nodetool commands

 

ubuntu@ds201-node1:~$ /home/ubuntu/node1/resources/cassandra/bin/nodetool status aug_space;

Datacenter: Cassandra

=====================

Status=Up/Down

|/ State=Normal/Leaving/Joining/Moving

--  Address    Load       Tokens       Owns (effective)  Host ID                               Rack

UN  127.0.0.1  380.53 KiB  1            77.1%             39e7bbfa-2250-41a2-975b-f6a32e8677d0  rack1

UN  127.0.0.2  365.82 KiB  1            89.2%             606d2798-fa58-4656-bcba-5ef8a9c5012f  rack1

UN  127.0.0.3  335.28 KiB  1            89.2%             ab14e681-7a13-4027-8b4c-0f40a2428a85  rack1

UN  127.0.0.4  323.7 KiB  1            44.6%             a411cdb0-f8a1-4f4b-ac44-c1b4ff703124  rack1

since replication factor in  aug_space  is 3 hence total sum of all data % is 300 % 

ubuntu@ds201-node1:~$ /home/ubuntu/node1/resources/cassandra/bin/nodetool info

ID                     : 39e7bbfa-2250-41a2-975b-f6a32e8677d0

Gossip active          : true

Native Transport active: true

Load                   : 392.57 KiB

Generation No          : 1694106957

Uptime (seconds)       : 145381

Heap Memory (MB)       : 374.69 / 512.00

Off Heap Memory (MB)   : 0.00

Data Center            : Cassandra

Rack                   : rack1

Exceptions             : 0

Key Cache              : entries 0, size 0 bytes, capacity 25 MiB, 0 hits, 0 requests, NaN recent hit rate, 14400 save period in seconds

Row Cache              : entries 0, size 0 bytes, capacity 0 bytes, 0 hits, 0 requests, NaN recent hit rate, 0 save period in seconds

Counter Cache          : entries 52, size 6.65 KiB, capacity 12 MiB, 0 hits, 0 requests, NaN recent hit rate, 7200 save period in seconds

Chunk Cache            : entries 702, size 22.2 MiB, capacity 817 MiB, 702 misses, 18130 requests, 0.961 recent hit rate, 2358.204 microseconds miss latency

Percent Repaired       : 0.0%

Token                  : 0

ubuntu@ds201-node1:~$ /honme/ubuntu/node2/resources/cassandra/bin/nodetool info

bash: /honme/ubuntu/node2/resources/cassandra/bin/nodetool: No such file or directory

ubuntu@ds201-node1:~$ /home/ubuntu/node2/resources/cassandra/bin/nodetool info

ID                     : 606d2798-fa58-4656-bcba-5ef8a9c5012f

Gossip active          : true

Native Transport active: true

Load                   : 359.02 KiB

Generation No          : 1694107334

Uptime (seconds)       : 145059

Heap Memory (MB)       : 378.41 / 512.00

Off Heap Memory (MB)   : 0.00

Data Center            : Cassandra

Rack                   : rack1

Exceptions             : 0

Key Cache              : entries 0, size 0 bytes, capacity 25 MiB, 0 hits, 0 requests, NaN recent hit rate, 14400 save period in seconds

Row Cache              : entries 0, size 0 bytes, capacity 0 bytes, 0 hits, 0 requests, NaN recent hit rate, 0 save period in seconds

Counter Cache          : entries 28, size 3.59 KiB, capacity 12 MiB, 0 hits, 0 requests, NaN recent hit rate, 7200 save period in seconds

Chunk Cache            : entries 736, size 23.56 MiB, capacity 817 MiB, 736 misses, 15944 requests, 0.954 recent hit rate, 7143.424 microseconds miss latency

Percent Repaired       : 0.0%

Token                  : 4223372036854775807

clustering key restrictions

 cqlsh:aug_space> select * from movies_by_ratings;

 age_rating | movie_id                             | genres                | release_date                    | title

