Nuffing.com

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Axios’ CEO said this week that regular citizens can’t be journalists because they don’t have fancy credentials and demanded that we take corporate news seriously again.

Not the Bee

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Gizmodo

I recently became aware of WeSQL. A MySQL-compatible database that separates compute and storage, using S3 as the storage layer. The product uses a columnar format by default which is significantly more space-efficient than InnoDB.

WeSQL introduces a new storage engine called SmartEngine using a LSM-tree-based structure that is ideal for a storage bucket implementation, and documentation shows the implementation of raft replication to combat latency concerns. There is a lot more information to review, the serverless architecture and WeScale, a database proxy and resource manager.

It was very easy to take it for an initial spin using a docker container and an AWS S3 bucket. I would really like to try CloudFlare R2 which implements the S3 API.

Under the covers there are over 180 new variables comprising 83 for the smartengine, 57 for raft, and 22 for objectstore and more. This implies a lot of tunable options and a lot of complexity to optimize for a variety of workloads using the 79 new status variables.

I was able to launch a demo and confirm

mysql> SELECT VERSION();
+-----------+
| VERSION() |
+-----------+
| 8.0.35    |
+-----------+
1 row in set (0.01 sec)

mysql> SELECT @@wesql_version;
+-----------------+
| @@wesql_version |
+-----------------+
| 0.1.0           |
+-----------------+
1 row in set (0.00 sec)

One of my early tests showed that it does not support FOREIGN KEYS, which is not a major concern.

ERROR 1235 (42000) at line 10: SE currently doesn't support foreign key constraints

I did have some subsequent issues with the current docs version 8.0.35-0.1.0_beta1.37 and I did revert to a prior version from docs earlier this week 8.0.35-0.1.0_beta1.gedaf338.36. Given it’s a very new product I am sure there is a lot of ongoing development.

This is just a quick introduction but it’s a definitely a different architecture in the RDBMS landscape for MySQL compatibility. I hope to run some more tests using the provided sysbench use cases and my own workloads to delve under the covers more.

