly into Amazon S3 Storage. These steps can probably also be adapted for other processes that may not be MySQL oriented.

Steps

In order to perform this task we need to be able to stream the data, encrypt it, and then upload it to S3. There are a number of ways to do each step and I will try and dive into multiple examples so that way you can mix and match the solution to your desired results.  The AWS S3 CLI tools that I will be using to do the upload also allows encryption but to try and get these steps open for customization, I am going to do the encryption in the stream.

  1. Stream MySQL backup
  2. Encrypt the stream
  3. Upload the stream to AWS S3
Step 1 : Stream MySQL Backup

There are a number of ways to stream the MySQL backup. A lot of it depends on your method of backup. We can stream the mysqldump method or we can utilize the file level backup tool Percona Xtrabackup to stream the backup. Here are some examples of how these would be performed.

mysqldump

When using mysqldump it naturally streams the results. This is why we have to add the greater than sign to stream the data into our .sql file. Since mysqldump is already streaming the data we will pipe the results into our next step

[root@node1 ~]# mysqldump --all-databases > employee.sql

becomes

[root@node1 ~]# mysqldump --all-databases |
xtrabackup

xtrabackup will stream the backup but with a little more assistance to tell it to do so. You can reference Precona’s online documentation (https://www.percona.com/doc/percona-xtrabackup/2.4/innobackupex/streaming_backups_innobackupex.html) for all of the different ways to stream and compress the backups using xtrabackup. We will be using the stream to tar method.

innobackupex --stream=tar /root > /root/out.tar

becomes

innobackupex --stream=tar ./ |
Step 2 : Encrypt The Stream

Now that we have the backup process in place, we will then want to make sure that our data is secure. We will want to encrypt the data that we are going to be sending up to AWS S3 as to make sure the data is protected. We can accomplish this a couple of ways. The first tool I am going to look at is GnuPG (https://www.gnupg.org/), which is the open source version of PGP encryption. The second tool I will look at is another very popular tool OpenSSL (https://www.openssl.org/).  Below are examples of how I set them up and tested their execution with streaming.

GnuPG

I will be creating a public and private key pair with a password that will be used to encrypt and decrypt the data. If you are going to do this for your production and sensitive data, please ensure that your private key is safe and secure.  When creating the keypair I was asked to provide a password.  When decrypting the data I was then asked for the password again to complete the process. It was an interactive step and is not shown in the example below. To accept a stream, you don’t provide a file name to encrypt, then to stream the output, you just don’t provide an output parameter.

KEY PAIR CREATION
[root@node1 ~]# gpg --gen-key
gpg (GnuPG) 2.0.14; Copyright (C) 2009 Free Software Foundation, Inc.
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.

Please select what kind of key you want:
   (1) RSA and RSA (default)
   (2) DSA and Elgamal
   (3) DSA (sign only)
   (4) RSA (sign only)
Your selection? 1
RSA keys may be between 1024 and 4096 bits long.
What keysize do you want? (2048)
Requested keysize is 2048 bits
Please specify how long the key should be valid.
         0 = key does not expire
      <n>  = key expires in n days
      <n>w = key expires in n weeks
      <n>m = key expires in n months
      <n>y = key expires in n years
Key is valid for? (0)
Key does not expire at all
Is this correct? (y/N) y

GnuPG needs to construct a user ID to identify your key.

Real name: root
Name must be at least 5 characters long
Real name: root@kmarkwardt
Email address: markwardt@pythian.com
Comment:
You selected this USER-ID:
    "root@kmarkwardt <markwardt@pythian.com>"

Change (N)ame, (C)omment, (E)mail or (O)kay/(Q)uit? O
You need a Passphrase to protect your secret key.

can't connect to `/root/.gnupg/S.gpg-agent': No such file or directory
gpg-agent[1776]: directory `/root/.gnupg/private-keys-v1.d' created
We need to generate a lot of random bytes. It is a good idea to perform
some other action (type on the keyboard, move the mouse, utilize the
disks) during the prime generation; this gives the random number
generator a better chance to gain enough entropy.

After typing for what felt like FOREVER, to generate enough entropy

gpg: /root/.gnupg/trustdb.gpg: trustdb created
gpg: key 1EFB61B1 marked as ultimately trusted
public and secret key created and signed.

gpg: checking the trustdb
gpg: 3 marginal(s) needed, 1 complete(s) needed, PGP trust model
gpg: depth: 0  valid:   1  signed:   0  trust: 0-, 0q, 0n, 0m, 0f, 1u
pub   2048R/1EFB61B1 2016-04-29
      Key fingerprint = 8D98 2D23 3C49 F1E7 9CD2  CD0F 7163 EB03 1EFB 61B1
uid                  root@kmarkwardt <markwardt@pythian.com>
sub   2048R/577322A0 2016-04-29

[root@node1 ~]#

 

