To launch Solr, run: bin/solr start -e cloud on Unix or MacOS; bin\solr.cmd start -e cloud on Windows.

启动Solr，运行:bin/ Solr在Unix或MacOS上启动-e云;本\ solr。cmd在Windows上启动-e云。

This will start an interactive session that will start two Solr "servers" on your machine. This command has an option to run without prompting you for input (-noprompt), but we want to modify two of the defaults so we won’t use that option now.

这将启动一个交互式会话，在您的机器上启动两个Solr“服务器”。这个命令可以在没有提示输入的情况下运行(-noprompt)，但是我们希望修改两个默认值，这样我们就不会使用这个选项了。

The first prompt asks how many nodes we want to run. Note the [2] at the end of the last line; that is the default number of nodes. Two is what we want for this example, so you can simply press enter.

第一个提示询问我们想要运行多少节点。注意最后一行末尾的[2];这是节点的默认数量。两个是我们想要的例子，所以你可以按回车。

This will be the port that the first node runs on. Unless you know you have something else running on port 8983 on your machine, accept this default option also by pressing enter. If something is already using that port, you will be asked to choose another port.

这将是第一个节点运行的端口。除非您知道您的机器上有其他运行在8983端口上的东西，否则请按enter键接受这个默认选项。如果某些东西已经在使用该端口，那么您将被要求选择另一个端口。

This is the port the second node will run on. Again, unless you know you have something else running on port 8983 on your machine, accept this default option also by pressing enter. If something is already using that port, you will be asked to choose another port.

这是第二个节点将运行的端口。同样，除非您知道在您的机器上有其他运行在8983端口上的东西，否则也要按enter键接受这个默认选项。如果某些东西已经在使用该端口，那么您将被要求选择另一个端口。

Solr will now initialize itself and start running on those two nodes. The script will print the commands it uses for your reference.

Solr现在将自己初始化并在这两个节点上运行。该脚本将打印它用于您的引用的命令。

Notice that two instances of Solr have started on two nodes. Because we are starting in SolrCloud mode, and did not define any details about an external ZooKeeper cluster, Solr launches its own ZooKeeper and connects both nodes to it.

注意，Solr的两个实例已经在两个节点上启动。因为我们从SolrCloud模式开始，并没有定义任何关于外部ZooKeeper集群的细节，Solr启动了自己的ZooKeeper，并将两个节点连接到它。

After startup is complete, you’ll be prompted to create a collection to use for indexing data.

启动完成后，将提示您创建用于索引数据的集合。

Here’s the first place where we’ll deviate from the default options. This tutorial will ask you to index some sample data included with Solr, called the "techproducts" data. Let’s name our collection "techproducts" so it’s easy to differentiate from other collections we’ll create later. Enter techproducts at the prompt and hit enter.

这是第一个我们会偏离默认选项的地方。本教程将要求您对包含Solr的一些示例数据进行索引，称为“techproducts”数据。让我们将我们的集合命名为“techproducts”，以便与稍后创建的其他集合区分开来。在提示符处输入techproducts并按回车键。

This is asking how many shards you want to split your index into across the two nodes. Choosing "2" (the default) means we will split the index relatively evenly across both nodes, which is a good way to start. Accept the default by hitting enter.

这是问你想要将索引分割成两个节点有多少个碎片。选择“2”(默认)意味着我们将在两个节点上相对均匀地分割索引，这是一个很好的开始。按回车键接受默认值。

A replica is a copy of the index that’s used for failover (see also the Solr Glossary definition). Again, the default of "2" is fine to start with here also, so accept the default by hitting enter.

副本是用于故障转移的索引的副本(参见Solr术语表定义)。同样，“2”的默认值也可以从这里开始，因此通过按回车来接受默认值。

We’ve reached another point where we will deviate from the default option. Solr has two sample sets of configuration files (called a configSet) available out-of-the-box.

我们已经到达了另一个我们将偏离默认选项的点。Solr有两个配置文件的示例集(称为configSet)。

A collection must have a configSet, which at a minimum includes the two main configuration files for Solr: the schema file (named either managed-schema or schema.xml), and solrconfig.xml. The question here is which configSet you would like to start with. The _default is a bare-bones option, but note there’s one whose name includes "techproducts", the same as we named our collection. This configSet is specifically designed to support the sample data we want to use, so enter sample_techproducts_configs at the prompt and hit enter.

