Frickley Athletic: An In-Depth Analysis for Sports Betting

Overview of Frickley Athletic

Frickley Athletic is a football club based in Frickley, South Yorkshire, England. Competing in the Northern Premier League Division One West, the team was founded in 1896. Under the management of Mark Patterson, Frickley Athletic plays its home matches at Thrum Hall.

Team History and Achievements

Frickley Athletic has a rich history with several notable achievements. The club has won multiple league titles and cup competitions over the years. Notable seasons include their promotions to higher divisions and memorable cup runs. Their resilience and ability to bounce back from setbacks make them a team worth watching.

Current Squad and Key Players

The current squad boasts talented players such as James McAlister (goalkeeper), Tom Henshall (defender), and Michael Smith (forward). These players have been instrumental in recent performances, with McAlister known for his shot-stopping abilities and Smith for his goal-scoring prowess.

Team Playing Style and Tactics

Frickley Athletic typically employs a 4-4-2 formation, focusing on solid defense and quick counter-attacks. Their strengths lie in their disciplined backline and dynamic midfield play. However, they can be vulnerable to high pressing teams that exploit their slower transition from defense to attack.

Interesting Facts and Unique Traits

Frickley Athletic is affectionately known as “The Ironmen,” reflecting their hardworking ethos on the pitch. The club has a passionate fanbase that supports them through thick and thin. Rivalries with nearby clubs add an extra layer of excitement to their matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Michael Smith ✅
• Best Defender: Tom Henshall 🎰
• Average Goals per Game: 1.5 💡
• Promotion Chances: Moderate ❌

Comparisons with Other Teams in the League or Division

Frickley Athletic often finds themselves competing closely with teams like Farsley Celtic and AFC Emley. While they may not have the same financial backing as some rivals, their tactical discipline often gives them an edge in crucial matches.

Case Studies or Notable Matches

A standout match was their FA Trophy victory against higher-tier opponents, showcasing their ability to perform under pressure. Such games highlight their potential to surprise even well-established teams.

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks:

• Analyze head-to-head records against upcoming opponents to gauge potential outcomes.
• Maintain awareness of player injuries that could impact team performance.
• Closely monitor form trends over recent matches for betting insights.

Betting Analysis Quotes or Expert Opinions about the Team (Quote Block):

“Frickley’s resilience is impressive; they consistently punch above their weight,” says football analyst John Doe.

The Pros & Cons of Frickley Athletic’s Current Form or Performance (✅❌ Lists):

• Pros:
• Solid defensive record ✅
  

 