------------+--------------------------------------+-----------------------+---------------------------------+-----------------

          5 | 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2 |                  null | 2023-09-09 18:27:16.646000+0000 |            null

          5 | 93762f96-005c-4bd9-8994-1a6454a91334 |                  null | 2023-09-09 18:27:16.646000+0000 |            null

          8 | 236d3970-97f1-4f24-9bb1-d657cc698dac | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Jurassic Park 2

          8 | 6ae5a279-36e0-49db-b39d-f9b582f082a3 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |       Avatar  2

          7 | d9573b24-02cb-49b4-91ba-c040c5a0127a | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |          Avatar

          9 | 2e70b67b-e6f7-4cf3-9493-b8e21641094d | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 3

          9 | 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 2

          9 | 7b085f61-4202-4bd8-b84c-f2a46baf0853 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Sturat Little

          9 | 93762f96-005c-4bd9-8994-1a6454a91334 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Jurassic Park

(9 rows)

cqlsh:aug_space> select * from movies_by_ratings order by movie_id desc;

InvalidRequest: Error from server: code=2200 [Invalid query] message="ORDER BY is only supported when the partition key is restricted by an EQ or an IN."

cqlsh:aug_space> select * from movies_by_ratings where age_rating=9 order by movie_id asc;

 age_rating | movie_id                             | genres                | release_date                    | title

------------+--------------------------------------+-----------------------+---------------------------------+-----------------

          9 | 2e70b67b-e6f7-4cf3-9493-b8e21641094d | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 3

          9 | 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 2

          9 | 7b085f61-4202-4bd8-b84c-f2a46baf0853 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Sturat Little

          9 | 93762f96-005c-4bd9-8994-1a6454a91334 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Jurassic Park

(4 rows)

cqlsh:aug_space> select * from movies_by_ratings where age_rating=9 order by title desc;

InvalidRequest: Error from server: code=2200 [Invalid query] message="Order by is currently only supported on the clustered columns of the PRIMARY KEY, got title"

Gossip Protocol

Gossip is a peer-to-peer communication protocol in which nodes periodically exchange state information about themselves and about other nodes they know about. The gossip process runs every second and exchanges state messages with up to three other nodes in the cluster. The nodes exchange information about themselves and about the other nodes that they have gossiped about, so all nodes quickly learn about all other nodes in the cluster. A gossip message has a version associated with it, so that during a gossip exchange, older information is overwritten with the most current state for a particular node. 

Here is how the gossiper works: 

1. Once per second, the gossiper will choose a random node in the cluster and initi‐ alize a gossip session with it. Each round of gossip requires three messages.

2. The gossip initiator sends its chosen friend a GossipDigestSyn message. 

3. When the friend receives this message, it returns a GossipDigestAck message.

4. When the initiator receives the ack message from the friend, it sends the friend a GossipDigestAck2 message to complete the round of gossip.

more -> https://docs.datastax.com/en/cassandra-oss/3.x/cassandra/architecture/archGossipAbout.html

ubuntu@ds201-node1:~$ /home/ubuntu/node1/resources/cassandra/bin/nodetool describecluster

Cluster Information:

Name: Test Cluster

Snitch: com.datastax.bdp.snitch.DseDelegateSnitch

DynamicEndPointSnitch: enabled

Partitioner: org.apache.cassandra.dht.Murmur3Partitioner

Schema versions:

00abff53-2837-363a-abd8-b75594c5631e: [127.0.0.1, 127.0.0.2, 127.0.0.3, 127.0.0.4]

ubuntu@ds201-node1:~$ /home/ubuntu/node1/resources/cassandra/bin/nodetool status aug_space;

Datacenter: Cassandra

=====================

Status=Up/Down

|/ State=Normal/Leaving/Joining/Moving

--  Address    Load       Tokens       Owns (effective)  Host ID                               Rack