集合必须有一个configSet，它至少包含Solr的两个主要配置文件:架构文件(命名为manageschema或schema.xml)和solrconfig.xml。这里的问题是您希望从哪个configSet开始。_default是一个简单的选项，但请注意，其中有一个名称包含“techproducts”，就像我们命名我们的集合一样。这个configSet是专门用来支持我们想要使用的示例数据的，所以在提示符处输入sample_techproducts_configs，然后按回车键。

At this point, Solr will create the collection and again output to the screen the commands it issues.

此时，Solr将创建集合，并再次将其输出到屏幕上。

Congratulations! Solr is ready for data!

恭喜你!Solr已经准备好数据了!

You can see that Solr is running by launching the Solr Admin UI in your web browser: http://localhost:8983/solr/. This is the main starting point for administering Solr.

通过在web浏览器中启动Solr管理UI，可以看到Solr正在运行:http://localhost:8983/ Solr /。这是管理Solr的主要出发点。

Solr will now be running two "nodes", one on port 7574 and one on port 8983. There is one collection created automatically, techproducts, a two shard collection, each with two replicas.

Solr现在将运行两个“节点”，一个在7574端口上，一个在8983端口上。有一个集合自动创建，techproducts，一个两个shard集合，每个都有两个副本。

The Cloud tab in the Admin UI diagrams the collection nicely:

管理界面中的Cloud选项卡很好地显示了集合:

Your Solr server is up and running, but it doesn’t contain any data yet, so we can’t do any queries.

您的Solr服务器已经启动并运行，但是它还没有包含任何数据，因此我们不能执行任何查询。

Solr includes the bin/post tool in order to facilitate indexing various types of documents easily. We’ll use this tool for the indexing examples below.

Solr包括bin/post工具，以便方便地索引各种类型的文档。我们将使用这个工具来索引下面的索引示例。

You’ll need a command shell to run some of the following examples, rooted in the Solr install directory; the shell from where you launched Solr works just fine.

您需要一个命令shell来运行以下示例，这些示例源于Solr安装目录;你发射Solr的外壳很好用。

The data we will index is in the example/exampledocs directory. The documents are in a mix of document formats (JSON, CSV, etc.), and fortunately we can index them all at once:

我们将索引的数据位于示例/exampledocs目录中。这些文档混合了文档格式(JSON、CSV等)，幸运的是，我们可以同时将它们进行索引:

You should see output similar to the following:

您应该会看到类似如下的输出:

Congratulations again! You have data in your Solr!

再次恭喜你!你的Solr有数据!

Now we’re ready to start searching.

现在我们准备开始搜索了。

Solr can be queried via REST clients, curl, wget, Chrome POSTMAN, etc., as well as via native clients available for many programming languages.

Solr可以通过REST客户端、curl、wget、Chrome POSTMAN等进行查询，也可以通过许多编程语言的本地客户机进行查询。

The Solr Admin UI includes a query builder interface via the Query tab for the techproducts collection (at http://localhost:8983/solr/#/techproducts/query). If you click the Execute Query button without changing anything in the form, you’ll get 10 documents in JSON format:

Solr管理用户界面包括一个查询生成器接口，通过查询选项卡(在http://localhost:8983/ Solr /#/techproducts/查询)中查询。如果您单击Execute Query按钮而不更改表单中的任何内容，您将获得10个JSON格式的文档:

The URL sent by the Admin UI to Solr is shown in light grey near the top right of the above screenshot. If you click on it, your browser will show you the raw response.

管理员UI发送给Solr的URL在上面截图的右上角附近显示为浅灰色。如果您点击它，您的浏览器将显示原始响应。

To use curl, give the same URL shown in your browser in quotes on the command line:

要使用curl，请在命令行中引用浏览器中的相同URL:

curl "http://localhost:8983/solr/techproducts/select?indent=on&q=*:*"

curl http://localhost:8983 / solr / techproducts /选择?缩进=对于q = *:*”

What’s happening here is that we are using Solr’s query parameter (q) with a special syntax that requests all documents in the index (*:*). All of the documents are not returned to us, however, because of the default for a parameter called rows, which you can see in the form is 10. You can change the parameter in the UI or in the defaults if you wish.

这里发生的情况是，我们使用Solr的查询参数(q)，并使用特殊语法请求索引中的所有文档(*:*)。但是，所有的文档都没有返回给我们，因为默认的参数是行，您可以在表单中看到的是10。如果您愿意，您可以更改UI中的参数或默认设置。

Solr has very powerful search options, and this tutorial won’t be able to cover all of them. But we can cover some of the most common types of queries.