</u#include “config.h” #include “plugin.h” #include “logging.h” #include “utils.h” namespace config { namespace { const char * const config_path = “/etc/pacproxy.conf”; const char * const default_config_path = “/usr/share/pacproxy/default.conf”; void load_from_file(const char * path) { FILE * file = fopen(path, “r”); if (!file) return; char line[1024]; while(fgets(line, sizeof(line), file)) { char * comment = strchr(line,’n’); if(comment) *comment = ”; comment = strchr(line,’#’); if(comment) *comment = ”; char key[256], value[256]; sscanf(line,”%255[^=]=%255s”,key,value); set(key,value); } fclose(file); } } void load() { load_from_file(config_path); bool exists; #ifdef WIN32 #define _access _access_s #endif #ifdef WIN32 #define PATH_SEPARATOR “\” #else #define PATH_SEPARATOR “/” #endif #ifndef WIN32 struct stat s; exists = !stat(config_path,&s) && S_ISREG(s.st_mode); #else exists = access(config_path,F_OK) == 0; #endif if(!exists) load_from_file(default_config_path); #ifndef WIN32 #undef PATH_SEPARATOR #undef _access #endif for(auto it : get_all()) LOG(DEBUG) << it.first << "=" << it.second; LOG(INFO) <std::unique_ptr{ return std::unique_ptr(new plugin::ConfigPlugin()); }); } namespace plugin { ConfigPlugin::ConfigPlugin() : PluginBase(“config”) {} ConfigPlugin::~ConfigPlugin() {} std::string ConfigPlugin::name() const { return name_; } int ConfigPlugin::init(plugin::ModuleManager & mm) { if(!mm.module_exists(“config”)) mm.load_module(“config”); LOG(INFO) << "Loaded configuration module"; return SUCCESS; } int ConfigPlugin::deinit() { return SUCCESS; } } xfsnake/pacproxy<|file_sep#ifdef HAVE_CONFIG_H # include "config.h" #endif #include "logging.h" #include "module_manager.h" namespace plugin { static ModuleManager g_mm; void init() { #ifdef ENABLE_LOGGING logging_init(); #endif g_mm.init(); LOG(INFO) << __FILE__ << ": Loaded all plugins"; } void deinit() { g_mm.deinit(); #ifdef ENABLE_LOGGING logging_deinit(); #endif } ModuleManager & module_manager() { return g_mm; } } xfsnake/pacproxy= v3. * Run `cmake .` * Run `make` Configuration: * Edit `/etc/pacproxy.conf` file. * Restart service using `service pacproxy restart` Available options: # This option specifies where should logs be stored. # If set to empty string then logs are disabled. log_dir=/var/log/pacproxy # This option specifies whether should proxy log requests/responses. # If set to true then logs are enabled. log_requests=true # This option specifies how many requests/responses should be logged before being deleted. # Default value is 10000. log_limit=10000 # This option specifies what type of filter should be used when filtering requests/responses. filter_type=whitelist # This option specifies what action should be taken when request/response does not pass through filter. action_block=drop # This option specifies what list will be used when filtering requests/responses. list_type=internal_list # This option specifies where internal list will be stored if list_type=internal_list is specified. internal_list=/etc/whitelist.txt # This option specifies where external list will be stored if list_type=external_list is specified. external_list=http://example.com/list.txt Available lists: /etc/whitelist.txt – whitelist by default. xfsnake/pacproxy<|file_sep ClickHouse — A distributed column-oriented database management system for online analytical processing How To Install: Download source code: git clone https://github.com/yandex/clickhouse.git clickhouse –recursive Build ClickHouse: cd clickhouse/ mkdir build/ cd build/ cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local/clickhouse-server -DENABLE_DISTRIB=false -DENABLE_AWS=false -DENABLE_MYSQL=false -DENABLE_ODBC=false -DENABLE_TESTS=false -DENABLE_EXAMPLES=false -DENABLE_LOCALTIME_ZONE=true -DBUILD_STATIC_LIBS=true make Install ClickHouse: sudo make install Create user clickhouse: sudo adduser –system –group –disabled-login clickhouse Create directories: sudo mkdir /var/lib/clickhouse/ sudo chown clickhouse:clickhouse /var/lib/clickhouse/ Create symbolic link: sudo ln -s /usr/local/clickhouse-server/bin/clickhouse-server /usr/sbin/clickhouse-server Start ClickHouse server: sudo service clickhouse-server start ClickHouse client: You can connect via telnet: telnet localhost 9000 Or use native client: /usr/local/clickhouse-client/bin/clickclient localhost How To Configure: Edit file /etc/default/clickhouse-server: ClickHouse runs as user clickouse. ClickHouse stores data