• 淘宝分布式 key/value 存储引擎Tair安装部署过程及Javaclient測试一例


    文件夹


    1. 简单介绍

    2. 安装步骤及问题小记

    3. 部署配置

    4. Javaclient測试

    5. 參考资料


    声明


    1. 以下的安装部署基于Linux系统环境:centos 6(64位),其他Linux版本号可能有所差异。

    2. 网上有人说tair安装失败可能是由于gcc版本号问题,高版本号的gcc可能不支持某些特性导致安装失败。经过实验证明。该说法是错误的,tair安装失败有各种可能的原因但绝对与gcc版本号无关,比方我的gcc開始版本号为4.4.7,后来tair安装失败,我又一次编译低版本号的gcc(gcc4.1.2)。可是问题相同出现。

    后来发现是其他原因。修正后又一次用高版本号gcc4.4.7成功安装。

    3. 以下的内容部分參考tair官方介绍文档,转载请注明原文地址。


    正文


    1. 简单介绍


    tair 是淘宝自己开发的一个分布式 key/value 存储引擎. tair 分为持久化和非持久化两种使用方式. 非持久化的 tair 能够看成是一个分布式缓存. 持久化的 tair 将数据存放于磁盘中. 为了解决磁盘损坏导致数据丢失, tair 能够配置数据的备份数目, tair 自己主动将一份数据的不同备份放到不同的主机上, 当有主机发生异常, 无法正常提供服务的时候, 其余的备份会继续提供服务.


    2. 安装步骤及问题小记


    2.1 安装步骤

    由于tair的实现用到了底层库 tbsys 和 tbnet,因此在安装tair之前须要先安装依赖库 tbsys 和 tbnet。



    2.1.1 获取源代码

    首先须要通过svn下载源代码,能够通过sudo yum install subversion安装svn服务。

    1. svn checkout http://code.taobao.org/svn/tb-common-utils/trunk/ tb-common-utils # 获取tbsys 和 tbnet的源代码
    2. svn checkout http://code.taobao.org/svn/tair/trunk/ tair # 获取tair源代码

    2.1.2 安装依赖库或软件

    编译tair或tbnet/tbsys之前须要预先安装一些编译所需的依赖库或软件。
    在安装这些依赖之前最好首先检查系统是否已经安装,在用rpm管理软件包的os上能够使用rpm -q 软件包名查看是否已安装该软件或库。

    a. 安装libtool
    sudo yum install libtool # 同一时候会安装libtool所依赖的automake和autoconfig
    b. 安装boost-devel库
    sudo yum install boost-devel
    c. 安装zlib库
    sudo yum install zlib-devel

    2.1.3 编译安装tbsys和tbnet

    1. tair 的底层依赖于tbsys库和tbnet库, 所以要先编译安装这两个库.

    2. a. 环境变量设置 TBLIB_ROOT 
    取得源代码后, 先指定环境变量 TBLIB_ROOT 为须要安装的文件夹. 这个环境变量在兴许 tair 的编译安装中仍旧会被使用到. 
    比方要安装到当前用户的lib文件夹下, 则指定export TBLIB_ROOT="~/lib"。

    b. 安装
    进入源代码文件夹, 执行build.sh进行安装. 

    1. 2.1.4 编译安装tair

    进入 tair 源代码文件夹,依次按以下顺序编译安装
    ./bootstrap.sh
    ./configure  # 注意, 在执行configue的时候, 能够使用 --with-boost=xxxx 来指定boost的文件夹. 使用--with-release=yes 来编译release版本号.
    make
    make install
    成功安装后会在当前用户home文件夹下生成文件夹tair_bin,即tair的成功安装后的文件夹。


    2.2 问题小记

    安装过程并非一帆风顺的,期间出现了非常多问题,在此简单记录以供參考。

    2.2.1 g++未安装

    checking for C++ compiler default output file name...
    configure: error: in `/home/config_server/tair/tb-common-utils/tbnet':
    configure: error: C++ compiler cannot create executables
    See `config.log' for more details.
    make: *** No targets specified and no makefile found. Stop.
    make: *** No rule to make target `install'. Stop.
    说明安装了gcc但未安装g++,而tair是用C++开发的,因此仅仅能用g++编译。通过过sudo yum install gcc-c++安装就可以。