New Variables

branch_objectstore_id
clone_autotune_concurrency
clone_block_ddl
clone_buffer_size
clone_ddl_timeout
clone_delay_after_data_drop
clone_donor_timeout_after_network_failure
clone_enable_compression
clone_max_concurrency
clone_max_data_bandwidth
clone_max_network_bandwidth
clone_ssl_ca
clone_ssl_cert
clone_ssl_key
clone_valid_donor_list
initialize_branch_objectstore_id
initialize_from_objectstore
initialize_objectstore_bucket
initialize_objectstore_endpoint
initialize_objectstore_provider
initialize_objectstore_region
initialize_objectstore_use_https
initialize_repo_objectstore_id
initialize_smartengine_objectstore_data
objectstore_bucket
objectstore_endpoint
objectstore_mtr_test_bucket_dir
objectstore_provider
objectstore_region
objectstore_use_https
raft_replication_allow_no_valid_entry
raft_replication_appliedindex_force_delay
raft_replication_archive_log_bin_index
raft_replication_archive_recovery
raft_replication_archive_recovery_stop_datetime
raft_replication_auto_leader_transfer
raft_replication_auto_leader_transfer_check_seconds
raft_replication_auto_reset_match_index
raft_replication_check_commit_index_interval
raft_replication_checksum
raft_replication_cluseter_info_on_objectstore
raft_replication_cluster_id
raft_replication_cluster_info
raft_replication_configure_change_timeout
raft_replication_current_term
raft_replication_disable_election
raft_replication_disable_fifo_cache
raft_replication_dynamic_easyindex
raft_replication_election_timeout
raft_replication_flow_control
raft_replication_force_change_meta
raft_replication_force_recover_index
raft_replication_force_reset_meta
raft_replication_force_single_mode
raft_replication_force_sync_epoch_diff
raft_replication_heartbeat_thread_cnt
raft_replication_io_thread_cnt
raft_replication_large_batch_ratio
raft_replication_large_event_split_size
raft_replication_large_trx
raft_replication_learner_heartbeat
raft_replication_learner_node
raft_replication_learner_pipelining
raft_replication_learner_timeout
raft_replication_log_cache_size
raft_replication_log_level
raft_replication_log_type_node
raft_replication_max_delay_index
raft_replication_max_log_size
raft_replication_max_packet_size
raft_replication_min_delay_index
raft_replication_mts_recover_use_index
raft_replication_new_follower_threshold
raft_replication_optimistic_heartbeat
raft_replication_pipelining_timeout
raft_replication_prefetch_cache_size
raft_replication_prefetch_wakeup_ratio
raft_replication_prefetch_window_size
raft_replication_purged_gtid
raft_replication_recover_backup
raft_replication_recover_new_cluster
raft_replication_reset_prefetch_cache
raft_replication_send_timeout
raft_replication_start_index
raft_replication_sync_follower_meta_interva
raft_replication_with_cache_log
raft_replication_worker_thread_cnt
recovery_snapshot_from_objectstore
recovery_snapshot_only
recovery_snapshot_timestamp
recovery_snapshot_tmpdir
repo_objectstore_id
server_id_on_objectstore
serverless
smartengine_auto_shrink_enabled
smartengine_auto_shrink_schedule_interval
smartengine_batch_group_max_group_size
smartengine_batch_group_max_leader_wait_time_us
smartengine_batch_group_slot_array_size
smartengine_block_cache_size
smartengine_block_size
smartengine_bottommost_level
smartengine_bulk_load_size
smartengine_compact
smartengine_compaction_delete_percent
smartengine_compaction_task_extents_limit
smartengine_compaction_threads
smartengine_compression_options
smartengine_compression_per_level
smartengine_concurrent_writable_file_buffer_num
smartengine_concurrent_writable_file_buffer_switch_limit
smartengine_concurrent_writable_file_single_buffer_size
smartengine_data_dir
smartengine_deadlock_detect
smartengine_disable_auto_compactions
smartengine_disable_instant_ddl
smartengine_disable_online_ddl
smartengine_disable_parallel_ddl
smartengine_dump_memtable_limit_size
smartengine_enable_2pc
smartengine_estimate_cost_depth
smartengine_flush_delete_percent
smartengine_flush_delete_percent_trigger
smartengine_flush_delete_record_trigger
smartengine_flush_log_at_trx_commit
smartengine_flush_memtable
smartengine_flush_threads
smartengine_hotbackup
smartengine_idle_tasks_schedule_time
smartengine_level0_file_num_compaction_trigger
smartengine_level0_layer_num_compaction_trigger
smartengine_level1_extents_major_compaction_trigger
smartengine_level2_usage_percent
smartengine_level_compaction_dynamic_level_bytes
smartengine_lock_scanned_rows
smartengine_lock_wait_timeout
smartengine_master_thread_compaction_enabled
smartengine_master_thread_monitor_interval_ms
smartengine_max_background_dumps
smartengine_max_free_extent_percent
smartengine_max_row_locks
smartengine_max_shrink_extent_count
smartengine_max_write_buffer_number_to_maintain
smartengine_memtable_size
smartengine_min_write_buffer_number_to_merge
smartengine_mutex_backtrace_threshold_ns
smartengine_parallel_flush_log
smartengine_parallel_read_threads
smartengine_parallel_recovery_thread_num
smartengine_parallel_wal_recovery
smartengine_pause_background_work
smartengine_persistent_cache_dir
smartengine_persistent_cache_mode
smartengine_persistent_cache_size
smartengine_purge_invalid_subtable_bg
smartengine_query_trace_print_slow
smartengine_query_trace_sum
smartengine_query_trace_threshold_time
smartengine_rate_limiter_bytes_per_sec
smartengine_reset_pending_shrink
smartengine_row_cache_size
smartengine_scan_add_blocks_limit
smartengine_shrink_allocate_interval
smartengine_shrink_table_space
smartengine_sort_buffer_size
smartengine_stats_dump_period_sec
smartengine_strict_collation_check
smartengine_strict_collation_exceptions
smartengine_table_cache_numshardbits
smartengine_table_cache_size
smartengine_total_max_shrink_extent_count
smartengine_total_memtable_size
smartengine_total_wal_size
smartengine_unsafe_for_binlog
smartengine_wal_dir
smartengine_wal_recovery_mode
smartengine_write_disable_wal
snapshot_archive
snapshot_archive_dir
snapshot_archive_expire_auto_purge
snapshot_archive_expire_seconds
snapshot_archive_innodb_tar_mode
snapshot_archive_on_objectstore
snapshot_archive_period
snapshot_archive_smartengine_backup_checkpoint
snapshot_archive_smartengine_tar_mode
table_on_objectstore
wesql_version