UN  127.0.0.1  380.53 KiB  1            77.1%             39e7bbfa-2250-41a2-975b-f6a32e8677d0  rack1

UN  127.0.0.2  365.82 KiB  1            89.2%             606d2798-fa58-4656-bcba-5ef8a9c5012f  rack1

UN  127.0.0.3  335.28 KiB  1            89.2%             ab14e681-7a13-4027-8b4c-0f40a2428a85  rack1

UN  127.0.0.4  323.7 KiB  1            44.6%             a411cdb0-f8a1-4f4b-ac44-c1b4ff703124  rack1

ubuntu@ds201-node1:~$ /home/ubuntu/node1/resources/cassandra/bin/nodetool gossipinfo;

/127.0.0.1

  generation:1694106957

  heartbeat:120749

  STATUS:25:NORMAL,0

  LOAD:120733:389660.0

  SCHEMA:117734:00abff53-2837-363a-abd8-b75594c5631e

  DC:41:Cassandra

  RACK:18:rack1

  RELEASE_VERSION:4:4.0.0.2284

  NATIVE_TRANSPORT_ADDRESS:3:127.0.0.1

  X_11_PADDING:92056:{"dse_version":"6.0.0","workloads":"Cassandra","workload":"Cassandra","active":"true","server_id":"08-00-27-32-1E-DD","graph":false,"health":0.9}

  NET_VERSION:1:256

  HOST_ID:2:39e7bbfa-2250-41a2-975b-f6a32e8677d0

  NATIVE_TRANSPORT_READY:54:true

  NATIVE_TRANSPORT_PORT:6:9041

  NATIVE_TRANSPORT_PORT_SSL:7:9041

  STORAGE_PORT:8:7000

  STORAGE_PORT_SSL:9:7001

  JMX_PORT:10:7199

  TOKENS:24:<hidden>

/127.0.0.2

  generation:1694107334

  heartbeat:120358

  STATUS:51:NORMAL,4223372036854775807

  LOAD:120300:374600.0

  SCHEMA:117343:00abff53-2837-363a-abd8-b75594c5631e

  DC:65:Cassandra

  RACK:18:rack1

  RELEASE_VERSION:4:4.0.0.2284

  NATIVE_TRANSPORT_ADDRESS:3:127.0

Secondary index in cassandra

 

cqlsh:aug_space> select * from movies;

 movie_id                             | age_rating | genres                | release_date                    | title

--------------------------------------+------------+-----------------------+---------------------------------+-----------------

 236d3970-97f1-4f24-9bb1-d657cc698dac |          8 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Jurassic Park 2

 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2 |          9 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 2

 6ae5a279-36e0-49db-b39d-f9b582f082a3 |          8 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |       Avatar  2

 d9573b24-02cb-49b4-91ba-c040c5a0127a |          7 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |          Avatar

 93762f96-005c-4bd9-8994-1a6454a91334 |          9 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Jurassic Park

 7b085f61-4202-4bd8-b84c-f2a46baf0853 |          9 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Sturat Little

cqlsh:aug_space> select * from movies where title='avatar';

InvalidRequest: Error from server: code=2200 [Invalid query] message="Cannot execute this query as it might involve data filtering and thus may have unpredictable performance. If you want to execute this query despite the performance unpredictability, use ALLOW FILTERING"

cqlsh:aug_space> select  token(movie_id) , movie_id from movies;

 system.token(movie_id) | movie_id

------------------------+--------------------------------------

   -7679664659544709434 | 236d3970-97f1-4f24-9bb1-d657cc698dac

   -5854020856241272382 | 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2

   -5353160429242411980 | 6ae5a279-36e0-49db-b39d-f9b582f082a3

   -5014329844387628670 | d9573b24-02cb-49b4-91ba-c040c5a0127a

    3478065203213068432 | 93762f96-005c-4bd9-8994-1a6454a91334

    6637426536803809222 | 7b085f61-4202-4bd8-b84c-f2a46baf0853

(6 rows)

cqlsh:aug_space> CREATE INDEX IF NOT EXISTS ON movies (title);

cqlsh:aug_space> select * from movies where title='Avatar';

 movie_id                             | age_rating | genres                | release_date                    | title