    2.2.2 头文件路径错误

    In file included from channel.cpp:16: tbnet.h:39:19: error: tbsys.h: No such file or directory databuffer.h: In member function 'void tbnet::DataBuffer::expand(int)': databuffer.h:429: error: 'ERROR' was not declared in this scope databuffer.h:429: error: 'TBSYS_LOG' was not declared in this scope socket.h: At global scope: socket.h:191: error: 'tbsys' has not been declared socket.h:191: error: ISO C++ forbids declaration of 'CThreadMutex' with no type socket.h:191: error: expected ';' before '_dnsMutex' channelpool.h:85: error: 'tbsys' has not been declared channelpool.h:85: error: ISO C++ forbids declaration of 'CThreadMutex' with no type channelpool.h:85: error: expected ';' before '_mutex' channelpool.h:93: error: 'atomic_t' does not name a type channelpool.h:94: error: 'atomic_t' does not name a type connection.h:164: error: 'tbsys' has not been declared connection.h:164: error: ISO C++ forbids declaration of 'CThreadCond' with no type connection.h:164: error: expected ';' before '_outputCond' iocomponent.h:184: error: 'atomic_t' does not name a type iocomponent.h: In member function 'int tbnet::IOComponent::addRef()': iocomponent.h:108: error: '_refcount' was not declared in this scope iocomponent.h:108: error: 'atomic_add_return' was not declared in this scope iocomponent.h: In member function 'void tbnet::IOComponent::subRef()': iocomponent.h:115: error: '_refcount' was not declared in this scope iocomponent.h:115: error: 'atomic_dec' was not declared in this scope iocomponent.h: In member function 'int tbnet::IOComponent::getRef()': iocomponent.h:122: error: '_refcount' was not declared in this scope iocomponent.h:122: error: 'atomic_read' was not declared in this scope transport.h: At global scope: transport.h:23: error: 'tbsys' has not been declared transport.h:23: error: expected `{' before 'Runnable' transport.h:23: error: invalid function declaration packetqueuethread.h:28: error: 'tbsys' has not been declared packetqueuethread.h:28: error: expected `{' before 'CDefaultRunnable' packetqueuethread.h:28: error: invalid function declaration connectionmanager.h:93: error: 'tbsys' has not been declared connectionmanager.h:93: error: ISO C++ forbids declaration of 'CThreadMutex' with no type connectionmanager.h:93: error: expected ';' before '_mutex' make[1]: *** [channel.lo] Error 1 make[1]: Leaving directory `/home/tair/tair/tb-common-utils/tbnet/src' make: *** [install-recursive] Error 1
    have installed in ~/lib
    由于tbnet和tbsys在两个不同的文件夹,但它们的源代码文件中头文件的互相引用却没有加绝对或相对路径,将两个文件夹的源代码加入到C++环境变量中就可以。

    CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/home/tair/tair/tb-common-utils/tbsys/src:/home/tair/tair/tb-common-utils/tbnet/src
    export CPLUS_INCLUDE_PATH


    3. 部署配置

    tair的执行, 至少须要一个 config server 和一个 data server. 推荐使用两个 config server 多个data server的方式. 两个config server有主备之分.
    tair有三个配置文件。各自是对config server、data server及group信息的配置,在tair_bin安装文件夹下的etc文件夹下有这三个配置文件的样例,我们将其复制一下,成为我们须要的配置文件。

    cp configserver.conf.default configserver.conf
    cp dataserver.conf.default dataserver.conf
    cp group.conf.default group.conf

    我的部署环境:



    在配置之前。请查阅官网给出的配置文件字段详细解释,以下直接贴出我自己的配置并加以简单的说明。



    3.1 配置config server

    #
    # tair 2.3 --- configserver config
    #
    
    [public]
    config_server=10.10.7.144:51980
    config_server=10.10.7.144:51980
    
    [configserver]
    port=51980
    log_file=/home/dataserver1/tair_bin/logs/config.log
    pid_file=/home/dataserver1/tair_bin/logs/config.pid
    log_level=warn
    group_file=/home/dataserver1/tair_bin/etc/group.conf
    data_dir=/home/dataserver1/tair_bin/data/data
    dev_name=venet0:0
    注意事项:

