Blackberry PIM 为什么联系人创建速度这么慢？

Question

以下代码是我从黑莓论坛获取的，在 8500 系列设备上使用 BB PIM API 花费了近 26 分钟来创建 2000 个随机联系人。有谁知道为什么需要这么长时间或者如何提高地址簿中联系人创建的性能？

public static void testContactCreation() {
    ContactList contacts = null;
    try {
      contacts = (ContactList) PIM.getInstance().openPIMList(PIM.CONTACT_LIST, PIM.READ_WRITE);
    } catch (PIMException e) {
      // An error occurred
      return;
    }

    String arrSzCities[] = {"Birmingham", "Walsall", "Wolverhampton", "Banbury", "Bromsgrove", "Lichfield", "Balsall Heath",
        "West Bromich", "Smethwick", "Scott Arms", "Perry Barr", "Small Heath", "Acocks Green", "Great Barr",
        "Harborne", "Selly Oak", "Newtown", "Hockley", "Nuneaton", "Stafford", "Stoke", "Sandwell", "Brierly Hill",
        "Longbridge", "Sutton Coldfield", "Tamworth", "Coventry", "Rugby", "Hall Green", "Olton", "Dorridge",
        "Lapworth", "Shirley", "Wythall", "Warwick", "Dudley", "Barnt Green", "Tile Hill", "Berkswell", "Canley",
        "Yardley", "Yardley Wood", "Bordesley Green", "Cosely", "Four Oaks", "Erdington", "Aston", "Duddington"};

    String arrSzCountries[] = {"England", "Wales", "Scotland", "Northern Ireland", "Eire", "Spain", "France", "Italy",
        "Monaco", "Switzerland", "Austria", "Germany", "Lapland", "Estonia", "Hungary", "Slovakia", "Slovenia",
        "Czech Republic", "Latvia", "Holland", "Belgium", "Luxembourg", "Iceland", "Finland", "Denmark", "Norway"};

    String arrSzFamilyNames[] = {"SMITH", "JOHNSON", "WILLIAMS", "BROWN", "JONES", "MILLER", "DAVIS", "GARCIA", "RODRIGUEZ",
        "WILSON", "MARTINEZ", "ANDERSON", "TAYLOR", "THOMAS", "HERNANDEZ", "MOORE", "MARTIN", "JACKSON", "THOMPSON",
        "WHITE", "LOPEZ", "LEE", "GONZALEZ", "HARRIS", "CLARK", "LEWIS", "ROBINSON", "WALKER", "PEREZ", "HALL", "YOUNG",
        "ALLEN", "SANCHEZ", "WRIGHT", "KING", "SCOTT", "GREEN", "BAKER", "ADAMS", "NELSON", "HILL", "RAMIREZ", "CAMPBELL",
        "MITCHELL", "ROBERTS", "CARTER", "PHILLIPS", "EVANS", "TURNER", "TORRES", "PARKER", "COLLINS", "EDWARDS", "STEWART",
        "FLORES", "MORRIS", "NGUYEN", "MURPHY", "RIVERA", "COOK", "ROGERS", "MORGAN", "PETERSON", "COOPER", "REED",
        "BAILEY", "BELL", "GOMEZ", "KELLY", "HOWARD", "WARD", "COX", "DIAZ", "RICHARDSON", "WOOD", "WATSON", "BROOKS",
        "BENNETT", "GRAY", "JAMES", "REYES", "CRUZ", "HUGHES", "PRICE", "MYERS", "LONG", "FOSTER", "SANDERS", "ROSS",
        "MORALES", "POWELL", "SULLIVAN", "RUSSELL", "ORTIZ", "JENKINS", "GUTIERREZ", "PERRY", "BUTLER", "BARNES", "FISHER",
        "HENDERSON", "COLEMAN", "SIMMONS", "PATTERSON", "JORDAN", "REYNOLDS", "HAMILTON", "GRAHAM", "KIM", "GONZALES",
        "ALEXANDER", "RAMOS", "WALLACE", "GRIFFIN", "WEST",
        "COLE", "HAYES", "CHAVEZ", "GIBSON", "BRYANT", "ELLIS", "STEVENS", "MURRAY", "FORD", "MARSHALL", "OWENS",
        "MCDONALD", "HARRISON", "RUIZ", "KENNEDY", "WELLS", "ALVAREZ", "WOODS", "MENDOZA", "CASTILLO", "OLSON",
        "WEBB", "WASHINGTON", "TUCKER", "FREEMAN", "BURNS", "HENRY", "VASQUEZ", "SNYDER", "SIMPSON", "CRAWFORD", "JIMENEZ",
        "PORTER", "MASON", "SHAW", "GORDON", "WAGNER", "HUNTER", "ROMERO", "HICKS", "DIXON", "HUNT", "PALMER", "ROBERTSON",
        "BLACK", "HOLMES", "STONE", "MEYER", "BOYD", "MILLS", "WARREN", "FOX", "ROSE", "RICE", "MORENO", "SCHMIDT", "PATEL",
        "FERGUSON", "NICHOLS", "HERRERA", "MEDINA", "RYAN", "FERNANDEZ", "WEAVER", "DANIELS", "STEPHENS", "GARDNER", "PAYNE",
        "KELLEY", "DUNN", "PIERCE", "ARNOLD", "TRAN", "SPENCER", "PETERS", "HAWKINS", "GRANT", "HANSEN", "CASTRO", "HOFFMAN",
        "HART", "ELLIOTT", "CUNNINGHAM", "KNIGHT"};