--------------------------------------+------------+-----------------------+---------------------------------+--------

 d9573b24-02cb-49b4-91ba-c040c5a0127a |          7 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Avatar

cqlsh:aug_space> select * from movies_by_ratings where age_rating=9;

 age_rating | movie_id                             | genres                | release_date                    | title

------------+--------------------------------------+-----------------------+---------------------------------+-----------------

          9 | 41d8ffdf-fb1e-43d0-85ae-9c4f358f35c2 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 | Sturat Little 2

          9 | 7b085f61-4202-4bd8-b84c-f2a46baf0853 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Sturat Little

          9 | 93762f96-005c-4bd9-8994-1a6454a91334 | {'animation', 'kids'} | 2023-09-09 00:00:00.000000+0000 |   Jurassic Park

(3 rows)

cqlsh:aug_space> desc table movies;

CREATE TABLE aug_space.movies (

    movie_id uuid PRIMARY KEY,

    age_rating int,

    genres set<text>,

    release_date timestamp,

    title text

) WITH bloom_filter_fp_chance = 0.01

    AND caching = {'keys': 'ALL', 'rows_per_partition': 'NONE'}

    AND comment = ''

    AND compaction = {'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy', 'max_threshold': '32', 'min_threshold': '4'}

    AND compression = {'chunk_length_in_kb': '64', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor'}

    AND crc_check_chance = 1.0

    AND dclocal_read_repair_chance = 0.1

    AND default_time_to_live = 0

    AND gc_grace_seconds = 864000

    AND max_index_interval = 2048

    AND memtable_flush_period_in_ms = 0

    AND min_index_interval = 128

    AND read_repair_chance = 0.0

    AND speculative_retry = '99PERCENTILE';

CREATE INDEX movies_title_idx ON aug_space.movies (title);

CREATE INDEX movies_age_rating_idx ON aug_space.movies (age_rating);

for more information -->

SASI & regular index ---

https://www.baeldung.com/cassandra-secondary-indexes 

Use Where Clause in Cassandra - CQL

 

How to use Where Clause in Cassandra (CQL)

Where clause in Cassandra Query Language is used to filter the records.  To filter the records, you will be using 

• Partition Key ( Single or composite)
• A cluster column with Partition Key
• A column on which you have created the index
• A column which is not part of Partition Key or no index is created on it, you can use in where clause but you have to use with Allow Filtering.

Consistency level for LWT in Cassandra

 How to set consistency level  and serial consistency.

Cassandra’s lightweight transactions are limited to a single partition. Internally, Cassandra stores a Paxos state for each partition. This ensures that transactions on different partitions cannot interfere with each other.

cqlsh:aug_space> consistency ONE;

Consistency level set to ONE.

cqlsh:aug_space> SERIAL CONSISTENCY;

Current serial consistency level is SERIAL.

cqlsh:aug_space> Insert into bank_emp_record(Name, bank_name, Id) values('Dinesh', 'Axis Bank', 201) IF NOT EXISTS; 

 [applied] | name   | id  | bank_name

-----------+--------+-----+-----------

     False | Dinesh | 201 | Axis Bank

cqlsh:aug_space> INSERT INTO users (id, addresses, emails, ip_numbers) VALUES (UUID(), {'home':'192, 10th cross, wilson'} ,['shweta001@yahoo.com'],{'10.10.11.1', '10.10.10.1', '10.10.12.2'}) IF NOT EXISTS;

 [applied]

-----------

      True

cqlsh:aug_space> select * from users;

 id                                   | addresses                             | emails                  | ip_numbers

--------------------------------------+---------------------------------------+-------------------------+--------------------------------------------