    (1)首先须要配置config server的服务器地址和端口号,端口号能够默认,服务器地址改成自己的,有一主一备两台configserver,这里仅为測试使用就设置为一台了。

    (2)log_file/pid_file等的路径设置最好用绝对路径,默认的是相对路径,并且是不对的相对路径(没有返回上级文件夹)。因此这里须要改动。注意data文件和log文件非常重要,data文件必不可少。而log文件是部署出错后能给你详细的出错原因。

    (3)dev_name非常重要。须要设置为你自己当前网络接口的名称,默觉得eth0。这里我依据自己的网络情况进行了改动(ifconfig查看网络接口名称)。


    3.2 配置data server

    #
    #  tair 2.3 --- tairserver config 
    #
    [public]
    config_server=10.10.7.144:51980
    config_server=10.10.7.144:51980
    
    [tairserver]
    #
    #storage_engine:
    #
    # mdb 
    # kdb
    # ldb
    #
    storage_engine=ldb
    local_mode=0
    #
    #mdb_type:
    # mdb
    # mdb_shm
    #
    mdb_type=mdb_shm
    
    #
    # if you just run 1 tairserver on a computer, you may ignore this option.
    # if you want to run more than 1 tairserver on a computer, each tairserver must have their own "mdb_shm_path"
    #
    #
    mdb_shm_path=/mdb_shm_path01
    #tairserver listen port
    port=51910
    heartbeat_port=55910
    
    process_thread_num=16
    #
    #mdb size in MB
    #
    slab_mem_size=1024
    log_file=/home/dataserver1/tair_bin/logs/server.log
    pid_file=/home/dataserver1/tair_bin/logs/server.pid
    log_level=warn
    dev_name=venet0:0
    ulog_dir=/home/dataserver1/tair_bin/data/ulog
    ulog_file_number=3
    ulog_file_size=64
    check_expired_hour_range=2-4
    check_slab_hour_range=5-7
    dup_sync=1
    
    do_rsync=0
    # much resemble json format
    # one local cluster config and one or multi remote cluster config.
    # {local:[master_cs_addr,slave_cs_addr,group_name,timeout_ms,queue_limit],remote:[...],remote:[...]}
    rsync_conf={local:[10.0.0.1:5198,10.0.0.2:5198,group_local,2000,1000],remote:[10.0.1.1:5198,10.0.1.2:5198,group_remote,2000,3000]}
    # if same data can be updated in local and remote cluster, then we need care modify time to
    # reserve latest update when do rsync to each other.
    rsync_mtime_care=0
    # rsync data directory(retry_log/fail_log..)
    rsync_data_dir=/home/dataserver1/tair_bin/data/remote
    # max log file size to record failed rsync data, rotate to a new file when over the limit
    rsync_fail_log_size=30000000
    # whether do retry when rsync failed at first time
    rsync_do_retry=0
    # when doing retry,  size limit of retry log's memory use
    rsync_retry_log_mem_size=100000000
    
    [fdb]
    # in MB
    index_mmap_size=30
    cache_size=256
    bucket_size=10223
    free_block_pool_size=8
    data_dir=/home/dataserver1/tair_bin/data/fdb
    fdb_name=tair_fdb
    
    [kdb]
    # in byte
    map_size=10485760      # the size of the internal memory-mapped region
    bucket_size=1048583    # the number of buckets of the hash table
    record_align=128       # the power of the alignment of record size
    data_dir=/home/dataserver1/tair_bin/data/kdb      # the directory of kdb's data
    