SAMPLE USAGE
ENCRYPT
[root@node1 openssl]# echo "test" | gpg --output install.log.gpg --encrypt -r root 
[root@node1 openssl]# cat install.log.gpg
?
 ???    Ws"???l?
??g             ?w??g?C}P
   ?5A??f?6?p?
???Qq?m??&?rKE??*}5.?4XTj?????Th????}A???: ^V?/w?$???"?<'?;
?Y?|?W????v?R??a?8o<BG??!?R???f?u?????????e??????/?X?y?S7??H??@???Y?X~x>qoA0??L?????*???I?;I?l??]??Gs?G'?!??
                                                                                                            ??k>?
DECRYPT
[root@node1 ~]# gpg --decrypt -r root --output install.log.decrypted install.log.gpg
install.log.decrypted
You need a passphrase to unlock the secret key for
user: "root@kmarkwardt <markwardt@pythian.com>"
2048-bit RSA key, ID 577322A0, created 2016-04-29 (main key ID 1EFB61B1)

can't connect to `/root/.gnupg/S.gpg-agent': No such file or directory
gpg: encrypted with 2048-bit RSA key, ID 577322A0, created 2016-04-29
     "root@kmarkwardt <markwardt@pythian.com>"
[root@node1 ~]# ls
install.log.decrypted
install.log.gpg

ENCRYPT STREAM

[root@node1 ~]# mysqldump --all-databases | gpg --encrypt -r root 
or
[root@node1 ~]# innobackupex --stream=tar ./ | gpg --encrypt -r root 

 

OpenSSL

As with GPG we will generate a public and private key with a pass phrase.  There are other ways to use openssl to encrypt and decrypt the data such as just using a password with no keys, using just keys with no password, or encrypt with no password or keys.  I am using keys with a password as this is a very secure method.

KEY PAIR CREATION
[root@node1 openssl]# openssl req -newkey rsa:2048 -keyout privkey.pem -out req.pem
Generating a 2048 bit RSA private key
.......................................+++
........+++
writing new private key to 'privkey.pem'
Enter PEM pass phrase:
Verifying - Enter PEM pass phrase:
-----
You are about to be asked to enter information that will be incorporated
into your certificate request.
What you are about to enter is what is called a Distinguished Name or a DN.
There are quite a few fields but you can leave some blank
For some fields there will be a default value,
If you enter '.', the field will be left blank.
-----
Country Name (2 letter code) [XX]:
State or Province Name (full name) []:
Locality Name (eg, city) [Default City]:
Organization Name (eg, company) [Default Company Ltd]:
Organizational Unit Name (eg, section) []:
Common Name (eg, your name or your server's hostname) []:
Email Address []:

Please enter the following 'extra' attributes
to be sent with your certificate request
A challenge password []:
An optional company name []:

[root@node1 openssl]# openssl x509 -req -in req.pem -signkey privkey.pem -out cert.pem
Signature ok
subject=/C=XX/L=Default City/O=Default Company Ltd
Getting Private key
Enter pass phrase for privkey.pem:
[root@node1 openssl]# ls -al
total 20
drwxr-xr-x  2 root root 4096 May  5 10:47 .
dr-xr-x---. 9 root root 4096 May  4 04:38 ..
-rw-r--r--  1 root root 1103 May  5 10:47 cert.pem
-rw-r--r--  1 root root 1834 May  5 10:43 privkey.pem
-rw-r--r--  1 root root  952 May  5 10:43 req.pem
[root@node1 openssl]# rm -rf req.pem 
SAMPLE USAGE
ENCRYPT
[root@node1 openssl]# echo "test" | openssl smime -encrypt -aes256 -binary -outform DER cert.pem > test.dat
[root@node1 openssl]# cat test.dat 
???0??1?k0?g0O0B1
                 0    UXX10U

                              Default City10U

?V??p?A$????PO??+???q@t??????\"%:0
??J?????5???0?D/?1z-?xO??&?#?;???E>^?g??#7??#m????lA???'??{)?*xM
P?l????]iz/???H???????[root@node1 openssl]#
DECRYPT
[root@node1 openssl]# openssl smime -decrypt -in test.dat -inform DER -inkey privkey.pem -out test.txt
Enter pass phrase for privkey.pem:
[root@node1 openssl]# cat test.txt 
test

ENCRYPT STREAM

[root@node1 ~]# mysqldump --all-databases | openssl smime -encrypt -aes256 -binary -outform DER cert.pem
or 
[root@node1 ~]# innobackupex --stream=tar ./ | openssl smime -encrypt -aes256 -binary -outform DER cert.pem
Step 3 : Stream to Amazon AWS S3

Now that we have secured the data, we will want to pipe the data into an Amazon AWS S3 bucket.  This will provide an offsite copy of the MySQL backup that you can convert to long term storage, or restore into an EC2 instance.  With this method I will only be looking at one.  The Amazon provided AWS CLI tools incorporates working with S3.  Allowing you to copy your files up into S3 with the ability to stream your input.

AWS CLI

In order to tell the AWS CLI S3 copy command to accept STDIN input you just have to put a dash in the place of the source file.  This will allow the command to accept a stream to copy.  The AWS CLI tools for copying into S3 also allows for encryption.  But I wanted to provide other methods as well to allow you to customize your own solution.   You can also stream the download of the S3 bucket item, which could allow for uncompression as you download the data or any other number of options.