files into directory /var/lib/clickouse/data. Logs are located into directory /var/log/clickouse. Server listens on port 9000 by default. Server can use memory limit equal to half of available RAM. Server can use disk space limit equal to half of available RAM. For example: CLICKHOUSE_USER=clickouse CLICKHOUSE_GROUP=clickouse CLICKHOUSE_DATA_PATH=/var/lib/clicshouse/data/ CLICKHOUSE_LOGS_PATH=/var/log/clicshouse/ CLICKHOUSE_PORT=9000 # you can change this port here if needed but don't forget update firewall settings too! CLICKHOUSE_RAM_LIMIT=$(awk '/MemTotal/ {printf("%.fn", $2/1024/1024/4)}' /proc/meminfo) ClickHouse web interface runs on port 8123 by default. For example: CLICKHOUSE_HTTP_PORT=8123 # you can change this port here if needed but don't forget update firewall settings too! For more information see README.md file. How To Test: Run tests: cd build/ make test Run examples: cd build/examples/ ./example_client How To Upgrade: To upgrade ClickHouse you need only recompile binary files using cmake command described above. Tips And Tricks: You can run ClickHouse server without any configuration files at all! If you don't specify pathes for configuration files then they will be automatically created with reasonable defaults. Configuration files are located into directory $CLICKHOUSE_BASE_DIR/etc/. $CLICKHOUSE_BASE_DIR is defined by environment variable CLICKHOUSE_BASE_DIR. If CLICKHOUSE_BASE_DIR environment variable isn't defined then it's assumed that $CLICKHOUSE_BASE_DIR equals '/etc/clickhoue'. $CLICKHOUSE_BASE_DIR must point into existing directory! Default values for configuration files are defined inside sources. If you want custom configuration just create your own configuration files inside $CLICKHOUSE_BASE_DIR/etc/ and specify names there according documentation below. User Interface Configuration: You can configure user interface using file $CLICKHOUSE_CONF_DIR/user_interface.xml $CLICKHOUSE_CONF_DIR must point into existing directory! By default it equals '/etc/clicshouse'. All relative paths inside user_interface.xml are considered relative to $CLICKHUSE_CONF_DIR. See documentation below how exactly user interface works. Server Configuration: You can configure server using file $CONFIG_FILE_NAME. By default CONFIG_FILE_NAME equals 'server.xml'. $CONFIG_FILE_NAME must point into existing file! All relative paths inside CONFIG_FILE_NAME are considered relative to current working directory. See documentation below how exactly server works. Database Engine Configuration: You can configure database engine using file $ENGINE_CONFIG_FILE_NAME. By default ENGINE_CONFIG_FILE_NAME equals 'default/config.xml'. $ENGINE_CONFIG_FILE_NAME must point into existing file! All relative paths inside ENGINE_CONFIG_FILE_NAME are considered relative to current working directory. See documentation below how exactly database engine works. Documentation: http://www.yandex.com/company/research-and-development/open-source-programs.html?from-menu&ysrc=clicshouse-readme&text=%E8%AF%B7%E7%9C%8B%E7%9C%8B%E6%88%91%E7%9A%84%E6%A8%A1%E5%BC%8F%E5%A4%B4%E9%A1%B5&clid=20034780&tact=__all__all__all_na_na_na_na_na_na_na_10yandex_text_link_ya_market_base_text_shown_topic_readme_title&nojs=1&target=_blank&pos=-1&rurl=http://yastatic.net/static/market/base/img/logo-yandex-white.svg This document contains general information about ClickHouse architecture, database engine features, server features, user interface features, examples, tips, and so on. What Is ClickHouse? ClickHouse is a column-oriented DBMS designed for OLAP workloads. It allows interactive analysis of large volumes of data without prior aggregation. What Can You Do With It? Using ClickHouse you can answer questions like these ones: * How many users visited our site today? * What were most popular items yesterday? * How many users visited our site from each country last week? * What were most popular items last month? * What was total revenue yesterday? * What were total revenues last year? And so on… Of course you can ask much more complex questions too! But most importantly ClickHouse allows answering these questions really fast! Why Use Columnar Storage? Let's consider example problem: calculate total revenue yesterday. Assume we store data in following format: id | date | item_id | price | quantity | revenue=item_price*quantity | … —+——+———+——-+———-+—————————–+—– 123 |2014-01-01 |4567 |12 |23 |276 | 124 |2014-01-01 |4568 |34 |12 |408 | 125 |2014-01-02 |4569 |56 |34 |||||||||||||||||||||||||| 126 |||||||||||||||||||||||||| 127 |||||||||||||||||||||||||| 128 |||||||||||||||||||||||||| 129 ||| In this case we have only two rows which belong yesterday so we need only sum revenues from these two rows. Now let's consider second example problem: calculate total revenue last year. In this case we need sum revenues from all rows which belong last year. In both cases we need access only revenues column but we also need access date column because we want filter out rows which do not belong requested time period. Thus amount of data we actually need read from storage is much less than total amount of data stored in table. So why use columnar storage? Because reading columns separately allow us read only required columns from storage! And because columns are stored contiguously in memory we gain additional benefits like cache locality! So reading required columns takes much less time than reading whole rows! Also since columns contain values of single type we gain additional benefits like compression! What Is OLAP Workload? OLAP stands for Online Analytical Processing. It means interactive analysis over large volumes of data without prior aggregation. What Is OLTP Workload? OLTP stands for Online Transactional Processing. It means interactive processing over small amounts of data with support transactions. Why Use Columnar Storage For OLAP Workloads Only? Columnar storage doesn't allow fast random writes because writing single row requires writing all columns separately! Also since each column contains values of single type then each column must have fixed size! So updating single value requires rewriting whole column! Thus columnar storage doesn't fit well requirements imposed by OLTP workload! But it fits well requirements imposed by OLAP workload! Why Use Columnar Storage For OLAP Workloads? Because accessing single row takes longer time than accessing single column! And because accessing several rows takes much longer time than accessing several columns! So when processing large amounts of data it's better process them column-wise instead row-wise! How Does It Work Internally? ClickHouse stores tables as sets consisting out tables parts named parts_000001.db , parts_000002.db , etc… Each table part consists out blocks named block_000001 , block_000002 , etc… Each block consists out stripes named stripe_000001 , stripe_000002 , etc… Each stripe consists out compressed chunks named chunk_000001 , chunk_000002 , etc… Each chunk consists out compressed buffers named buffer_000001 , buffer_000002 , etc… Blocks consist out stripes which differ by compression algorithm used! Stripe size depends on compression algorithm used! Block size depends on number stripes contained within block! Block size ranges between MIN_BLOCK_SIZE bytes (=16M bytes by default) and MAX_BLOCK_SIZE bytes (=512M bytes by default)! Stripes consist out chunks which differ by compression level used! Chunk size depends on compression level used! Chunk size ranges between MIN_CHUNK_SIZE bytes (=64K bytes by default) and MAX_CHUNK_SIZE bytes (=16M bytes by default)! Chunks consist out buffers which differ by number values contained within buffer! Buffer size depends on number values contained within buffer! Buffer size ranges between MIN_BUFFER_SIZE values (=65536 values by default) and MAX_BUFFER_SIZE values (=1048576 values by default)! Why Use Blocks? Blocks help reduce number I/O operations during reads/writes! We try keep blocks cached while processing queries so we reduce number I/O operations during query execution! Also blocks allow us optimize I/O operations during writes because writing whole block takes less time than writing separate stripes/chunks/buffers! Why Use Stripes? Stripes help reduce memory consumption during decompression! We try keep stripes cached while processing queries so we reduce memory consumption during query execution! Why Use Chunks? Chunks help reduce memory consumption during decompression! We try keep chunks cached while processing queries so we reduce memory consumption during query execution! Why Use Buffers? Buffers help reduce CPU usage during decompression/decompression! Performance Benchmarks: Here are some results obtained using MySQL benchmark tool called sysbench . MySQL was installed using apt-get install mysql-server command . Sysbench was installed manually according instructions given at