    [ldb]
    #### ldb manager config
    ## data dir prefix, db path will be data/ldbxx, "xx" means db instance index.
    ## so if ldb_db_instance_count = 2, then leveldb will init in
    ## /data/ldb1/ldb/, /data/ldb2/ldb/. We can mount each disk to
    ## data/ldb1, data/ldb2, so we can init each instance on each disk.
    data_dir=/home/dataserver1/tair_bin/data/ldb
    ## leveldb instance count, buckets will be well-distributed to instances
    ldb_db_instance_count=1
    ## whether load backup version when startup.
    ## backup version may be created to maintain some db data of specifid version.
    ldb_load_backup_version=0
    ## whether support version strategy.
    ## if yes, put will do get operation to update existed items's meta info(version .etc),
    ## get unexist item is expensive for leveldb. set 0 to disable if nobody even care version stuff.
    ldb_db_version_care=1
    ## time range to compact for gc, 1-1 means do no compaction at all
    ldb_compact_gc_range = 3-6
    ## backgroud task check compact interval (s)
    ldb_check_compact_interval = 120
    ## use cache count, 0 means NOT use cache,`ldb_use_cache_count should NOT be larger
    ## than `ldb_db_instance_count, and better to be a factor of `ldb_db_instance_count.
    ## each cache mdb's config depends on mdb's config item(mdb_type, slab_mem_size, etc)
    ldb_use_cache_count=1
    ## cache stat can't report configserver, record stat locally, stat file size.
    ## file will be rotate when file size is over this.
    ldb_cache_stat_file_size=20971520
    ## migrate item batch size one time (1M)
    ldb_migrate_batch_size = 3145728
    ## migrate item batch count.
    ## real batch migrate items depends on the smaller size/count
    ldb_migrate_batch_count = 5000
    ## comparator_type bitcmp by default
    # ldb_comparator_type=numeric
    ## numeric comparator: special compare method for user_key sorting in order to reducing compact
    ## parameters for numeric compare. format: [meta][prefix][delimiter][number][suffix] 
    ## skip meta size in compare
    # ldb_userkey_skip_meta_size=2
    ## delimiter between prefix and number 
    # ldb_userkey_num_delimiter=:
    ####
    ## use blommfilter
    ldb_use_bloomfilter=1
    ## use mmap to speed up random acess file(sstable),may cost much memory
    ldb_use_mmap_random_access=0
    ## how many highest levels to limit compaction
    ldb_limit_compact_level_count=0
    ## limit compaction ratio: allow doing one compaction every ldb_limit_compact_interval
    ## 0 means limit all compaction
    ldb_limit_compact_count_interval=0
    ## limit compaction time interval
    ## 0 means limit all compaction
    ldb_limit_compact_time_interval=0
    ## limit compaction time range, start == end means doing limit the whole day.
    ldb_limit_compact_time_range=6-1
    ## limit delete obsolete files when finishing one compaction
    ldb_limit_delete_obsolete_file_interval=5
    ## whether trigger compaction by seek
    ldb_do_seek_compaction=0
    ## whether split mmt when compaction with user-define logic(bucket range, eg) 
    ldb_do_split_mmt_compaction=0
    
    #### following config effects on FastDump ####
    ## when ldb_db_instance_count > 1, bucket will be sharded to instance base on config strategy.
    ## current supported:
    ##  hash : just do integer hash to bucket number then module to instance, instance's balance may be
    ##         not perfect in small buckets set. same bucket will be sharded to same instance
    ##         all the time, so data will be reused even if buckets owned by server changed(maybe cluster has changed),
    ##  map  : handle to get better balance among all instances. same bucket may be sharded to different instance based
    ##         on different buckets set(data will be migrated among instances).
    ldb_bucket_index_to_instance_strategy=map
    ## bucket index can be updated. this is useful if the cluster wouldn't change once started
    ## even server down/up accidently.
    ldb_bucket_index_can_update=1
    ## strategy map will save bucket index statistics into file, this is the file's directory
    ldb_bucket_index_file_dir=/home/dataserver1/tair_bin/data/bindex
    ## memory usage for memtable sharded by bucket when batch-put(especially for FastDump)
    ldb_max_mem_usage_for_memtable=3221225472
    ####
    