UPLOAD STREAM

echo "test" | aws s3 cp - s3://pythian-test-bucket/incoming.txt 

BACKUP / ENCRYPT / UPLOAD STREAM

-- MySQL Dump -> OpenSSL Encryption -> AWS S3 Upload
[root@node1 ~]# mysqldump --all-databases | openssl smime -encrypt -aes256 -binary -outform DER cert.pem | aws s3 cp - s3://pythian-test-bucket/mysqldump.sql.dat
-- Xtrabackup -> OpenSSL Encryption -> AWS S3 Upload
[root@node1 ~]# innobackupex --stream=tar ./ | openssl smime -encrypt -aes256 -binary -outform DER cert.pem |aws s3 cp - s3://pythian-test-bucket/mysqldump.tar.dat
-- MySQL Dump -> GPG Encryption -> AWS S3 Upload
[root@node1 ~]# mysqldump --all-databases | gpg --encrypt -r root | aws s3 cp - s3://pythian-test-bucket/mysqldump.sql.gpg
-- MySQL Dump -> GPG Encryption -> AWS S3 Upload
[root@node1 ~]# innobackupex --stream=tar ./ | gpg --encrypt -r root | aws s3 cp - s3://pythian-test-bucket/mysqldump.tar.gpg

References

  • https://www.percona.com/doc/percona-xtrabackup/2.4/innobackupex/streaming_backups_innobackupex.html
  • https://linuxconfig.org/using-openssl-to-encrypt-messages-and-files-on-linux
  • https://www.gnupg.org/gph/en/manual/c14.html
  • https://www.gnupg.org/gph/en/manual/x110.html
  • https://linuxconfig.org/using-openssl-to-encrypt-messages-and-files-on-linux
  • https://www.openssl.org/docs/manmaster/apps/openssl.html
  • http://docs.aws.amazon.com/cli/latest/reference/s3/cp.html

 

 

Categories: DBA Blogs

OPN Webcast (VAR) Next Generation Oracle Database Appliance

MAY 2016 ...

We share our skills to maximize your revenue!
Categories: DBA Blogs

Links for 2016-05-12 [del.icio.us]

Categories: DBA Blogs

FREE Webinar: Efficient techniques to create and maintain your #Oracle Standby Database

The Oracle Instructor - Fri, 2016-05-13 01:36

Join us with this FREE event on May 19, 13:00 CET and register now, because seats are limited.

DataGuard_Lunchtime_Webinar

I will be talking about

  • how to create and maintain a 12c Standby Database in the most efficient way
  • how to do switchover and failover
  • how to keep up client connectivity after role changes

These topics will be live demonstrated – positively no slide show reading.

After this major part of the event, we will briefly advertise our digital learning offerings that relate to Oracle Database Core Technology and how you may take advantage of them.

Hope to see YOU in the session:-)


Categories: DBA Blogs

Migrating Your Enterprise Applications To Amazon Web Services (AWS)

Pythian Group - Thu, 2016-05-12 15:34

 

Many of the enterprise clients we work with are looking at Amazon Web Services (AWS) to support their cloud strategies and reap the benefits of the public cloud: lower costs, higher scalability, greater availability of computing resources.

 

AWS is well known for its cutting edge service offerings, which are always growing and evolving—making them an easy choice to recommend to our enterprise clients. When used to provide infrastructure as a service (IaaS), it simplifies hardware provisioning and management and makes it easy to allocate compute, memory and storage capacity. In a platform as a service (PaaS) situation, it can streamline operations through automated backups and patching, and can offer the convenience of pay-as-you-go billing.

 

The challenge for many organizations migrating to the cloud comes when they start to move their enterprise applications. That’s because these applications often rely on highly customized infrastructure with tightly coupled components. They also tend to exist in silos. Some careful planning is needed to make sure the new cloud environment delivers the outperforms the legacy on-premises one in terms of scalability, reliability and performance. Whenever desired by the customer, we will often recommend and apply a number of optimizations during the migration process, such as decoupling of components, stability and performance tune-ups as well as improved operational  visibility and platform automation.

 

When we migrated Harvard Business Publishing to AWS, for example, we tailored the public cloud architecture to meet their specific needs. (Harvard Business Publishing is a subsidiary of Harvard University — based in Boston, with offices in New York City, India, Singapore, Qatar and the United Kingdom. They produce and distribute content across multiple platforms.)

 

Harvard Business Publishing was running Oracle applications in a data center on three Oracle Database Appliances. With the data center lease coming up for renewal, they asked us to help migrate their Oracle-based production, quality assurance and disaster recovery systems to an AWS cloud. Obviously, these systems were mission critical.

 

We did thorough capacity planning and developed a reliable, highly-automated and predictable migration approach up front, customized the AWS architecture, and migrated to a large number of virtual machines and Oracle schemas.

 

In another instance, we migrated CityRealty’s core applications to the cloud from on-premise hardware running Oracle databases. CityRealty is the oldest continuously operating real estate website in New York City — the world’s largest real estate market — and remains the city’s leading site today. We proposed moving three production databases to Amazon Elastic Compute Cloud (EC2), which provides highly scalable compute capacity, as well as upgrading the Oracle databases. We also built a highly efficient standby and recovery solution using Amazon Elastic Block Storage (EBS) snapshots.