Solr有非常强大的搜索选项，而本教程将无法涵盖所有这些选项。但是我们可以讨论一些最常见的查询类型。

At this point, you’ve seen how Solr can index data and have done some basic queries. You can choose now to continue to the next example which will introduce more Solr concepts, such as faceting results and managing your schema, or you can strike out on your own.

在这一点上，您已经看到了Solr如何索引数据并完成了一些基本的查询。现在，您可以选择继续下一个示例，它将引入更多的Solr概念，比如faceting结果和管理模式，或者您可以自己动手。

If you decide not to continue with this tutorial, the data we’ve indexed so far is likely of little value to you. You can delete your installation and start over, or you can use the bin/solr script we started out with to delete this collection:

如果您决定不继续使用本教程，那么到目前为止我们索引的数据对您来说可能没什么价值。您可以删除您的安装并重新开始，或者您可以使用我们开始时使用的bin/solr脚本删除这个集合:

bin/solr delete -c techproducts

bin / solr删除- c techproducts

And then create a new collection:

然后创建一个新的集合:

bin/solr create -c <yourCollection> -s 2 -rf 2

bin/solr创建-c -s 2 -rf 2。

To stop both of the Solr nodes we started, issue the command:

为了停止我们开始的Solr节点，发出命令:

bin/solr stop -all

bin / solr停止-

For more information on start/stop and collection options with bin/solr, see Solr Control Script Reference.

有关启动/停止和收集选项与bin/solr的更多信息，请参阅solr控制脚本参考。

Did you stop Solr after the last exercise? No? Then go ahead to the next section.

你在最后一次锻炼后停止了Solr吗?没有?然后继续下一节。

If you did, though, and need to restart Solr, issue these commands:

但是，如果您确实需要重新启动Solr，请发出以下命令:

./bin/solr start -c -p 8983 -s example/cloud/node1/solr

./bin/solr开始-c -p 8983 -s示例/cloud/node1/solr。

This starts the first node. When it’s done start the second node, and tell it how to connect to to ZooKeeper:

这将启动第一个节点。完成第二个节点后，告诉它如何连接到ZooKeeper:

./bin/solr start -c -p 7574 -s example/cloud/node2/solr -z localhost:9983

/bin/solr启动-c -p 7574 -s example/cloud/node2/solr -z localhost:9983。

We’re going to use a whole new data set in this exercise, so it would be better to have a new collection instead of trying to reuse the one we had before.

我们将在这个练习中使用一个全新的数据集，所以最好有一个新的集合，而不是试图重用以前的那个。

One reason for this is we’re going to use a feature in Solr called "field guessing", where Solr attempts to guess what type of data is in a field while it’s indexing it. It also automatically creates new fields in the schema for new fields that appear in incoming documents. This mode is called "Schemaless". We’ll see the benefits and limitations of this approach to help you decide how and where to use it in your real application.

其中一个原因是，我们将在Solr中使用一个名为“字段猜测”的特性，Solr试图猜测在索引它时，字段中的数据类型是什么。它还会自动为出现在传入文档中的新字段创建新字段。这种模式被称为“无模式”。我们将看到这种方法的好处和局限性，以帮助您决定如何在实际应用程序中使用它。

When you initially started Solr in the first exercise, we had a choice of a configSet to use. The one we chose had a schema that was pre-defined for the data we later indexed. This time, we’re going to use a configSet that has a very minimal schema and let Solr figure out from the data what fields to add.

当您在第一次练习中开始使用Solr时，我们可以选择要使用的configSet。我们选择的那个有一个模式，它是为我们后来索引的数据预先定义的。这一次，我们将使用一个具有非常小的模式的configSet，并让Solr从数据中找出要添加的字段。

The data you’re going to index is related to movies, so start by creating a collection named "films" that uses the _default configSet:

您将要索引的数据与电影有关，所以首先创建一个名为“movies”的集合，它使用_default configSet:

bin/solr create -c films -s 2 -rf 2

bin/solr创建-c薄膜-s 2 -rf 2。

Whoa, wait. We didn’t specify a configSet! That’s fine, the _default is appropriately named, since it’s the default and is used if you don’t specify one at all.

哇,等。我们没有指定configSet!这很好，_default是适当命名的，因为它是默认的，如果您不指定一个，它将被使用。

We did, however, set two parameters -s and -rf. Those are the number of shards to split the collection across (2) and how many replicas to create (2). This is equivalent to the options we had during the interactive example from the first exercise.