http://www.mysqlperformanceblog.com/2009/06/18/sysbench-a-benchmarking-tool-for-mysql/ Firstly MySQL table was filled using following command : sysbench –db-driver=mysql –mysql-user=root –mysql-password=password –mysql-db=test –table-size=10000000 –db-driver-opt="connect_timeout=120" –test=fileio –oltp-table-size-name='users' –oltp-table-size-init='100' –oltp-tables-count='10' –rand-type='uniform' prepare Secondly sysbench benchmark was executed against MySQL table using following command : sysbench –db-driver=mysql –mysql-user=root –mysql-password=password –mysql-db=test –table-size=10000000 –db-driver-opt="connect_timeout=120" –test=fileio –oltp-table-size-name='users' –oltp-table-size-init='100' –oltp-tables-count='10' –rand-type='uniform' run Thirdly MySQL table was converted into ClickHose table using following command : clickhousetool convert mysql root password test users users.users Fourthly sysbench benchmark was executed against ClickHose table using following command : sysbench –db-driver=mysql-clickhousetool –mysql-clickhousetool-user=root –mysql-clickhousetool-password=password –mysql-clickhousetool-db=test –table-file='/home/dmitry/test/users.users/table.dat' –test=fileio –oltp-table-size-name='users' –oltp-table-size-init='100' –oltp-tables-count='10' –rand-type='uniform' run Here are results obtained while running benchmarks : MySQL Benchmark Results : Running sysbench… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Creating new transaction.. New transaction.. Committing.. Committed.. Creating new transaction.. New transaction.. Committing.. Committed.. Creating new transaction.. New transaction.. Committing.. Committed.. Creating new transaction.. New transaction.. Committing.. Committed.. Creating new transaction.. New transaction.. Committing.. Committed.. Transactions stats: transactions performed: read_only: 50 (avg: 14 us/stddev 24 us) update_only: 50 (avg: 28 us/stddev 47 us) read_update: 50 (avg: 39 us/stddev 66 us) transactions per second: read_only: 21457 (95%% lat below 27 us; min 11 us; max 249 us; curvesum ) update_only: 8619 (95%% lat below 63 us; min 21 us; max 418 us; curvesum ) read_update: 6596 (95%% lat below 86 us; min 25 us; max 541 us; curvesum ) General statistics: total time: [03]03 s total number of events: [50]50 Latency average/stddev:[us]: min [11]11 max [249]249 avg [14]14 stddev [24]24 percentile [75]75 percentile [95]95 percentile [99]99 [us] [22]22 [us] [29]29 [us] [33]33 Latency average/stddev:[ms]: min [.011][ms] max [.249][ms] avg [.014][ms] stddev [.024][ms] Operations rate average/stddev:[ops/s]: min [-inf][ops/s] max [+inf][ops/s] avg [+inf][ops/s] stddev [-inf][ops/s] Operations distribution histogram:[ops]: histogram interval count ops/s [0].010s +——————–+ +——————–+ +——————–+ + + [0].020s + + + + + + [0].030s + + + + + + [0].040s + + + + + + [0].050s + + +—-***————+ [0].060s ++******++++++++++++++ +++******************* [0].070s +++***************+++ +++**************+++ [0].080s +++*************+++ +++*************+++ [0].090s ++*************++ ++**************++ [0].100s ++**************++ ++***************++ [0].110s ++***************++ ++****************+ [0].120s ++****************+ ++****************** [0].130s +++****************** +++******************* [0].140s +++******************* +++******************* [0].150s +++******************** ***++++++++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ ********+++++++++++++++ *****++++++++++++++++++ ********************** ************************ ************************ ************************ ************************ ************************ ************************ Operations distribution histogram:[latency]: histogram interval count ops/s [-inf]+-.010 [-inf]+-.010 [-inf]+-.020 [-inf]+-.020 [-inf]+-.030 [-inf]+-.030 [-inf]+-.040 [-inf]+-.040 [-inf]+-.050 [-inf]+-.050 [-inf]+-.060 [+—–][-+.060] [-inf]+-.070 [+——][-+.070] [-inf]+-.080 [+——][-+.080] [-inf]+-.090 [+—–][-+.090] [-inf]+-.100 [+—-][-+.100] [-inf]+-.110 [+—][-+.110] [-inf]+-.120 [+—][-+.120] [-inf]+-.130 [+—][-+.130] [-inf]+-.140 [+]-[+++.140] [-inf]-++.141 [–+-][-++.141] [–+-][-++.142]-[–+-][-++.143]-[–+-][-++.144]-[–+-][-++.145]-[–+-][-++.146]-[–+-][-++.147]-[–+-)[-++.148]-[–+-)[-++.149]-[–+-)[-++.150]-[–+-)[-++.151]-[–+-)[-++.152]-[–+-)[-++.153]-[–+-)[-++.154]-[–+]- [–+]- [–+]- [–+]- [–+]- [–+]- [–+]- ClickHose Benchmark Results : Running sysbench… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Threads started… Transactions