 

In both of these cases, we did the planning up front to ensure the new cloud environment would be ready to receive the clients’ applications and run them without any compromise in performance. We’ve done similar migrations for clients operating e-commerce businesses with revenues of millions and even billions per year. Every situation is a little different: in some cases clients needed to simplify and standardize their mission-critical systems; in others, boost reliability; in others, increase automation or eliminate transaction-hampering latency.

 

We have the benefit of small, dedicated cloud teams around the world with multiple, advanced AWS certifications to address even the most complex requirements. Our goal always is to ensure the public cloud architecture is ideally suited to the enterprise, and to provide detailed implementation plans and data management solutions for optimal performance. That allows us to implement and manage public, private, and hybrid environments with lower risk and cost for organizations that want to seize the benefits of the cloud.

Find out how Pythian can help you with cloud solutions for AWS  today.

Categories: DBA Blogs

Understanding query slowness after platform change

Bobby Durrett's DBA Blog - Thu, 2016-05-12 14:54

We are moving a production database from 10.2 Oracle on HP-UX 64 bit Itanium to 11.2 Oracle on Linux on 64 bit Intel x86. So, we are upgrading the database software from 10.2 to 11.2. We are also changing endianness from Itanium’s byte order to that of Intel’s x86-64 processors. Also, my tests have shown that the new processors are about twice as fast as the older Itanium CPUs.

Two SQL queries stand out as being a lot slower on the new system although other queries are fine. So, I tried to understand why these particular queries were slower. I will just talk about one query since we saw similar behavior for both. This query has sql_id = aktyyckj710a3.

First I looked at the way the query executed on both systems using a query like this:

select ss.sql_id,
ss.plan_hash_value,
sn.END_INTERVAL_TIME,
ss.executions_delta,
ELAPSED_TIME_DELTA/(executions_delta*1000),
CPU_TIME_DELTA/(executions_delta*1000),
IOWAIT_DELTA/(executions_delta*1000),
CLWAIT_DELTA/(executions_delta*1000),
APWAIT_DELTA/(executions_delta*1000),
CCWAIT_DELTA/(executions_delta*1000),
BUFFER_GETS_DELTA/executions_delta,
DISK_READS_DELTA/executions_delta,
ROWS_PROCESSED_DELTA/executions_delta
from DBA_HIST_SQLSTAT ss,DBA_HIST_SNAPSHOT sn
where ss.sql_id = 'aktyyckj710a3'
and ss.snap_id=sn.snap_id
and executions_delta > 0
and ss.INSTANCE_NUMBER=sn.INSTANCE_NUMBER
order by ss.snap_id,ss.sql_id;

It had a single plan on production and averaged a few seconds per execution:

PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
--------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
      918231698 11-MAY-16 06.00.40.980 PM              195         1364.80228     609.183405    831.563728                  0                      0                      0          35211.9487             1622.4             6974.40513
      918231698 11-MAY-16 07.00.53.532 PM              129         555.981481     144.348698    441.670271                  0                      0                      0          8682.84496         646.984496             1810.51938
      918231698 11-MAY-16 08.00.05.513 PM               39         91.5794872     39.6675128    54.4575897                  0                      0                      0          3055.17949          63.025641             669.153846
      918231698 12-MAY-16 08.00.32.814 AM               35         178.688971     28.0369429    159.676629                  0                      0                      0          1464.28571              190.8             311.485714
      918231698 12-MAY-16 09.00.44.997 AM              124         649.370258     194.895944    486.875758                  0                      0                      0           13447.871         652.806452             2930.23387
      918231698 12-MAY-16 10.00.57.199 AM              168         2174.35909     622.905935    1659.14223                  0                      0             .001303571          38313.1548         2403.28571             8894.42857
      918231698 12-MAY-16 11.00.09.362 AM              213         3712.60403     1100.01973    2781.68793                  0                      0             .000690141          63878.1362               3951             15026.2066
      918231698 12-MAY-16 12.00.21.835 PM              221         2374.74486      741.20133    1741.28251                  0                      0             .000045249          44243.8914         2804.66063               10294.81

On the new Linux system the query was taking 10 times as long to run as in the HP system.

PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms CPU Average ms IO Average ms Cluster Average ms Application Average ms Concurrency Average ms Average buffer gets Average disk reads Average rows processed
--------------- ------------------------- ---------------- ------------------ -------------- ------------- ------------------ ---------------------- ---------------------- ------------------- ------------------ ----------------------
     2834425987 10-MAY-16 07.00.09.243 PM               41         39998.8871     1750.66015    38598.1108                  0                      0                      0          50694.1463         11518.0244             49379.4634
     2834425987 10-MAY-16 08.00.13.522 PM               33         44664.4329     1680.59361    43319.9765                  0                      0                      0          47090.4848         10999.1818             48132.4242
     2834425987 11-MAY-16 11.00.23.769 AM                8          169.75075      60.615125      111.1715                  0                      0                      0             417.375                 92                2763.25
     2834425987 11-MAY-16 12.00.27.950 PM               11         14730.9611     314.497455    14507.0803                  0                      0                      0          8456.63636         2175.63636             4914.90909
     2834425987 11-MAY-16 01.00.33.147 PM                2           1302.774       1301.794             0                  0                      0                      0               78040                  0                  49013
     2834425987 11-MAY-16 02.00.37.442 PM                1           1185.321       1187.813             0                  0                      0                      0               78040                  0                  49013
     2834425987 11-MAY-16 03.00.42.457 PM               14         69612.6197     2409.27829     67697.353                  0                      0                      0          45156.8571         11889.1429             45596.7143
     2834425987 11-MAY-16 04.00.47.326 PM               16         65485.9254     2232.40963    63739.7442                  0                      0                      0          38397.4375         12151.9375             52222.1875
     2834425987 12-MAY-16 08.00.36.402 AM               61         24361.6303     1445.50141    23088.6067                  0                      0                      0          47224.4426         5331.06557              47581.918
     2834425987 12-MAY-16 09.00.40.765 AM               86         38596.7262     1790.56574    37139.4262                  0                      0                      0          46023.0349         9762.01163             48870.0465