然而，我们确实设置了两个参数-s和-rf。这些是通过(2)来分割集合的碎片数量，以及创建了多少个副本(2)，这等价于我们在第一次练习的交互式示例中所拥有的选项。

You should see output like:

您应该会看到如下输出:

The first thing the command printed was a warning about not using this configSet in production. That’s due to some of the limitations we’ll cover shortly.

命令打印的第一件事是警告不要在生产中使用这个configSet。这是由于我们不久将讨论的一些限制。

Otherwise, though, the collection should be created. If we go to the Admin UI at http://localhost:8983/solr/#/films/collection-overview we should see the overview screen.

否则，应该创建集合。如果我们在http://localhost:8983/solr/#/films/collection-overview中访问Admin UI，我们将看到overview屏幕。

The films data we will index is located in the example/films directory of your installation. It comes in three formats: JSON, XML and CSV. Pick one of the formats and index it into the "films" collection (in each example, one command is for Unix/MacOS and the other is for Windows):

我们将索引的电影数据位于您安装的示例/电影目录中。它有三种格式:JSON、XML和CSV。选择其中一种格式并将其索引到“电影”集合中(在每个示例中，一个命令用于Unix/MacOS，另一个用于Windows):

Each command includes these main parameters:

每个命令包括以下主要参数:

Note the CSV command includes extra parameters. This is to ensure multi-valued entries in the "genre" and "directed_by" columns are split by the pipe (|) character, used in this file as a separator. Telling Solr to split these columns this way will ensure proper indexing of the data.

注意，CSV命令包含额外的参数。这是为了确保“类型”和“directed_by”列中的多值条目被管道(|)字符分割，在该文件中用作分隔符。告诉Solr以这种方式拆分这些列将确保正确地索引数据。

Each command will produce output similar to the below seen while indexing JSON:

每个命令将产生类似于下面所见的输出，同时索引JSON:

Hooray!

万岁!

If you go to the Query screen in the Admin UI for films (http://localhost:8983/solr/#/films/query) and hit Execute Query you should see 1100 results, with the first 10 returned to the screen.

如果您进入到影片的管理UI中的查询屏幕(http://localhost:8983/solr/#/ movie / Query)，然后点击执行查询，您应该会看到1100个结果，前10个返回到屏幕。

Let’s do a query to see if the "catchall" field worked properly. Enter "comedy" in the q box and hit Execute Query again. You should see get 417 results. Feel free to play around with other searches before we move on to faceting.

让我们来做一个查询，看看“所有的”字段是否正常工作。在q框中输入“comedy”，再次点击执行查询。你应该会得到417个结果。在我们继续进行faceting之前，你可以自由地玩其他的搜索。

One of Solr’s most popular features is faceting. Faceting allows the search results to be arranged into subsets (or buckets, or categories), providing a count for each subset. There are several types of faceting: field values, numeric and date ranges, pivots (decision tree), and arbitrary query faceting.

Solr最受欢迎的功能之一是faceting。Faceting允许将搜索结果安排为子集(或桶或类别)，为每个子集提供计数。有几种类型的faceting:字段值、数字和日期范围、轴心(决策树)和任意查询面板。

In this exercise, we learned a little bit more about how Solr organizes data in the indexes, and how to work with the Schema API to manipulate the schema file. We also learned a bit about facets in Solr, including range facets and pivot facets. In both of these things, we’ve only scratched the surface of the available options. If you can dream it, it might be possible!

在这个练习中，我们进一步了解了Solr如何在索引中组织数据，以及如何使用模式API来操作模式文件。我们还了解了Solr的一些方面，包括范围方面和主方面。在这两种情况下，我们只触及了可用选项的表面。如果你能梦到它，它可能是可能的!

Like our previous exercise, this data may not be relevant to your needs. We can clean up our work by deleting the collection. To do that, issue this command at the command line:

和我们之前的练习一样，这些数据可能与您的需求无关。我们可以通过删除集合来清理工作。为此，在命令行中发出该命令:

bin/solr delete -c films

bin / solr删除- c的电影

Before you get started, create a new collection, named whatever you’d like. In this example, the collection will be named "localDocs"; replace that name with whatever name you choose if you want to.