New Status

Com_show_consensuslogs
Com_raft_replication_start
Com_raft_replication_stop
Com_native_admin_proc
Com_native_trans_proc
Com_show_consensuslog_events
Smartengine_block_cache_miss
Smartengine_block_cache_hit
Smartengine_block_cache_add
Smartengine_block_cache_index_miss
Smartengine_block_cache_index_hit
Smartengine_block_cache_filter_miss
Smartengine_block_cache_filter_hit
Smartengine_block_cache_data_miss
Smartengine_block_cache_data_hit
Smartengine_row_cache_add
Smartengine_row_cache_hit
Smartengine_row_cache_miss
Smartengine_memtable_hit
Smartengine_memtable_miss
Smartengine_number_keys_written
Smartengine_number_keys_read
Smartengine_number_keys_updated
Smartengine_bytes_written
Smartengine_bytes_read
Smartengine_block_cachecompressed_miss
Smartengine_block_cachecompressed_hit
Smartengine_wal_synced
Smartengine_wal_bytes
Smartengine_write_self
Smartengine_write_other
Smartengine_write_wal
Smartengine_number_superversion_acquires
Smartengine_number_superversion_releases
Smartengine_number_superversion_cleanups
Smartengine_number_block_not_compressed
Smartengine_snapshot_conflict_errors
Smartengine_wal_group_syncs
Smartengine_rows_deleted
Smartengine_rows_inserted
Smartengine_rows_updated
Smartengine_rows_read
Smartengine_system_rows_deleted
Smartengine_system_rows_inserted
Smartengine_system_rows_updated
Smartengine_system_rows_read
Smartengine_max_level0_layers
Smartengine_max_imm_numbers
Smartengine_max_level0_fragmentation_rate
Smartengine_max_level1_fragmentation_rate
Smartengine_max_level2_fragmentation_rate
Smartengine_max_level0_delete_percent
Smartengine_max_level1_delete_percent
Smartengine_max_level2_delete_percent
Smartengine_all_flush_megabytes
Smartengine_all_compaction_megabytes
Smartengine_top1_subtable_size
Smartengine_top2_subtable_size
Smartengine_top3_subtable_size
Smartengine_top1_mod_mem_info
Smartengine_top2_mod_mem_info
Smartengine_top3_mod_mem_info
Smartengine_global_external_fragmentation_rate
Smartengine_write_transaction_count
Smartengine_pipeline_group_count
Smartengine_pipeline_group_wait_timeout_count
Smartengine_pipeline_copy_log_size
Smartengine_pipeline_copy_log_count
Smartengine_pipeline_flush_log_size
Smartengine_pipeline_flush_log_count
Smartengine_pipeline_flush_log_sync_count
Smartengine_pipeline_flush_log_not_sync_count

Planet MySQL

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At the 2024 Federalist Society National Lawyers Convention, Professor Mark W. Smith delivered a compelling speech on the Second Amendment, emphasizing the Supreme Court’s decision in New York State Rifle & Pistol Association v. Bruen and its profound impact on gun rights in America.

Understanding the “Unqualified Command” of the Second Amendment

Professor Smith began by highlighting the Supreme Court’s characterization of the Second Amendment as an “unqualified command.” This designation underscores that the right to keep and bear arms is fundamental and not subject to arbitrary restrictions. He stressed that any ambiguity in historical context should default to the clear text of the Second Amendment, ensuring that the government bears the burden of justifying any limitations on this right.

The Role of Historical Analogues in Gun Control Legislation

A significant portion of the speech focused on how courts should evaluate historical precedents when assessing modern gun control laws. Professor Smith outlined key criteria for determining suitable historical analogues:

• Actual Laws: Only enacted laws, not mere proposals or societal norms, should be considered.
• Relevant Time Period: The primary focus should be on laws from 1791, the year the Second Amendment was ratified, as these reflect the original understanding of the right.
• Constitutional Consistency: Historical laws rooted in unconstitutional practices, such as those based on racial discrimination, should be disregarded.
• Prevalence and Duration: Laws that were widespread and enduring carry more weight than isolated or short-lived statutes.

By adhering to these guidelines, courts can ensure that modern interpretations of the Second Amendment remain faithful to its original intent.

Applying the “Why” & “How” Analysis

Professor Smith introduced the “why” and “how” framework to assess the relevance of historical laws to contemporary issues:

• Why: The purpose behind the historical law.
• How: The method by which the law was implemented.