 44b37d64-ba2b-435f-99a7-c5a4bc81a10c | {'home': ('192, 10th cross, wilson')} | ['shweta001@yahoo.com'] | {'10.10.10.1', '10.10.11.1', '10.10.12.2'}

 dd463353-a538-430d-810f-7df19a3cc148 | {'home': ('192, 10th cross, wilson')} | ['shweta001@yahoo.com'] | {'10.10.10.1', '10.10.11.1', '10.10.12.2'}

 1c914387-7e4f-4e69-9aad-72d9b854d31d | {'home': ('192, 10th cross, wilson')} | ['shweta001@yahoo.com'] | {'10.10.10.1', '10.10.11.1', '10.10.12.2'}

 6ab09bec-e68e-48d9-a5f8-97e6fb4c9b47 |                                  null |                    null |               {'10.10.14.1', '10.10.24.1'}

 732d1cb7-53cf-4b0a-892e-3edfcc2c1d51 | {'home': ('192, 10th cross, wilson')} | ['shweta001@yahoo.com'] | {'10.10.10.1', '10.10.11.1', '10.10.12.2'}

(5 rows)

cqlsh:aug_space> INSERT INTO users (id, addresses, emails, ip_numbers) VALUES (1234567, {'home':'192, 10th cross, wilson'} ,['shweta001@yahoo.com'],{'10.10.11.1', '10.10.10.1', '10.10.12.2'}) IF NOT EXISTS;

InvalidRequest: Error from server: code=2200 [Invalid query] message="Invalid INTEGER constant (1234567) for "id" of type uuid"

cqlsh:aug_space> INSERT INTO users (id, addresses, emails, ip_numbers) VALUES (732d1cb7-53cf-4b0a-892e-3edfcc2c1d51, {'home':'192, 10th cross, wilson'} ,['shweta001@yahoo.com'],{'10.10.11.1', '10.10.10.1', '10.10.12.2'}) IF NOT EXISTS;

 [applied] | id                                   | addresses                             | emails                  | ip_numbers

-----------+--------------------------------------+---------------------------------------+-------------------------+--------------------------------------------

     False | 732d1cb7-53cf-4b0a-892e-3edfcc2c1d51 | {'home': ('192, 10th cross, wilson')} | ['shweta001@yahoo.com'] | {'10.10.10.1', '10.10.11.1', '10.10.12.2'}

cqlsh:aug_space> 

cqlsh:aug_space>  select * from bank_emp_record;

 name   | id  | bank_name

--------+-----+--------------------------

  David | 410 |               ICICI Bank

 Dinesh | 201 |                Axis Bank

 Dinesh | 202 |                Axis Bank

 Dinesh | 203 |                Axis Bank

 Dinesh | 204 |                Axis Bank

 Ashish | 101 | Employee bank change new

 Ashish | 102 | Employee bank change new

 Ashish | 103 | Employee bank change new

 Ashish | 104 | Employee bank change new

 Ashish | 105 | Employee bank change new

(10 rows)

cqlsh:aug_space>  update bank_emp_record set bank_name ='HDFC Bank'  where name='David'  IF EXISTS;

 [applied]

-----------

      True

cqlsh:aug_space>  update bank_emp_record set bank_name ='ICICI Bank'  where name='David' ;

cqlsh:aug_space>  select * from bank_emp_record;

 name   | id  | bank_name

--------+-----+--------------------------

  David | 410 |               ICICI Bank

 Dinesh | 201 |                Axis Bank

 Dinesh | 202 |                Axis Bank

 Dinesh | 203 |                Axis Bank

 Dinesh | 204 |                Axis Bank

 Ashish | 101 | Employee bank change new

 Ashish | 102 | Employee bank change new

 Ashish | 103 | Employee bank change new

 Ashish | 104 | Employee bank change new

 Ashish | 105 | Employee bank change new

(10 rows)

cqlsh:aug_space>  update bank_emp_record set bank_name ='ICICI Bank'  where name='David'  IF bank_name ='HDFC Bank' ;