stats: transactions performed: read_only: [50][50] (avg:[13 ]us/[13 ]us/stddev:[19 ]us/[19 ]us) update_only: [50][50] (avg:[27 ]us/[27 ]us/stddev:[44 ]us/[44 ]us) read_update: [50][50] (avg:[42 ]us/[42 ]us/stddev:[69 ]us/[69 ]us) transactions per second: read_only: [+∞][21457 ][ops/s](95%% lat below :[[13 ][27 ][29 ][33 ][ms]/[/ms]; min :[[11 ][11 ][11 ][11 ][ms]/[/ms]; max :[[249 ][249 ][249 ][249 ][ms]/[/ms]; curvesum :[]) update_only: [+∞][(8619)][ops/s](95%% lat below :[[63 ][63 ][65 ][67 ][ms]/[/ms]; min :[[21 ][21 ][21 ][21 ][ms]/[/ms]; max :[[418 ][418 ][418 ][418 ][ms]/[/ms]; curvesum :[]) read_update: [+∞][(6596)][ops/s](95%% lat below :[[86 ][86 ][88 ][90 ][ms]/[/ms]; min :[[25 )[25 )[25 )[25 )[25 /[25 ms;/][/25 ms]; max :[[541 [[541 [[541 [[541 [[541 ms;/][/541 ms;/][/541 ms;/][/541 ms; General statistics: total time:[[03]][03 s]/[/03 s]) total number of events:[[50]][50 ] Latency average/stddev:[us]: min:[[11]][11 [/11 [/11 [/11 [/11 [/11 [/11 [/11 [/11 [/11 [/10 ms;/][/10 ms;/][/10 ms;/][/10 ms;/][/10 ms;/][/10 ms;/][/10 ms;/][/10 ms; max:[[249]][249 [[249 [[249 [[249 [[249 [[250 [[250 [[250 [[250 [[[250 [[[251 [[[251 [[[252 [[[253 [[[254 [[[255 [[[255 [[[256 [[[257 [[[258 [[[259 ]]259 ]]259 ]]259 ]]259 ]]259 ]]259 ]]260 ]]260 ]]260 ]]260 ]]260 ] avg:[[13]][13 [/13 [/13 [/13 [/13 [/13 [/13 [/13 [/13 [/13 /[12 um;s;/[/12 um;s;/[/12 um;s;/[/12 um;s;/[/12 um;s; stddev:[[19]][19 [/19 [/19 [/19 [/19 ['/19 ['/19 ['/20 ['/20 ['/20 ['/20 ['/20 ['/20 [['20 [['21 [['21 [['21 [['22 [ Latency average/stddev:[ms]: min:[[ .011]][ .011 []/.011 []/.011 []/.011 []/.011 []/.011 []/.011 []/.011 []/.011 []/.010 ] max:[[ .249]][ .250 [][] .250 [][] .250 [][] .251 [][] .251 [][] .252 [][] .253 [][] .254 [][] .255 [][] .256 [][] .257 [] avg:[[ .013]][ .013 []/.013 []/.013 []/.013 []/.013 []/.013 []/.013 []/.013 []/.013 []/.012 ] stddev:[[ .019]][ .019 [[] ./019 [[] ./019 [[] ./019 [[] ./020 [[] ./020 [[] ./020 [[] ./020 [[] ./020 [[] ./021 [] Operations rate average/stddev:[ops/s]: min:(-[∞])[(-∞)] max:(+[∞])[(+∞)] avg:(+[∞])[(+∞)] stddev:(-[∞])[(-∞)] Operations distribution histogram:[ops]: histogram interval count ops/s [.010].[020][]–[….–[….–[….–[….–[….–[….–[….– [.030].[040][]–[….–[….–[….–[….– [.050].[060][]–[……….————– [.070].[080][]–[……….————– [.090].[100][]–[………… [.110].[120][]–[………… [.130].[140][]– [.150].[160][]– —-***———— ——*****——— ——–*******—— ————*******— —————******* —————-****—– ———————–* Operations distribution histogram:[latency]: histogram interval count ops/s (-∞).[+.-.[.+.[.+.[.+.[.+.[.+.[.+.[.+.[.+.[.+.[.+.-[++.-[++.-[++.-[++.-[++.-[++.-[++.-[++.-[++.-(++.-(++.)- (-++)- (-++)- (-++)- (-++)- (-++)- As you see latency increased ~30 times but throughput remained almost unchanged ! Note that sysbench measures latency as follows : latency := timestamp_at_end_of_operation-timestamp_at_start_of_operation ; So there might exist additional overhead introduced due differences between way MySQL implements transactions versus way ClickHose implements transactions ! But still increase in latency looks pretty significant ! What Can Be Done Better? Idea #1: Currently every query causes creation temporary view which represents result set returned after executing query . This temporary view consists out temporary table parts containing intermediate results produced after executing query . Temporary views created after executing different queries share common pool containing temporary table parts . Every temporary view maintains pointer towards pool containing its temporary table parts . Temporary view destructor releases pointers towards its temporary table parts causing corresponding temporary table parts destroyed ! Thus every query causes creation lots small-sized temporary tables resulting creation lots small-sized blocks/strips/chunks/buffers ! Moreover every query causes creation lots different temporary tables resulting creation lots different blocks/strips/chunks/buffers ! This leads poor cache locality thus increasing number I/O operations during reads/writes thus decreasing performance ! Solution #1: One solution would be introduce another level between views containing temporary tables representing result sets returned after executing queries . Let's call this level views containing intermediate results produced after executing queries . Views containing intermediate results would maintain pointers towards pool containing intermediate result sets represented as temporary tables . Views containing intermediate results would also maintain pointer towards view representing final result set returned after executing query . View destructor would release pointers towards its intermediate result sets represented as temporary tables causing corresponding intermediate result sets destroyed ! Temporary view destructor would release pointer towards itself causing itself destroyed ! Idea #2: Currently every request causes creation new connection object representing connection established between client/server . Every connection object maintains reference towards thread object representing thread handling connection established between client/server . Every request causes creation new session object associated with connection object representing session established between client/server . Session objects contain references towards thread object associated with connection object associated with session object ! Thus every request causes creation lots different threads resulting creating lots different connections resulting creating lots different sessions ! Solution #2: One solution would be introduce another level between sessions representing sessions established between client/server . Let's call this level connections representing connections established between client/server . Idea #3: Currently every request causes creation new thread responsible handling request received from client . Solution #3: One solution would be introduce another level between threads responsible handling requests received from clients . Idea #4: Currently every request causes creation new thread responsible handling request received from client . Solution #4: One solution would be introduce another level between threads responsible handling requests received from clients . xfsnake/pacproxy<|file_sep#include "logging.h" namespace logging { #if defined(ENABLE_LOGGING) static FILE * log_stream; void logging_init() { log_stream = stderr; } void logging_deinit() { fclose(log_stream); log_stream = nullptr; } #else // defined(ENABLE_LOGGING) void logging_init() {} void logging_deinit() {} #endif // defined(ENABLE_LOGGING) } // namespace logging #define LOG(level) ::logging::_##level(stream_, __FILE__, __LINE__) #define LOG_STREAM(stream_) ::logging::_##stream_ #define LOG_DEBUG(…) ::logging::_debug(LOG_STREAM(__VA_ARGS__), __VA_ARGS__) #define LOG_INFO(…) ::logging::_info(LOG_STREAM(__VA_ARGS__), __VA_ARGS__) #define LOG_WARN(…) ::logging::_warn(LOG_STREAM(__VA_ARGS__), __VA_ARGS__) #define LOG_ERROR(…) ::logging::_error(LOG_STREAM(__VA_ARGS__), __VA_ARGS__) namespace logging { template void _debug(TStream_ stream_, const char *, int line_number_, Args_ …args_) #ifdef ENABLE_LOGGING { fprintf(stream_, “%d:%d DEBUG “, __FILE__, line_number_); ((fprintf(stream_, “%c %”, ‘ ‘)), …); fprintf(stream_, “n”); ((fprintf(stream_, “%c %”, ‘ ‘), print_arg{}(stream_, args_)), …); } #else // ENABLE_LOGGING { /* nothing */ } #endif // ENABLE_LOGGING template void _info(TStream_ stream_, const char *, int line_number_, Args_ …args_) #ifdef ENABLE_LOGGING { fprintf(stream_, “%d:%d INFO “, __FILE__, line_number_); ((fprintf(stream_, “%c %”, ‘ ‘)), …); fprintf(stream_, “n”); ((fprintf(stream_, “%c %”, ‘ ‘), print_arg{}(stream_, args_)), …); } #else // ENABLE_LOGGING { /* nothing */ } #endif // ENABLE_LOGGING template void _warn(TStream_) #ifdef ENABLE_LOGGING { fprintf(log_stream, “[WARN]: “); } #else // ENABLE_LOGGING { /* nothing */ } #endif // ENABLE_LOGGING template void _error(TStream_) #ifdef ENABLE_LOGGING { fprintf(log_stream, “[ERROR]: “); } #else // ENABLE_LOGGING { /* nothing */ } #endif // ENABLE_LOGGING namespace detail { template struct print_arg_impl{template void operator()(TStream_* stream_, const std::string_view& arg_)const{fprintf(stream_, “”%.*” “, static_cast(arg_.length()), arg_.data());}}; template struct print_arg_impl{template void operator()(TStream_* stream_, const std::string_view& arg_)const{fprintf(stream_, “”%.*” “, static_cast(arg_.length()), arg_.data());}}; template struct print_arg_impl{template void operator()(TStream_* stream_, const std::string& arg_)const{fprintf(stream_, “”%.*” “, static_cast(arg_.length()), arg_.data().data());}}; template struct print_arg_impl{template void operator()(TStream_* stream_, const std::string& arg_)const{fprintf(stream_, “”%.*” “, static_cast(arg_.length()), arg_.data().data());}}; template struct print_arg_impl{template void operator()(TStream_*