He illustrated this with the Supreme Court’s decision in District of Columbia v. Heller, where the Court found that historical bans on “dangerous and unusual” weapons did not justify modern handgun bans, as handguns are commonly used for lawful purposes today.

The Impact of Bruen & Rahimi on Second Amendment Jurisprudence

Discussing the Bruen decision, Professor Smith noted that the Court rejected New York’s restrictive “may issue” permitting system, finding no historical precedent for such limitations on public carry. He also addressed the Rahimi case, emphasizing that it represents a routine application of the principles established in Heller and Bruen, reinforcing the necessity for courts to adhere to historical context when evaluating gun control measures.

Guarding Against Overgeneralization

To prevent the erosion of Second Amendment rights through overly broad interpretations, Professor Smith proposed several safeguards:

• Consistency with the Second Amendment’s Text: Derived principles must not contradict the explicit guarantee of the right to keep and bear arms.
• Alignment with Supreme Court Precedent: Any historical principle must be in harmony with established Court rulings.
• Focus on Law-Abiding Citizens: The analysis should center on the rights of lawful individuals, not the actions of criminals.
• Preservation of the Amendment’s Purpose: Any derived principle should uphold the Second Amendment’s role in protecting against tyranny and ensuring self-defense.

By implementing these measures, courts can maintain a faithful interpretation of the Second Amendment, safeguarding it from dilution through generalized reasoning.

Let’s Get it Done!

Professor Smith’s address serves as a vital reminder of the importance of adhering to the original understanding of the Second Amendment. His insights provide a robust framework for evaluating modern gun control laws, ensuring that the fundamental right to keep and bear arms remains protected for future generations.

How Has the Bruen Decision Impacted the 2nd Amendment Litigation Landscape? ~ DEEP DIVE

Unpacking Court Mistakes in 2nd Amendment Cases: How Courts are Twisting Your Gun Rights! ~ DEEP DIVE

 

AmmoLand Shooting Sports News

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The Firearm Blog

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Gizmodo

This was principally written for my SQL students but I thought it might be useful to others. SQL calculation are performed row-by-row in the SELECT-list. In its simplest form without even touching a table, you can add two literal numbers like this:

SELECT 2 + 2 AS result;

It will display the result of the addition to the column alias result as a derived table, or the following result:

+--------+
| result |
+--------+
|      4 |
+--------+
1 row in set (0.00 sec)

Unfortunately, the use of literal values as shown above doesn’t really let you see how the calculation is made row-by-row because it only returns only one row. You can rewrite the two literal values into one variable by using a Common Table Expressions (CTEs). The CTE creates an struct tuple with only one x element. Another way to describe what the CTE does would say, it creates a derived table named struct with a single x column in the SELECT-list.

The CTE runs first, then a subsequent query may use the CTE’s derived table results. Below is a query that uses the value in the struct.x derived table (or references the struct tuple’s x element) twice while assigning the value to a new column alias, labelled result. The FROM clause places the struct tuple in the queries namespace, which lets you reference it in the SELECT-list.

WITH struct AS
 (SELECT 2 AS x)
SELECT struct.x + struct.x AS result
FROM  struct;

Like the literal example, it will display the result of the addition to the column alias result as a derived table of one row:

+--------+
| result |
+--------+
|      4 |
+--------+
1 row in set (0.00 sec)

Having laid a basis for a simple calculation in one row, let’s expand the example and demonstrate how to perform row-by-row calculations. The example requires introducing some new concepts. One uses the UNION ALL set operator to fabricate a CTE derived table with three rows. Another uses a comma within the WITH clause to create two derived tables or CTEs. The last uses the CROSS JOIN to add the single row CTE’s single y column to each of the rows returned by the multiple row CTE.

The CROSS JOIN is a Cartesian product, which multiplies the rows in one table against the rows in another table while adding the columns from each table. That means fabricating a table of one column and one row lets you put a variable into all the rows of another table or set of tables combined through an equijoin or non-equijoin operation.

The query below takes a struct1 derived table of one column and three rows and a struct2 derived table of one column and one row, then uses a CROSS JOIN to create a new derived table, which would be a table of two columns and three rows. The Cartesian product only provides the two columns that we will multiply to create new data.

The SELECT-list lets us fabricate a new column where we multiply the values of column x and column y to create a set of new results in column result.