    #### leveldb config (Warning: you should know what you're doing.)
    ## one leveldb instance max open files(actually table_cache_ capacity, consider as working set, see `ldb_table_cache_size)
    ldb_max_open_files=655
    ## whether return fail when occure fail when init/load db, and
    ## if true, read data when compactiong will verify checksum
    ldb_paranoid_check=0
    ## memtable size
    ldb_write_buffer_size=67108864
    ## sstable size
    ldb_target_file_size=8388608
    ## max file size in each level. level-n (n > 0): (n - 1) * 10 * ldb_base_level_size
    ldb_base_level_size=134217728
    ## sstable's block size
    # ldb_block_size=4096
    ## sstable cache size (override `ldb_max_open_files)
    ldb_table_cache_size=1073741824
    ##block cache size
    ldb_block_cache_size=16777216
    ## arena used by memtable, arena block size
    #ldb_arenablock_size=4096
    ## key is prefix-compressed period in block,
    ## this is period length(how many keys will be prefix-compressed period)
    # ldb_block_restart_interval=16
    ## specifid compression method (snappy only now)
    # ldb_compression=1
    ## compact when sstables count in level-0 is over this trigger
    ldb_l0_compaction_trigger=1
    ## write will slow down when sstables count in level-0 is over this trigger
    ## or sstables' filesize in level-0 is over trigger * ldb_write_buffer_size if ldb_l0_limit_write_with_count=0
    ldb_l0_slowdown_write_trigger=32
    ## write will stop(wait until trigger down)
    ldb_l0_stop_write_trigger=64
    ## when write memtable, max level to below maybe
    ldb_max_memcompact_level=3
    ## read verify checksum
    ldb_read_verify_checksums=0
    ## write sync log. (one write will sync log once, expensive)
    ldb_write_sync=0
    ## bits per key when use bloom filter
    #ldb_bloomfilter_bits_per_key=10
    ## filter data base logarithm. filterbasesize=1<<ldb_filter_base_logarithm
    #ldb_filter_base_logarithm=12                                   

    该配置文件内容非常多,红色标出来的是我改动的部分。其他的採用默认。当中:

    (1)config_server的配置与之前必须全然相同。

    (2)这里面的port和heartbeat_port是data server的端口号和心跳端口号,必须确保系统能给你使用这些端口号。一般默认的就可以。这里我改动是由于自己的Linux系统仅仅同意分配30000以后的端口号。依据自己情况改动。

    (3)data文件、log文件等非常重要,与前一样,最好用绝对路径


    3.3 配置group信息

    #group name
    [group_1]
    # data move is 1 means when some data serve down, the migrating will be start. 
    # default value is 0
    _data_move=0
    #_min_data_server_count: when data servers left in a group less than this value, config server will stop serve for this group
    #default value is copy count.
    _min_data_server_count=1
    #_plugIns_list=libStaticPlugIn.so
    _build_strategy=1 #1 normal 2 rack 
    _build_diff_ratio=0.6 #how much difference is allowd between different rack 
    # diff_ratio =  |data_sever_count_in_rack1 - data_server_count_in_rack2| / max (data_sever_count_in_rack1, data_server_count_in_rack2)
    # diff_ration must less than _build_diff_ratio
    _pos_mask=65535  # 65535 is 0xffff  this will be used to gernerate rack info. 64 bit serverId & _pos_mask is the rack info, 
    _copy_count=1    
    _bucket_number=1023
    # accept ds strategy. 1 means accept ds automatically
    _accept_strategy=1
    
    
    # data center A
    _server_list=10.10.7.146:51910
    #_server_list=192.168.1.2:5191
    #_server_list=192.168.1.3:5191
    #_server_list=192.168.1.4:5191
    
    
    # data center B
    #_server_list=192.168.2.1:5191
    #_server_list=192.168.2.2:5191
    #_server_list=192.168.2.3:5191
    #_server_list=192.168.2.4:5191
    
    
    #quota info
    _areaCapacity_list=0,1124000;
    

    这个文件我仅仅配置了data server列表,我仅仅有一个dataserver,因此仅仅需配置一个。


    3.4 启动集群

    在完毕安装配置之后, 能够启动集群了.  启动的时候须要先启动data server 然后再启动cofnig server.  假设是为已有的集群加入dataserver则能够先启动dataserver进程然后再改动gruop.conf,假设你先改动group.conf再启动进程,那么须要执行touch group.conf;在scripts文件夹下有一个脚本 tair.sh 能够用来帮助启动 tair.sh start_ds 用来启动data server.  tair.sh start_cs 用来启动config server.  这个脚本比較简单, 它要求配置文件放在固定位置, 採用固定名称.  使用者能够通过执行安装文件夹下的bin下的 tair_server (data server) 和 tair_cfg_svr(config server) 来启动集群.