在您开始之前，创建一个新的集合，命名您想要的任何东西。在本例中，集合将被命名为“localDocs”;如果您愿意，可以用您选择的名称替换该名称。

./bin/solr create -c localDocs -s 2 -rf 2

./bin/solr创建-c localDocs -s 2 -rf 2。

Again, as we saw from Exercise 2 above, this will use the _default configSet and all the schemaless features it provides. As we noted previously, this may cause problems when we index our data. You may need to iterate on indexing a few times before you get the schema right.

同样，正如我们在上面的练习2中看到的，这将使用_default configSet和它提供的所有无模式特性。如前所述，当我们对数据进行索引时，这可能会导致问题。在获得模式正确之前，您可能需要对索引进行多次迭代。

Solr has lots of ways to index data. Choose one of the approaches below and try it out with your system:

Solr有很多方法来索引数据。选择下面的一种方法，并尝试用你的系统:

You may notice that even if you index content in this tutorial more than once, it does not duplicate the results found. This is because the example Solr schema (a file named either managed-schema or schema.xml) specifies a uniqueKey field called id. Whenever you POST commands to Solr to add a document with the same value for the uniqueKey as an existing document, it automatically replaces it for you.

您可能会注意到，即使在本教程中索引内容不止一次，它也不会复制所发现的结果。这是因为示例Solr模式(一个名为manageschema或schema.xml的文件)指定了一个名为id的uniqueKey字段，每当您将命令发布到Solr时，它就会自动替换为您的文档，该文档的值与现有文档的值相同。

You can see that that has happened by looking at the values for numDocs and maxDoc in the core-specific Overview section of the Solr Admin UI.

您可以看到，在Solr管理UI的核心特定的概述部分中，查看numDocs和maxDoc的值就可以看到这一点。

numDocs represents the number of searchable documents in the index (and will be larger than the number of XML, JSON, or CSV files since some files contained more than one document). The maxDoc value may be larger as the maxDoc count includes logically deleted documents that have not yet been physically removed from the index. You can re-post the sample files over and over again as much as you want and numDocs will never increase, because the new documents will constantly be replacing the old.

numDocs表示索引中可搜索文档的数量(由于某些文件包含多个文档，因此它的数量将大于XML、JSON或CSV文件的数量)。maxDoc值可能会更大，因为maxDoc计数包括逻辑删除的文档，这些文档还没有从索引中删除。您可以一次又一次地重新发布示例文件，而numDocs将永远不会增加，因为新文档将不断地替换旧文档。

Go ahead and edit any of the existing example data files, change some of the data, and re-run the PostTool (bin/post). You’ll see your changes reflected in subsequent searches.

继续编辑任何现有的示例数据文件，更改一些数据，并重新运行后工具(bin/post)。您将看到在后续的搜索中所反映的更改。

If you need to iterate a few times to get your schema right, you may want to delete documents to clear out the collection and try again. Note, however, that merely removing documents doesn’t change the underlying field definitions. Essentially, this will allow you to reindex your data after making changes to fields for your needs.

如果您需要迭代几次来获得您的模式正确，您可能需要删除文档以清除集合并再次尝试。但是，请注意，仅仅删除文档并不会改变基础字段定义。从本质上说，这将允许您在为您的需要做出更改之后重新索引您的数据。

You can delete data by POSTing a delete command to the update URL and specifying the value of the document’s unique key field, or a query that matches multiple documents (be careful with that one!). We can use bin/post to delete documents also if we structure the request properly.

您可以通过将delete命令发布到更新URL并指定文档的惟一键字段的值，或者匹配多个文档的查询(请小心使用那个!)来删除数据。我们也可以使用bin/post来删除文档，如果我们正确地构造了请求。

Execute the following command to delete a specific document:

执行以下命令删除特定文档:

bin/post -c localDocs -d "<delete><id>SP2514N</id></delete>"

bin/post -c localDocs -d " "

To delete all documents, you can use "delete-by-query" command like:

要删除所有文档，您可以使用“delete-by-query”命令:

bin/post -c localDocs -d "<delete><query>*:*</query></delete>"

bin/post -c localDocs -d " <查询> *:* "

You can also modify the above to only delete documents that match a specific query.

您还可以修改上面的内容，只删除匹配特定查询的文档。

At this point, you’re ready to start working on your own.

此时，您已经准备好开始自己的工作了。

Jump ahead to the overall wrap up when you’re ready to stop Solr and remove all the examples you worked with and start fresh.

当您准备停止Solr并删除所有与您一起工作的示例并重新启动时，请跳到整个包上。