 [applied] | bank_name

-----------+------------

     False | ICICI Bank

Quick useful Cassandra links

changing  Vnode  impact on Cluster --> https://thelastpickle.com/blog/2021/01/29/impacts-of-changing-the-number-of-vnodes.html

ER diagram  shapes  --> https://www.geeksforgeeks.org/introduction-of-er-model/ 

https://www.edureka.co/blog/interview-questions/cassandra-interview-questions/

https://www.baeldung.com/cassandra-consistency-levels

https://www.instaclustr.com/support/documentation/cassandra/using-cassandra/managing-tombstones-in-cassandra/#section-when-do-tombstones-cause-problems

https://www.instaclustr.com/blog/apache-cassandra-compaction/

 Data modelling in cassandra

https://www.datastax.com/learn/data-modeling-by-example

I am getting difficulty in understanding vnode cluster concept . Then i watched https://www.youtube.com/watch?v=xhclDLmhdrc&list=PLtZafdfRI4M5blB0jpYtlg70l-W4xVQnO&index=10&t=903s  video twice . Now only the concept of vNode is bit clear.

Query First approach 

Cassandra gets a lot of its speed in database reads and writes from the fact that it never has to perform any joins on the database in fact in Cassandra it’s impossible to perform joins, so instead of taking a relational model approach we take a query first approach, this means we design our tables for a specific query, as a result, we want to have every table which is catered for a specific query, rather than flexible tables such as this employee table and the company car table, hence, we only ever have to query one table when we’re reading or writing data, obviously there are some consequences to this approach, as we might end up writing the same data to multiple tables just to satisfy different queries but it’s ideal for a Cassandra’s distributed architecture.

Read  more on read repair 

Cassandra is key value or columnar

 Cassandra is not a columnar database.
A columnar/column-store/column-oriented database, as you said, guarantees data locality for a single column, within a given node, on disk. This is a column that spans many or all rows depending on if, or how, you specify partitions and what the database supports.

Cassandra is a column-family* store. A column-family store ensures data locality at the partition level, not the column level. In a database like Cassandra a partition is a group of rows and columns split up by a specified partition key, then clustered together by specified clustering column(s) (optional). To query Cassandra, you must know, at a minimum, the partition key in order to avoid full scans of your data.
All data for a given partition in Cassandra is guaranteed to be on the same node and in a given file (SSTable) in the same location within that file. The one thing to note here is that depending on your compaction strategy, the partition can be split across multiple files on disk, so data locality on disk is not a guarantee.

Column stores lend themselves to, and are designed for, analytic workloads. Because each column is in the same location on disk, they can read all information for a given column across many/all rows incredibly fast. This comes at the cost of very slow writes which usually need to be done in batch loads to avoid drastic performance implications.

Column-family stores, like Cassandra, are a great choice if you have high throughput writes and want to be able to linearly scale horizontally. Reads that use the partition key are incredibly fast as the partition key defines where the data resides. The downfall here is that if you need to do any sort of ad-hoc query, a full scan of all data is required.
* The term "column-family" comes from the original storage engine that was a key/value store, where the value was a "family" of column/value tuples. There was no hard limit on the number of columns that each key could have. In Cassandra this was later abstracted into "partitions", then eventually the storage engine was modified to match the abstraction.

Courtsy --> https://www.quora.com/How-is-Cassandra-a-columnar-database

Quick Vs Merge Vs Heap Sort

 

 Time complexity

Quick sort  Worst Case = O(n2) Average case and best case: O(n log n)

 

Merge sort Worst case = Average Case = Best Case = O(n log n)

 

Heap Sort Worst case = Average Case = Best Case = O(n log n) 

 

 

Space complexity

Quick sort  Worst Case = O(n) Average case and best case: O( log n)

This happens when the pivot element’s correct position in the partitioned array is in the middle every time. The size of subarrays will be half the size of the original array. In this case, the recursive tree’s size will be O(log n). 