    String arrSzFirstNames[] = {"MARY", "PATRICIA", "LINDA", "BARBARA", "ELIZABETH", "JENNIFER", "MARIA", "SUSAN", "MARGARET",
        "DOROTHY", "LISA", "NANCY", "KAREN", "BETTY", "HELEN", "SANDRA", "DONNA", "CAROL", "RUTH", "SHARON", "MICHELLE",
        "LAURA", "SARAH", "KIMBERLY", "DEBORAH", "JESSICA", "SHIRLEY", "CYNTHIA", "ANGELA", "MELISSA", "BRENDA", "AMY",
        "ANNA", "REBECCA", "VIRGINIA", "KATHLEEN", "PAMELA", "MARTHA", "DEBRA", "AMANDA", "STEPHANIE", "CAROLYN", "CHRISTINE",
        "MARIE", "JANET", "CATHERINE", "FRANCES", "ANN", "JOYCE", "DIANE", "ALICE", "JULIE", "HEATHER", "TERESA", "DORIS",
        "GLORIA", "EVELYN", "JEAN", "CHERYL", "MILDRED", "GERALD", "KEITH", "SAMUEL",
        "JAMES", "JOHN", "ROBERT", "MICHAEL", "WILLIAM", "DAVID", "RICHARD", "CHARLES", "JOSEPH", "THOMAS", "CHRISTOPHER",
        "DANIEL", "PAUL", "MARK", "DONALD", "GEORGE", "KENNETH", "STEVEN", "EDWARD", "BRIAN", "RONALD", "ANTHONY",
        "KEVIN", "JASON", "MATTHEW", "GARY", "TIMOTHY", "JOSE", "LARRY", "JEFFREY", "FRANK", "SCOTT", "ERIC", "STEPHEN",
        "ANDREW", "RAYMOND", "GREGORY", "JOSHUA", "JERRY", "DENNIS", "WALTER", "PATRICK", "PETER", "HAROLD", "DOUGLAS",
        "HENRY", "CARL", "ARTHUR", "RYAN", "ROGER", "JOE", "JUAN", "JACK", "ALBERT", "JONATHAN", "JUSTIN", "TERRY"};

    String arrSzEmailProviders[] = {"google", "yahoo", "wanadoo", "freemail", "aol", "yellowmellow", "redmail", "bt", "tiscali",
        "naims", "bulldog", "demon", "virgin", "sky", "orange", "vodaphone", "o2", "three", "britishgas", "npower",
        "britishtelecom", "royalmail", "parcelforce", "dhl", "usps", "ford", "rover", "fiat", "seat", "volvo", "bmw",
        "landrover", "jaguar", "warburtons", "kingsmill", "hovis", "walkers", "cadburys", "ironbru", "redbull", "jura"};