    进入tair_bin文件夹后,按顺序启动:

    sudo sbin/tair_server -f etc/dataserver.conf     # 在dataserver端启动
    sudo sbin/tair_cfg_svr -f etc/configserver.conf   # 在config server端启动
    
    执行启动命令后,在两端通过ps aux | grep tair查看是否启动了。这里启动起来仅仅是第一步,还须要測试看是否真的启动成功。通过以下命令測试:

    sudo sbin/tairclient -c 10.10.7.144:51980 -g group_1
    TAIR> put k1 v1       
    put: success
    TAIR> put k2 v2
    put: success
    TAIR> get k2
    KEY: k2, LEN: 2
    当中10.10.7.144:51980是config server IP:PORT,group_1是group name,在group.conf里配置的。


    3.4 部署过程中的错误记录

    假设启动不成功或測试put/get时出现故障,那么须要查看config server端的logs/config.log和data server端的logs/server.log日志文件,里面会有详细的报错信息。


    3.4.1  Too many open files 

    [2014-07-09 10:37:24.863119] ERROR start (stat_manager.cpp:30) [139767832377088] open file [/home/dataserver1/tair_bin/data/ldb1/ldb/tair_db_001013.stat] failed: Too many open files
    [2014-07-09 10:37:24.863132] ERROR start (stat_manager.cpp:30) [139767832377088] open file [/home/dataserver1/tair_bin/data/ldb1/ldb/tair_db_001014.stat] failed: Too many open files
    [2014-07-09 10:37:24.863145] ERROR start (stat_manager.cpp:30) [139767832377088] open file [/home/dataserver1/tair_bin/data/ldb1/ldb/tair_db_001015.stat] failed: Too many open files
    [2014-07-09 10:37:24.863154] ERROR start (stat_manager.cpp:30) [139767832377088] open file [/home/dataserver1/tair_bin/data/ldb1/ldb/tair_db_001016.stat] failed: Too many open files
    [2014-07-09 10:37:24.863162] ERROR start (stat_manager.cpp:30) [139767832377088] open file [/home/dataserver1/tair_bin/data/ldb1/ldb/tair_db_001017.stat] failed: Too many open files
    由于我的存储引擎选择的是ldb,而ldb有一个配置ldb_max_open_files=65535,即默认最多能打开的文件个数是65535个,可是我的系统不同意,能够通过“ulimit -n”查看系统执行程序中打开的最多文件个数。一般为1024个,远远小于65535,这时有两个办法来解决,一是改动ldb_max_open_files的值,使其小于1024。二是改动系统最多同意打开文件个数(以下的參考资料有提供改动的方法),由于我是測试使用,因此这里直接改动了ldb_max_open_files的值。


    3.4.2 data server问题


    dataserver没配置好会报各种错误,以下列举一些我遇到的错误:


    问题1:

    TAIR> put abc a 
    put: unknow 
    TAIR> put a 11 
    put: unknow 
    TAIR> put abc 33 
    put: unknow 
    TAIR> get a 
    get failed: data not exists.

    问题2:

    ERROR wakeup_wait_object (../../src/common/wait_object.hpp:302) [140627106383616] [3] packet is null
    这些都是dataserver開始启动起来了。可是使用put/get时报错。然后dataserver立即down掉的情况,这时候就要依据log查看详细报错信息。改动错误的配置。

    还有以下这种报错信息:

    [2014-07-09 09:08:11.646430] ERROR rebuild (group_info.cpp:879) [139740048353024] can not get enough data servers. need 1 lef 0
    这是config server在启动时找不到data server。也就是data server必须要先启动成功后才干启动config server。


    3.4.3 端口问题

    start tair_cfg_srv listen port 5199 error

    有时候使用默认的端口号也不一定行。须要依据系统限制进行设置,比方我的系统环境仅仅能执行普通用户使用30000以上的端口号。因此这里我就不能使用默认端口号了,改下就可以。


    4. Javaclient測试

    Tair是一个分布式的key/value存储系统。数据往往存储在多个数据节点上。

    client须要决定数据存储的详细节点,然后才干完毕详细的操作。

    Tair的client通过和configserver交互获取这部分信息。configserver会维护一张表,这张表包括hash值与存储其对应数据的节点的对比关系。

    client在启动时,须要先和configserver通信,获取这张对比表。

    在获取到对比表后,client便能够開始提供服务。client会依据请求的key的hash值,查找对比表中负责该数据的数据节点,然后通过和数据节点通信完毕用户的请求。