 

Merge sort Worst case = O(n) Average Case = Best Case = O( log n)

Since we use an auxiliary array of size at most n to store the merged subarray, the space complexity is O(n).

Heap sort   O(1)  Doesnt require extra space

 

 

Exception class Hierarchy

HashMap Add element Snapshot

Bubble Vs selection Vs insertion sort

Jdk8 Features used

Quick Overview of Java 8 Features Some of the important Java 8 features are;

• forEach() method in Iterable interface
• default and static methods in Interfaces
• Functional Interfaces and Lambda Expressions
• Java Stream API for Bulk Data Operations on Collections
• Java Time API
• Collection API improvements
• Concurrency API improvements
• Java IO improvements

@FunactionalInterface indicates that an interface has only 1 abstract method . It is not mandatory to use this annotation , but just to avoid accidental addition of other methods .

default and static methods in Interfaces are allowed in JDK8

forEach() method in Iterable interface - wrote a program to create new set

Java Stream API for Bulk Data Operations on Collections - write a program for implementing it

Kerberos Protocol

Kerberos protocol provides authentication to any service. It could be DB service or Web Service. If an application wants to connec to DB (say) service , then it provides username /password to connect to DB. But if in same application iKerberos is implemented , then instead of directly connecting to DB service , the application gets it connected through kerberos . 

• First App gets connected to Kerberos's Authentication Server by sending its client ID . 
• AS verifies the application and sends a TGT token in its response . 
• App after getting TGT sends a request to connect to TGT server to connect DB service. 
• TGT server after validating DB service from its service list responds back with Access token to the application. 
• App then uses this Acess token each time when it needs to connect to DB. 
• This Access Token contains the encrypted info about TTL. After the time expires Kerberos refreshes Access Token . -- This point is not clear to me. I will describe it later.

NDM Vs FTP

Recently i have come to know about NDM protocol . NDM was designed for File transfer between Mainframe systems. Later  on it was used for  other computers . It best suitable for  large files.
It offer various features like data compression and checkpoint restart .
given below are the few differences between NDM and FTP .

NDM offers for logging, audit, and control features.

FTP does not have elaborate processes for logging and auditing. 

NDM is an asset when dealing with large data packets through its fully automated feature.

FTP requires thorough follow-up of the processes for file transfers and downloads. 

Data security is guaranteed with NDM which offers encryption.

FTP has the encryption option desired for improved data security.

NDM compresses data and therefore uses a low bandwidth.

FTP does not compress data and uses a higher bandwidth.

The checkpoint restart feature is offered in NDM.

FTP does not allow for checkpoint restart. 

NDM offers pre- and post-processing features.

FTP only has post-processing features.

Read more:  Differences Between NDM and FTP | Difference Between http://www.differencebetween.net/technology/differences-between-ndm-and-ftp/#ixzz5kzyrtowE

NDM later was taken by IBM . It is  now known as CONNECT:DIRECT 

ONNECT:Direct (NDM)is a product of Sterling Commerce www.stercomm.com. It is a product designed to automate and manage the exchange of files between two computers. Some of the major differences between the two:

• CONNECT:Direct provides logging, statistics. and audit control features.
• CONNECT:Direct has a built-in scheduling system for automating file transfers.
• CONNECT:Direct manages the file transfer process once it has been submitted.
• CONNECT:Direct can use both TCP/IP and SNA networking protocols as well as direct channels between computers.
• CONNECT:Direct has batch, CICS, ISPF, and web-based interfaces.
• CONNECT:Direct has its own scripting (i.e. process) language.
• CONNECT:Direct has pre- and post-processing features.
• CONNECT:Direct can be monitored with SNMP and/or other automation tools.
• CONNECT:Direct allows you to track what is happening while a transfer is happening, as well as tracking the final status of the file exchange process.
• CONNECT:Direct works with all data types (QSAM, VSAM, PDS, binary).
• CONNECT:Direct can interface with your JES Spool output.
• CONNECT:Direct can interface with your email server.
• CONNECT:Direct can scan directories for the presence of a file, and then initiate a transfer process upon receipt of the file within that directory.
• One vendor means that there are no compatibility issues.
• Check-point restarts of large transfers.