WITH struct1 AS
 (SELECT 1 AS x UNION ALL
  SELECT 2 AS x UNION ALL
  SELECT 3 AS x)
, struct2 AS
 (SELECT 10 AS y)
SELECT struct1.x AS x
,      struct2.y AS y
,      struct1.x * struct2.y AS result
FROM  struct1 CROSS JOIN struct2;

The query returns the following results, which show the values used to calculate the result and the result:

+---+----+--------+
| x | y  | result |
+---+----+--------+
| 1 | 10 |     10 |
| 2 | 10 |     20 |
| 3 | 10 |     30 |
+---+----+--------+
3 rows in set (0.00 sec)

As a rule, the columns x and y would not be displayed in the final derived table. You would only see the result columns’ values.

Let’s use an example from Alan Bwaulieu’s Learning SQL book with a twist. Rather than manually fabricating the ordinal numbers twice, let’s use the scope reference of a subsequent CTE to reference an earlier CTE. That would create two ten row tables of one column each, or a Cartesian product of a 100 row table with two columns. Then, let’s use the SELECT-list lets us fabricate only a new column, which will add 1 to the numbers 0 to 99 to give us the numbers 1 to a 100.

WITH ones AS
 (SELECT 0 AS x UNION ALL
  SELECT 1 AS x UNION ALL
  SELECT 2 AS x UNION ALL
  SELECT 3 AS x UNION ALL
  SELECT 4 AS x UNION ALL
  SELECT 5 AS x UNION ALL
  SELECT 6 AS x UNION ALL
  SELECT 7 AS x UNION ALL
  SELECT 8 AS x UNION ALL
  SELECT 9 AS x )
, tens AS
 (SELECT x * 10 AS x FROM ones)
SELECT ones.x + tens.x + 1 AS ordinal
FROM   ones CROSS JOIN tens
ORDER BY ordinal;

It returns the following result set:

+---------+
| ordinal |
+---------+
|       1 |
|       2 |
|       3 |
|       4 |
|       5 |
|       6 |
|       7 |
|       8 |
|       9 |
|      10 |
|      11 |
 ...
|      98 |
|      99 |
|     100 |
+---------+
100 rows in set (0.00 sec)

Moving on to more complex math, let’s create a numerals table with the result from our prior query. It will enable calculating the factors of exponents. The easiest way to create the table is shown below (only caveat is that it will build it with a biting rather than an int data type).

CREATE TABLE numerals AS
WITH ones AS
 (SELECT 0 AS x UNION ALL
  SELECT 1 AS x UNION ALL
  SELECT 2 AS x UNION ALL
  SELECT 3 AS x UNION ALL
  SELECT 4 AS x UNION ALL
  SELECT 5 AS x UNION ALL
  SELECT 6 AS x UNION ALL
  SELECT 7 AS x UNION ALL
  SELECT 8 AS x UNION ALL
  SELECT 9 AS x )
, tens AS
 (SELECT x * 10 AS x FROM ones)
SELECT ones.x + tens.x + 1 AS ordinal
FROM   ones CROSS JOIN tens
ORDER BY ordinal;

It can be described after running the foregoing script in MySQL as:

+---------+--------+------+-----+---------+-------+
| Field   | Type   | Null | Key | Default | Extra |
+---------+--------+------+-----+---------+-------+
| ordinal | bigint | NO   |     | 0       |       |
+---------+--------+------+-----+---------+-------+
1 row in set (0.00 sec)

The next query accepts a substitution variable into the WITH clause, which means an external program will call it. (Although, you could use a session level variable, which I would discourage.) This query returns the factors for any given exponent:

WITH magic AS
(SELECT %s AS vkey)
SELECT   CONCAT(magic.vkey,'^',LOG(magic.vkey,n.ordinal)) AS powers
,        n.ordinal AS result
FROM     numerals n CROSS JOIN magic
WHERE    MOD(n.ordinal,magic.vkey) = 0
AND      LOG(magic.vkey,n.ordinal) REGEXP '^[0-9]*$' 
OR       n.ordinal = 1
ORDER BY n.ordinal;

FYI, the regular expression is used to guarantee only integer return values and the 1 returns the identity property of an exponent raised to the zero power.

Assuming you created the numerals table, put the foregoing query in a query.sql file (because I was to lazy to write the full parameter handling), and you run it in the same directory as this Python program, it’ll take any valid integer as a value.