The query plans were not the same but they were similar. Also, the number of rows in our test cases were more than the average number of rows per run in production but it still didn’t account for all the differences.

We decided to use an outline hint and SQL Profile to force the HP system’s plan on the queries in the Linux system to see if the same plan would run faster.

It was a pain to run the query with bind variables that are dates for my test so I kind of cheated and replaced the bind variables with literals. First I extracted some example values for the variables from the original system:

select * from 
(select distinct
to_char(sb.LAST_CAPTURED,'YYYY-MM-DD HH24:MI:SS') DATE_TIME,
sb.NAME,
sb.VALUE_STRING 
from 
DBA_HIST_SQLBIND sb
where 
sb.sql_id='aktyyckj710a3' and
sb.WAS_CAPTURED='YES')
order by 
DATE_TIME,
NAME;

Then I got the plan of the query with the bind variables filled in with the literals from the original HP system. Here is how I got the plan without the SQL query itself:

truncate table plan_table;

explain plan into plan_table for 
-- problem query here with bind variables replaced
/

set markup html preformat on

select * from table(dbms_xplan.display('PLAN_TABLE',
NULL,'ADVANCED'));

This plan outputs an outline hint similar to this:

  /*+
      BEGIN_OUTLINE_DATA
      INDEX_RS_ASC(@"SEL$683B0107" 
      ...
      NO_ACCESS(@"SEL$5DA710D3" "VW_NSO_1"@"SEL$5DA710D3")
      OUTLINE(@"SEL$1")
      OUTLINE(@"SEL$2")
      UNNEST(@"SEL$2")
      OUTLINE_LEAF(@"SEL$5DA710D3")
      OUTLINE_LEAF(@"SEL$683B0107")
      ALL_ROWS
      OPT_PARAM('query_rewrite_enabled' 'false')
      OPTIMIZER_FEATURES_ENABLE('10.2.0.3')
      IGNORE_OPTIM_EMBEDDED_HINTS
      END_OUTLINE_DATA
  */

Now, to force aktyyckj710a3 to run on the new system with the same plan as on the original system I had to run the query on the new system with the outline hint and get the plan hash value for the plan that the query uses.

explain plan into plan_table for 
  SELECT 
    /*+
        BEGIN_OUTLINE_DATA
...
        END_OUTLINE_DATA
    */
  *
    FROM
...
Plan hash value: 1022624069

So, I compared the two plans and they were the same but the plan hash values were different. 1022624069 on Linux was the same as 918231698. I think that endianness differences caused the plan_hash_value differences for the same plan.

Then we forced the original HP system plan on to the real sql_id using coe_xfr_sql_profile.sql.

-- build script to load profile

@coe_xfr_sql_profile.sql aktyyckj710a3 1022624069

-- run generated script

@coe_xfr_sql_profile_aktyyckj710a3_1022624069.sql

Sadly, even after forcing the original system’s plan on the new system, the query still ran just as slow. But, at least we were able to remove the plan difference as the source of the problem.

We did notice a high I/O time on the Linux executions. Running AWR reports showed about a 5 millisecond single block read time on Linux and about 1 millisecond on HP. I also graphed this over time using my Python scripts:

Linux db file sequential read (single block read) graph:

Linux

HP-UX db file sequential read graph:

HP

So, in general our source HP system was seeing sub millisecond single block reads but our new Linux system was seeing multiple millisecond reads. So, this lead us to look at differences in the storage system. It seems that the original system was on flash or solid state disk and the new one was not. So, we are going to move the new system to SSD and see how that affects the query performance.

Even though this led to a possible hardware issue I thought it was worth sharing the process I took to get there including eliminating differences in the query plan by matching the plan on the original platform.

Bobby

Postscript:

Our Linux and storage teams moved the new Linux VM to solid state disk and resolved these issues. The query ran about 10 times faster than it did on the original system after moving Linux to SSD.

HP Version:

END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms
------------------------- ---------------- ------------------
02.00.03.099 PM                        245         5341.99923 
03.00.15.282 PM                        250         1280.99632 
04.00.27.536 PM                        341         3976.65855 
05.00.39.887 PM                        125         2619.58894

Linux:

END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Average ms
------------------------- ---------------- ------------------
16-MAY-16 09.00.35.436 AM              162         191.314809
16-MAY-16 10.00.38.835 AM              342         746.313994
16-MAY-16 11.00.42.366 AM              258         461.641705
16-MAY-16 12.00.46.043 PM              280         478.601618

The single block read time is well under 1 millisecond now that 
the Linux database is on SSD.