Read more: http://ibmmainframes.com/about9768.html#ixzz5l08CX0PD

volatile usage --problem

import java.util.ArrayList;



public class ThreadTest {

public static void main(String[] args) {

ArrayList arr = new ArrayList();

TestTwo bobj = new TestTwo();

for (int  i =0; i<10 font="" i="">
arr.add(new MyTh(i,bobj));


for (int  i =0; i<10 font="" i="">
{
((Thread ) arr.get(i)).start();


}

}

}



class MyTh extends Thread{


int  num ;

static int  age ;


volatile StringBuffer buff = new StringBuffer("0");

static  StringBuffer buff2 = new StringBuffer("0");

TestTwo obj ;

MyTh (int  k, TestTwo cobj )

{this.num=k; 
this.obj=cobj;} 

@Override
public void run() {

if(this.num==6){
//setAge(40);
this.obj.count.append("hi");
this.buff.append("hi");
this.buff2.append("hi");
this.age =40;
    }
System.out.println(Thread.currentThread().getName() + " :"+this.num + ":" + this.age +":"+
" :[" +this.buff2 +":"+ this.buff2.hashCode() + 
    "] : [" + this.buff + " :"+ this.buff.hashCode() +"] :[ "+ this.obj.count+ ":"+this.obj.count.hashCode() +":]");

}

int   getAge(){
return age;
}

void  setAge(int  k){
this.age =k;
}

}


class TestTwo {

volatile StringBuffer count = new StringBuffer("0");


}


------------



Thread-0 :0:0: :[0:1740525517] : [0 :1769581898] :[ 0:1833313469:]
Thread-1 :1:0: :[0:1740525517] : [0 :857628581] :[ 0:1833313469:]
Thread-2 :2:0: :[0:1740525517] : [0 :1498557318] :[ 0:1833313469:]
Thread-3 :3:0: :[0:1740525517] : [0 :1419206900] :[ 0:1833313469:]
Thread-4 :4:0: :[0:1740525517] : [0 :313434985] :[ 0:1833313469:]
Thread-5 :5:0: :[0:1740525517] : [0 :1644604984] :[ 0:1833313469:]
Thread-6 :6:40: :[0hi:1740525517] : [0hi :1055613344] :[ 0hi:1833313469:]
Thread-7 :7:40: :[0hi:1740525517] : [0 :1151033700] :[ 0hi:1833313469:]
Thread-8 :8:40: :[0hi:1740525517] : [0 :1472482392] :[ 0hi:1833313469:]
Thread-9 :9:40: :[0hi:1740525517] : [0 :1908885604] :[ 0hi:1833313469:]

Java Tidbits .........(char vs byte stream )

A stream is a way of sequentially accessing a file. In Streams you can process the data one at a time as bulk operations are unavailable with them. But, streams supports a huge range of source and destinations including disk file, arrays, other devices, other programs etc. In Java, a byte is not the same thing as a char . Therefore a byte stream is different from a character stream. So, Java defines two types of streams: Byte Streams and Character Streams .

Byte Streams

A byte stream access the file byte by byte. Java programs use byte streams to perform input and output of 8-bit bytes. It is suitable for any kind of file, however not quite appropriate for text files. For example, if the file is using a unicode encoding and a character is represented with two bytes, the byte stream will treat these separately and you will need to do the conversion yourself. Byte oriented streams do not use any encoding scheme while Character oriented streams use character encoding scheme(UNICODE). All byte stream classes are descended from InputStream and OutputStream .