    Tair当前支持Java和c++语言的client。Javaclient已有对应的实现(可从这里下载到对应的jar包),我们直接使用封装的接口操作就可以,但C++client眼下还没看到实现版本号(须要自己实现)。

    这里以简单的Javaclient为例进行client測试。


    4.1 依赖jar包

    Java測试程序除了须要封装好的tair相关jar包之外,还须要tair依赖的一些jar包,详细的有以下几个(不一定是这个版本号号):

    commons-logging-1.1.3.jar
    slf4j-api-1.7.7.jar
    slf4j-log4j12-1.7.7.jar
    log4j-1.2.17.jar
    mina-core-1.1.7.jar
    tair-client-2.3.1.jar

    4.2 Javaclient程序


    首先请參考Tair用户指南里面的关于javaclient的接口说明,以下直接给出演示样例,非常easy理解。


    package tair.client;
    
    import java.util.ArrayList;
    import java.util.List;
    
    import com.taobao.tair.DataEntry;
    import com.taobao.tair.Result;
    import com.taobao.tair.ResultCode;
    import com.taobao.tair.impl.DefaultTairManager;
    
    /**
     * @author WangJianmin
     * @date 2014-7-9
     * @description Java-client test application for tair.
     *
     */
    public class TairClientTest {
    
    	public static void main(String[] args) {
    
    		// 创建config server列表
    		List<String> confServers = new ArrayList<String>();
    		confServers.add("10.10.7.144:51980"); 
    	//	confServers.add("10.10.7.144:51980"); // 可选
    
    		// 创建client实例
    		DefaultTairManager tairManager = new DefaultTairManager();
    		tairManager.setConfigServerList(confServers);
    
    		// 设置组名
    		tairManager.setGroupName("group_1");
    		// 初始化client
    		tairManager.init();
    
    		// put 10 items
    		for (int i = 0; i < 10; i++) {
    			// 第一个參数是namespace,第二个是key,第三是value,第四个是版本号。第五个是有效时间
    			ResultCode result = tairManager.put(0, "k" + i, "v" + i, 0, 10);
    			System.out.println("put k" + i + ":" + result.isSuccess());
    			if (!result.isSuccess())
    				break;
    		}
    
    		// get one
    		// 第一个參数是namespce。第二个是key
    		Result<DataEntry> result = tairManager.get(0, "k3");
    		System.out.println("get:" + result.isSuccess());
    		if (result.isSuccess()) {
    			DataEntry entry = result.getValue();
    			if (entry != null) {
    				// 数据存在
    				System.out.println("value is " + entry.getValue().toString());
    			} else {
    				// 数据不存在
    				System.out.println("this key doesn't exist.");
    			}
    		} else {
    			// 异常处理
    			System.out.println(result.getRc().getMessage());
    		}
    
    	}
    
    }

    执行结果:

    log4j:WARN No appenders could be found for logger (com.taobao.tair.impl.ConfigServer).
    log4j:WARN Please initialize the log4j system properly.
    log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
    put k0:true
    put k1:true
    put k2:true
    put k3:true
    put k4:true
    put k5:true
    put k6:true
    put k7:true
    put k8:true
    put k9:true
    get:true
    value is v3

    注意事项:測试假设不是在config server或data server上进行,那么一定要确保測试端系统与config server和data server能互相通信,即ping通。否则有可能会报以下这种错误:

    Exception in thread "main" java.lang.RuntimeException: init config failed
     at com.taobao.tair.impl.DefaultTairManager.init(DefaultTairManager.java:80)
     at tair.client.TairClientTest.main(TairClientTest.java:27)

    我已将演示样例程序、须要的jar包及Makefile文件(我在Linux系统下測试,未用Eclipse跑程序)打包,须要的能够从这里下载。



    5. 參考资料


    1. TAIR home page

    2. Tair用户指南

    3. Too many open files 问题的解决


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  • 原文地址:https://www.cnblogs.com/lcchuguo/p/5079514.html
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