END_INTERVAL_TIME          number of waits ave microseconds 
-------------------------- --------------- ---------------- 
15-MAY-16 11.00.54.676 PM           544681       515.978687
16-MAY-16 12.00.01.873 AM           828539       502.911935
16-MAY-16 01.00.06.780 AM           518322       1356.92377
16-MAY-16 02.00.10.272 AM            10698       637.953543
16-MAY-16 03.00.13.672 AM              193       628.170984
16-MAY-16 04.00.17.301 AM              112        1799.3125
16-MAY-16 05.00.20.927 AM             1680       318.792262
16-MAY-16 06.00.24.893 AM              140       688.914286
16-MAY-16 07.00.28.693 AM             4837       529.759768
16-MAY-16 08.00.32.242 AM            16082       591.632508
16-MAY-16 09.00.35.436 AM           280927       387.293204
16-MAY-16 10.00.38.835 AM           737846        519.94157
16-MAY-16 11.00.42.366 AM          1113762       428.772997
16-MAY-16 12.00.46.043 PM           562258       510.357372

Sweet!

Categories: DBA Blogs

Database Migration and Integration using AWS DMS

Kubilay Çilkara - Thu, 2016-05-12 14:12


Amazon Web Services (AWS) recently released a product called AWS Data Migration Services (DMS) to migrate data between databases.

The experiment

I have used AWS DMS to try a migration from a source MySQL database to a target MySQL database, a homogeneous database migration.

The DMS service lets you use a resource in the middle Replication Instance - an automatically created EC2 instance - plus source and target Endpoints. Then you move data from the source database to the target database. Simple as that. DMS is also capable of doing heterogeneous database migrations like from MySQL to Oracle and even synchronous integrations. In addition AWS DMS also gives you a client tool called AWS Schema Converter tool which helps you convert your source database objects like stored procedures to the target database format. All things a cloud data integration project needs!

In my experiment and POC, I was particularly interested in the ability of the tool to move a simple data model as below, with 1-n relationship between tables t0(parent) and t1(child) like below.

(Pseudo code to quickly create two tables t0, t1 with 1-n relationship to try it. Create the tables both on source and target database)

t0 -> t1 Table DDL (Pseudo code)

CREATE TABLE `t0` (
  `id` int(11) NOT NULL,
  `txt` varchar(100) CHARACTER SET ucs2 DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

CREATE TABLE `t1` (
  `id` mediumint(9) NOT NULL AUTO_INCREMENT,
  `t0id` int(9) DEFAULT NULL,
  `txt` char(100) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `t0id` (`t0id`),
  CONSTRAINT `t1_ibfk_1` FOREIGN KEY (`t0id`) REFERENCES `t0` (`id`) ON DELETE CASCADE
) ENGINE=InnoDB DEFAULT CHARSET=utf8;


In this experiment, I didn't want to see just a migration, a copy, of a table from source database to a target database. I was interested more to see how easy is to migrate a data model - with Primary Key and Foreign Key relationship in place -  from the source database to the target database with zero downtime and using their CDC (Changed data Capture) or Ongoing-Replication migration option and capabilities of AWS DMS. That is, zero downtime database migration.

Here are the results of the experiment.

AWS DMS is ubiquitous, you can quickly set-up an agent (Replication Instance) and define source & target endpoints and start mapping your tables to be migrated from source database to target database with the tool. All conveniently using the AWS console.

Once you setup your replication instance and endpoints, create a Migration Task (say Alpha) and do an initial full migration (load) from the source database to the target database. Do this with the foreign keys (FKs) disabled on the target. This is a recommendation in the AWS DMS Guide in order to dump the data super fast as it does it with parallel threads, at least this is the recommendations for MySQL targets.

Then you can create a second Migration Task (say Beta) using a different endpoint, but this time with the foreign keys enabled on the target. You can do this even before your full load with Alpha to avoid waiting times. Configure Beta interface/task to run forever and let it integrate and sync the delta which occurred during the initial load. You can even start the Beta interface from a cut-off timestamp point. It uses source MySQL database's binlogs to propagate the changes. If you don't create beta interface, that is to use a different endpoint for the target with the parameter which enables the FKs, the DELETE SQL statements on the source which occur during the migration will not propagate to the target correctly and the CASCADEs to the child tables will not work on the target. CASCADE is a property of the Foreign Key.

To reconcile, to find out if you have migrated everything, I had to count the rows in each table on source and the target databases to monitor and see if it all worked. To do that I used Pentaho Spoon CE to quickly create a job to count the rows on both source and target database and validate migration/integration interfaces.

Overall, I found AWS DMS very easy to use, it quickly helps you wire an integration interface in the Cloud and start pumping and syncing data between sources and targets databases be it on Premise or Cloud. A kind of Middleware setup in AWS style, in the Cloud. No more middleware tools for data migration, AWS now has it's own. 
Categories: DBA Blogs

Properly removing users in MySQL

Pythian Group - Thu, 2016-05-12 09:58

Removing users in MySQL is an easy task, but there are some drawbacks to check carefully before dropping a user. Not taking these possible issues into consideration can render your application unusable.