    String arrSzAlphas[] = {"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T",
        "U", "V", "W", "X", "Y", "Z"};

    String arrSzStreetSuffixes[] = {"Road", "Street", "Crescent", "Close", "Way", "Mews", "Common", "Alley", "Common", "Grove",
        "Place", "Mill", "Manor", "Lane", "March", "Hill", "Park", "Passage", "Path", "Row", "Square", "Terrace", "View"};

    String arrSzStreetNames[] = {"High", "Station", "Main", "Park", "Church", "London", "Victoria", "Albert", "Green", "Manor",
        "Church", "Park", "Queens", "New", "Grange Road", "Kings Road", "North", "West", "South", "East", "Windsor",
        "Highfield", "Mill", "Alexander", "York", "St. John's", "Broad", "Springfield", "George", "Manchester", "Richmond",
        "School", "Stanley", "Chester", "Aghaloo", "Picadilly", "**bleep**", "Wooburn", "Crazies", "Valentia", "Luton", "Croydon",
        "Rookery", "Coronation", "Dawlish", "Tiverton", "Dartmouth", "Hubert", "Bristol", "Arley", "Grange", "Dale",
        "Serpentine", "Bournebrook", "University", "Holly", "Kitchener", "Millner", "Westminster", "Cherrington", "Gristhorpe",
        "Kensington", "Cartland", "Horatio", "Ethelbert", "Hornblower", "Hanky Panky", "Bewdley", "Acorn", "Berry", "Moor",
        "Brent", "Mungo Jerry", "Highbury", "Howell", "Tenbury", "Peacock", "Hartswell"};

    Random generator = new Random(1628434416);
    for (int i = 0; i < 2000; i++) {
      Contact contact = contacts.createContact();
      String[] addr = new String[contacts.stringArraySize(Contact.ADDR)];
      String[] szName = new String[contacts.stringArraySize(Contact.NAME)];

      // Family Name
      if (contacts.isSupportedArrayElement(Contact.NAME, Contact.NAME_FAMILY))
        szName[Contact.NAME_FAMILY] = arrSzFamilyNames[generator.nextInt(arrSzFamilyNames.length)];
      // First Name
      if (contacts.isSupportedArrayElement(Contact.NAME, Contact.NAME_GIVEN))
        szName[Contact.NAME_GIVEN] = arrSzFirstNames[generator.nextInt(arrSzFirstNames.length)];
      // Add name to contact
      contact.addStringArray(Contact.NAME, PIMItem.ATTR_NONE, szName);

      // Postcode (UK format)
      if (contacts.isSupportedArrayElement(Contact.ADDR, Contact.ADDR_POSTALCODE))
        addr[Contact.ADDR_POSTALCODE] =
            arrSzAlphas[generator.nextInt(arrSzAlphas.length)] + arrSzAlphas[generator.nextInt(arrSzAlphas.length)]
                + Integer.toString(generator.nextInt(99)) + Integer.toString(generator.nextInt(9)) + " "
                + arrSzAlphas[generator.nextInt(arrSzAlphas.length)] + arrSzAlphas[generator.nextInt(arrSzAlphas.length)];

      // Street and number
      if (contacts.isSupportedArrayElement(Contact.ADDR, Contact.ADDR_STREET))
        addr[Contact.ADDR_STREET] = Integer.toString(generator.nextInt(999)) + " "
            + arrSzStreetNames[generator.nextInt(arrSzStreetNames.length)]
            + " " + arrSzStreetSuffixes[generator.nextInt(arrSzStreetSuffixes.length)];

      // Locality
      if (contacts.isSupportedArrayElement(Contact.ADDR, Contact.ADDR_LOCALITY))
        addr[Contact.ADDR_LOCALITY] = arrSzCities[generator.nextInt(arrSzCities.length)];
      // Country
      if (contacts.isSupportedArrayElement(Contact.ADDR, Contact.ADDR_COUNTRY))
        addr[Contact.ADDR_COUNTRY] = arrSzCountries[generator.nextInt(arrSzCountries.length)];
      if (contacts.isSupportedField(Contact.ADDR))
        contact.addStringArray(Contact.ADDR, Contact.ATTR_HOME, addr);