#!/usr/bin/python
# ------------------------------------------------------------
#  Name: power.py
#  Date: 19 Oct 2024
# ------------------------------------------------------------
#  Purpose:
#  -------
#    The program shows you how to provide a single agrument
#    to a query and print the formatted output.
#
#    You can call the program:
#
#    ./power.py 3
#
# ------------------------------------------------------------

# Import libraries.
import sys
import mysql.connector
from mysql.connector import errorcode

# ============================================================
#  Define a local padding function.
# ============================================================
def pad(valueIn):
  # Define local variable.
  padding = ''

  # Convert single digit numbers to strings.
  if isinstance(valueIn,int) and len(str(valueIn)) == 1:
    padding = ' '

  # Return padding space.
  return padding
# ============================================================
#  End local function defintion.
# ============================================================

# Define any local variables.
powerIn = 2
query = ""

# ============================================================
#  Capture argument list minus the program name.
# ============================================================
arguments = sys.argv[1:]

# ============================================================
#  If one or more arguments exists and the first one is an
#  a string that can cast to an int, convert it to an int,
#  assign it to a variable, and ignore any other arguments
#  in the list.
# ============================================================
if len(arguments) >= 1 and arguments[0].isdigit():
  powerIn = int(arguments[0])

# ============================================================
#  Use a try-catch block to read and parse a query from a
#  a file found in the same local directory as the Python
#  program.
# ============================================================
try:
  file = open('query.sql','r')
  query = file.read().replace('\n',' ').replace(';','')
  file.close()

except IOError:
  print("Could not read file:", fileName)
 
# ============================================================
#  Attempt connection in a try-catch block.
# ============================================================
# --------------------------------------------------------
#  Open connection, bind variable in query and format
#  query output before closing the cursor.
# --------------------------------------------------------
try:
  # Open connection.
  cnx = mysql.connector.connect(user='student', password='student',
                                host='127.0.0.1',
                                database='studentdb')

  # Create cursor.
  cursor = cnx.cursor()

  # Execute cursor, and coerce string to tuple.
  cursor.execute(query, (powerIn,))

  # Display the rows returned by the query.
  for (powers, result) in cursor:
    print((" {} is: {}").format(powers, pad(result) + str(result)))

  # Close cursor.
  cursor.close()

# --------------------------------------------------------
#  Handle MySQL exception 
# --------------------------------------------------------
except mysql.connector.Error as e:
  if e.errno == errorcode.ER_ACCESS_DENIED_ERROR:
    print("Something is wrong with your user name or password")
  elif e.errno == errorcode.ER_BAD_DB_ERROR:
    print("Database does not exist")
  else:
    print("Error code:", e.errno)        # error number
    print("SQLSTATE value:", e.sqlstate) # SQLSTATE value
    print("Error message:", e.msg)       # error message

# --------------------------------------------------------
#  Close connection after try-catch completes.
# --------------------------------------------------------
# Close the connection when the try block completes.
else:
  cnx.close()

If you forget to call it with a numeric parameter, it uses 2 as the default. You would call it as follows from a Linux prompt from the local directory:

./power.py

It returns:

 2^0 is:  1
 2^1 is:  2
 2^2 is:  4
 2^3 is:  8
 2^4 is: 16
 2^5 is: 32
 2^6 is: 64

If you call it with a numeric parameter, it uses the numeric value. You would call it as follows from a Linux prompt from the local directory:

./power.py 3

It returns:

 3^0 is:  1
 3^1 is:  3
 3^2 is:  9
 3^3 is: 27
 3^4 is: 81

As always, I hope the post helps folks sort out how and why things work.

Planet MySQL

MySQL system variables configure the server’s operation, and the SET statement is used to change system variable. The MySQL SET statements have various options for specifying changes to SYSTEM VARIABLE. It’s important to understand how these changes are reflected in current sessions (connections), later sessions, and after database server restarts.Planet MySQL

Classic first-person shooters Unreal (1998) and Unreal Tournament are now available for free on the Internet Archive, with official OK from publisher Epic Games. An Epic spokesperson confirmed to PC Gamer that users are permitted to "independently link to and play these versions." Players can download the games directly from the Internet Archive and apply patches from Github for modern Windows compatibility, or use simplified installers through oldunreal.com. Both titles run on current hardware despite their age, though users may need to adjust dated default settings like 640×480 resolution and inverted mouse controls.

Read more of this story at Slashdot.

Slashdot

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The Firearm Blog