First it is important to understand the concept of user in MySQL. A user has three main functions: authentication, privileges and resources. This is different from other databases: in MySQL users don’t own objects. An object belongs to a database and there is no direct relationship between objects and users. This simplifies the process of deleting a user because no objects will be erased.

But users, as I wrote before, have an associated set of privileges that define what database sessions can do, and the privileges applied both in stored programs and view execution.

At the same time, procedures, functions, triggers, and views have two possible security contexts: invoker and definer. If they are created using the invoker security model, then the user calling the routine or view must have enough privileges to execute the code within the routine or view. But if created using the definer security model, the code can be executed if the definer has enough privileges to execute it. And yes, the default security model in MySQL is definer.

This means that, unless declared otherwise, most routines will check the privileges for the user that created the routine. If somebody removes that user, querying the view or executing the code will fail with error. All the procedures, functions, views and triggers created by that user with the default options will become unusable.

How do we fix this? The quick and dirty way is to recreate the user with a different password to avoid user logins. It is not an elegant solution but probably this is the first thing you will do while you solve the problem correctly.  Another alternative, if you are running MySQL 5.7 is account locking, this feature disables login for that account but allows code and views to be executed. In any case, it is a good practice to make a backup of the user creation scripts prior to dropping the user. Percona toolkit has the utility pt-show-grants for that purpose.

The elegant way to avoid the problem is to check that there are not routines or views using the definer security model and configured to run with the user privileges of the user you want to remove. There are several tables in the mysql user catalog that provide you with this information.

Unfortunately there is not an easy way to change this attributes. The best thing you can do is drop and recreate those objects using different security characteristics.

Let see an example:

Connect to the database using an account with enough privileges to create users, databases and procedures and create a new database only for testing purposes.

mysql> create database remove_test;
Query OK, 1 row affected (0,05 sec)

Create a user with privileges on the database created in previous step:

mysql> grant all privileges on remove_test.* to test_user@'%' identified by 'test';
Query OK, 0 rows affected, 1 warning (0,20 sec)

Connect to the database using the user created:

$ mysql -u test_user -ptest remove_test

Create a view, check the attributes and execute it. We are going to use a couple of functions that I will explain a bit later.

mysql> create view test_view as select current_user(),user();
Query OK, 0 rows affected (0,05 sec)
mysql> show create view test_view\G
*************************** 1. row ***************************
                View: test_view
         Create View: CREATE ALGORITHM=UNDEFINED DEFINER=`test_user`@`%` SQL SECURITY DEFINER VIEW `test_view` AS select current_user() AS `current_user()`,user() AS `user()`
character_set_client: utf8
collation_connection: utf8_general_ci
1 row in set (0,00 sec)

mysql> select * from test_view;
+----------------+---------------------+
| current_user() | user()              |
+----------------+---------------------+
| test_user@%    | test_user@localhost |
+----------------+---------------------+
1 row in set (0,00 sec)

As we can see, the view has been created with SQL SECURITY DEFINER and DEFINER=`test_user`@`%`. The view returns the value from two functions: current_user() returns the value that matches against the mysql.user table and that defines the privileges the current session or current execution context has. The function user() returns the host you are connected from and the username specified as connection identifier.

Now we reconnect to the database using the privileged account from the first step, and requery the view:

$ mysql -u root -p remove_test
mysql> select * from test_view;
+----------------+----------------+
| current_user() | user()         |
+----------------+----------------+
| test_user@%    | root@localhost |
+----------------+----------------+
1 row in set (0,00 sec)

The view is executed by root@localhost but with the privileges of test_user@%. It is important to note that if you monitor the execution of a routine or view, using SHOW PROCESSLIST or query against information_schema.processlist, the contents of user and host are the same that current_user() return, not the ones returned by user().

Now we will drop the user and query once again the view.

mysql> drop user test_user@'%';
Query OK, 0 rows affected (0,07 sec)
mysql> select * from test_view;
ERROR 1449 (HY000): The user specified as a definer ('test_user'@'%') does not exist

In case you don’t see the error and still get the correct results, this is because the contents of the view are stored in the query cache. Clear their contents and repeat the query.

mysql> reset query cache;
Query OK, 0 rows affected (0,00 sec)

How to validate if it is safe to remove a user? You must query the information_schema tables to find objects than can bring problems and recreate them.

mysql> select routine_schema as db,
    ->        routine_type as object_type,
    ->        routine_name as object_name
    -> from routines
    -> where security_type='DEFINER'
    ->   and definer='test_user@%'
    -> union all
    -> select trigger_schema as db,
    ->        'TRIGGER' as object_type,
    ->         trigger_name as object_name
    -> from triggers
    -> where definer='test_user@%'
    -> union all
    -> select table_schema as db,
    ->        'VIEW' as object_type,
    -> table_name as object_name
    -> from views
    -> where security_type='DEFINER'
    ->   and definer='test_user@%';
+-------------+-------------+-------------+
| db          | object_type | object_name |
+-------------+-------------+-------------+
| remove_test | VIEW        | test_view   |
+-------------+-------------+-------------+
1 row in set (0,02 sec)

Replace test_user@% with the value of the user you want to remove.