      // Email address
      if (contacts.isSupportedField(Contact.EMAIL)) {
        contact.addString(Contact.EMAIL, Contact.ATTR_HOME | Contact.ATTR_PREFERRED, szName[Contact.NAME_GIVEN] + "." + szName[Contact.NAME_FAMILY] + "@" + arrSzEmailProviders[generator.nextInt(arrSzEmailProviders.length)] + ".com");
      }

      // Telephone numbers (work and home)
      String szTelHome = "";
      String szTelWork = "";
      for (int j = 0; j < 7; j++) {
        String szDigit = Integer.toString(generator.nextInt(9));
        szTelHome = szTelHome + szDigit;
        szTelWork = szDigit + szTelWork;
      }
      if (contacts.isSupportedField(Contact.TEL)) {
        contact.addString(Contact.TEL, Contact.ATTR_HOME, szTelHome);
      }
      if (contacts.isSupportedField(Contact.TEL)) {
        contact.addString(Contact.TEL, Contact.ATTR_WORK, szTelHome);
      }

      try {
        contact.commit();
      } catch (PIMException e) {
        // An error occured

      }
    }

    try {
      contacts.close();
    } catch (PIMException e) {
    }
  }

Answer 1

嗯.. 26 分钟 2000 次接触意味着每次接触 780 毫秒。

1）。您能检查一下 contact.commit(); 不是这里最耗时的调用吗？你可以用这样的方法相对准确地做到这一点：

long timeSpentOnCommits; // defined somewhere at a top level
...
long start = System.currentTimeMillis();
contact.commit();
long taken = System.currentTimeMillis() - start;
timeSpentOnCommits += taken;
...
// then after the all contacts have been added you can get 
// an average commit time:
long timePerOneCommit = timeSpentOnCommits / 2000;
// view the got value in a `Dialog` or some logging

如果它花费了大部分时间，那么我认为你无法改进它。

2）。另一个想法 - 在“for”循环中，您创建了太多的 String 对象，因此这迫使操作系统过于频繁地调用垃圾收集（这是一件耗时的事情）。

请改用 StringBuffer。因此，不要

addr[Contact.ADDR_POSTALCODE] =
    arrSzAlphas[generator.nextInt(arrSzAlphas.length)]
    + arrSzAlphas[generator.nextInt(arrSzAlphas.length)]
    + Integer.toString(generator.nextInt(99)) 
    + Integer.toString(generator.nextInt(9)) 
    + " "
    + arrSzAlphas[generator.nextInt(arrSzAlphas.length)]
    + arrSzAlphas[generator.nextInt(arrSzAlphas.length)];

使用这个：

addr[Contact.ADDR_POSTALCODE] = new StringBuffer()
    .append(arrSzAlphas[generator.nextInt(arrSzAlphas.length)])
    .append(arrSzAlphas[generator.nextInt(arrSzAlphas.length)])
    .append(generator.nextInt(99))
    .append(generator.nextInt(9))
    .append(' ')
    .append(arrSzAlphas[generator.nextInt(arrSzAlphas.length)])
    .append(arrSzAlphas[generator.nextInt(arrSzAlphas.length)])
    .toString();

请注意，StringBuffer.append() 对于任何原始类型都是重载的，因此它可以直接接受整数和字符，而无需转换为 String > 对象。

相同的方法也适用于电话号码生成。

3）。通过从“for”循环中提取所有重复调用，可以稍微提高速度。例如，只需计算调用 arrSzAlphas.length 的次数（2000 次迭代 * 每次迭代 4 次调用 = 8000 次！），而您可以在循环之前调用一次并将其存储在从循环中可见的最终局部变量。对于一组 contacts.isSupportedXXXX 调用也是如此 - 您不应在所有迭代中一遍又一遍地调用它，只需在循环开始之前调用一次即可。