This way you get a list of the objects you must change to avoid problems. As I said before the best thing you can do to change the definer is recreating the objects. To obtain the creation script you should use SHOW CREATE VIEW, SHOW CREATE TRIGGER, SHOW CREATE PROCEDURE or SHOW CREATE FUNCTION.

If you want to avoid problems in the future, my recommendation is that for every application, create an account for that application, lock it with an unknown password or using account locking feature. Grant that account all the privileges needed to run the application and make sure all the views, procedures, functions and triggers have that account in the definer field.

Categories: DBA Blogs

Log Buffer #473: A Carnival of the Vanities for DBAs

Pythian Group - Wed, 2016-05-11 16:32

This Log Buffer Edition covers some of the hottest blog posts of Oracle, SQL Server and MySQL for the previous week.

Oracle:

A recent post on the OTN database forum reminded Jonathan how easy it is to forget to keep repeating a piece of information after the first couple of hundred times you’ve explained it. No matter how “intuitively obvious” it is for one person, it’s new to someone else.

The OMS Patcher is a newer patching mechanism for the OMS specifically.

A SQLT report has all kinds of pertinent information including—to name just a few—optimizer settings, indexes, statistics, plan history, and view definitions.

Joins are fundamental in SQL, and are used in most every production query. There are three types in particular that every developer should fully understand.

Why should Oracle Database professionals care about NoSQL and where to start?

SQL Server:

Understanding OPENJSON and FOR JSON in Azure and SQL 2016

Using Data Compression in Master Data Services 2016

The most frustrating thing with any new system is often just working out how to connect to it.

There are several decisions to be made when designing indexes for Memory-optimized tables in In-Memory OLTP, and to make the best choice, it pays to understand something about the nature and usage of memory-optimised indexes.

Database Hardware and Infrastructure Trends

STRING_SPLIT() in SQL Server 2016 : Follow-Up #2

MySQL:

Why would I run MySQL/MariaDB on POWER anyway?

By default, the write() system call returns after all data has been copied from the user space file descriptor into the kernel space buffers. There is no guarantee that data has actually reached the physical storage.

OpenCPS: Vietnam’s Public Sector goes Open Source

MySQL uses metadata locking to manage concurrent access to database objects, and to ensure data consistency when performing modifications to the schema

Using Docker for development is a great way of ensuring that what you develop will be the same that you deploy in production.

Categories: DBA Blogs

Defining Digital Transformation

Pythian Group - Wed, 2016-05-11 15:33

 

Terminology is important—and it’s particularly important for us to define terms that are central to what we do. So when it comes to the subject of digital transformation, what exactly are we talking about?

 

In speaking with clients and industry thought leaders, I’ve come to realize that the term “digital transformation” has a different meaning to different people. For a term that is so widely used — and that, on its surface, seems pretty straightforward — the range of interpretation is remarkable. It’s a bit like when we say “I’ll do it later.”  “Later” to one person means “before the sun goes down today.” “Later” to another person means “sometime in the future”, and it could mean days or weeks in their mind. “Later” to a third person can mean “I have no plans to do it, and this is my way of telling you nicely.”

 

Because the term is so essential to the work we do for our clients, I thought it would be helpful to define what digital transformation means to us here at Pythian. There’s so much we can say on the topic, so I plan to follow up with a series of articles about how I’ve seen it implemented, or worse, not implemented or even embraced as a concept.

 

To start, “digital transformation” is about technology. I know that to some people it isn’t, but I disagree. These days, you can’t transform your business without technology. It’s not about which technology you choose, as much as it’s about how to use it. Even more specifically, we’ve found that the businesses that are achieving positive transformation are using technology to capitalize on data. I have yet to see a single transformation project that didn’t use data as a major component of its success.

 

Let’s look at the term “transformation.” This equates to change, but it doesn’t mean change for its own sake. The change we’re talking about has to benefit the business. However, the factor that can make or break successful change is people. Their attitudes, preconceptions, and ideas almost always have to be aligned with the change for successful transformation to occur. People need to get behind the initiative, people have to fund it, people have to develop it, and people have to support it once it’s developed. And we all know that getting people to change can be more difficult than developing any new technology. In short, the transformative capabilities inherent in technology can only be realized when coupled with the willingness to embrace change.

Why Digital Transformation?

Why is the concept of digital transformation important in the first place? At Pythian, we believe that it’s about using technology and data to change your business for the better. What do we mean when we say “for the better”? Therein lies the controversy.  “For the better” means different things to different people depending on their company’s key objectives.

 

“For the better” can mean:

  • Becoming more efficient to drive costs down so your profitability can improve
  • Reducing mistakes and improving your reputation, or the quality of your product
  • Differentiating your product to get ahead of the competition
  • Doing what you do, only faster than your competitors
  • Creating new revenue streams
  • Improving the customer experience. This is a big one, so I will dedicate an entire blog post to exploring exactly what it means.

 

Digital transformation is the key to achieving any one, or all of these benefits, and knowing your objectives and priorities will help you shape your digital transformation initiative. So to start, focus less on what digital transformation is, and more on what you want the outcome of a transformation to be.

 

Categories: DBA Blogs

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Learn DB Concepts with me... - Wed, 2016-05-11 12:50
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