refactor: 1.0.0 API

README.md

@@ -102,27 +102,25 @@ CeresDB is a high-performance, distributed, schema-less, cloud native time-serie
 <dependency>
   <groupId>io.ceresdb</groupId>
   <artifactId>ceresdb-all</artifactId>
-  <version>0.1.0</version>
+  <version>1.0.0-alpha</version>
 </dependency>
 ```
 
 ## Init CeresDB client
 ```java
-// CeresDB options
-final CeresDBOptions opts = CeresDBOptions.newBuilder("127.0.0.1", 8831) //// CeresDB default grpc port 8831
-        .tenant("public", "sub_test", "test_token") // tenant info
+final CeresDBOptions opts = CeresDBOptions.newBuilder("127.0.0.1", 8831, DIRECT) // CeresDB default grpc port 8831，use DIRECT RouteMode
+        .database("public") // use database for client, can be overridden by the RequestContext in request
         // maximum retry times when write fails
         // (only some error codes will be retried, such as the routing table failure)
         .writeMaxRetries(1)
         // maximum retry times when read fails
         // (only some error codes will be retried, such as the routing table failure)
-        .readMaxRetries(1)
-        .build();
+        .readMaxRetries(1).build();
 
 final CeresDBClient client = new CeresDBClient();
-        if (!client.init(this.opts)) {
+if (!client.init(opts)) {
         throw new IllegalStateException("Fail to start CeresDBClient");
-        }
+}
 ```
 For more configuration options, see [configuration](docs/configuration.md)
 
@@ -132,105 +130,87 @@ CeresDB is a Schema-less time-series database, so creating table schema ahead of
 The following table creation statement（using the SQL API included in SDK ）shows all field types supported by CeresDB：
 
 ```java
-SqlResult result = client.management().executeSql("CREATE TABLE MY_FIRST_TABL(" +
-    "ts TIMESTAMP NOT NULL," +
-    "c1 STRING TAG NOT NULL," +
-    "c2 STRING TAG NOT NULL," +
-    "c3 DOUBLE NULL," +
-    "c4 STRING NULL," +
-    "c5 INT64 NULL," +
-    "c6 FLOAT NULL," +
-    "c7 INT32 NULL," +
-    "c8 INT16 NULL," +
-    "c9 INT8 NULL," +
-    "c10 BOOLEAN NULL,"
-    "c11 UINT64 NULL,"
-    "c12 UINT32 NULL,"
-    "c13 UINT16 NULL,"
-    "c14 UINT8 NULL,"
-    "c15 TIMESTAMP NULL,"
-    "c16 VARBINARY NULL,"
-    "TIMESTAMP KEY(ts)) ENGINE=Analytic"
-);
+// Create table manually, creating table schema ahead of data ingestion is not required
+String createTableSql = "CREATE TABLE IF NOT EXISTS machine_table(" +                                                                                              "ts TIMESTAMP NOT NULL," + //
+        "ts TIMESTAMP NOT NULL," +
+        "city STRING TAG NOT NULL," +
+        "ip STRING TAG NOT NULL," +
+        "cpu DOUBLE NULL," +
+        "mem DOUBLE NULL," +
+        "TIMESTAMP KEY(ts)" + // timestamp column must be specified
+        ") ENGINE=Analytic";
+
+Result<SqlQueryOk, Err> createResult = client.sqlQuery(new SqlQueryRequest(createTableSql)).get();
+if (!createResult.isOk()) {
+        throw new IllegalStateException("Fail to create table");
+}
 ```
 
-## Write data example
+## How to build write data
 ```java
-final long t0 = System.currentTimeMillis();
-final long t1 = t0 + 1000;
-final long t2 = t1 + 1000;
-final Rows data = Series.newBuilder("machine_metric")
-        .tag("city", "Singapore")
-        .tag("ip", "127.0.0.1")
-        .toRowsBuilder()
-        // codes below organizes 3 lines data (3 timestamps) for the `cpu` and `mem` column, this will just transport once through network. CeresDB encourage practices like this, because the SDK could use efficient compression algorithm to reduce network traffic and also be friendly to the sever side.
-        .field(t0, "cpu", FieldValue.withDouble(0.23)) // first row, first column
-        .field(t0, "mem", FieldValue.withDouble(0.55)) // first row, second column
-        .field(t1, "cpu", FieldValue.withDouble(0.25)) // second row, first column
-        .field(t1, "mem", FieldValue.withDouble(0.56)) // second row, second column
-        .field(t2, "cpu", FieldValue.withDouble(0.21)) // third row, first column
-        .field(t2, "mem", FieldValue.withDouble(0.52)) // third row, second column
+final Point point = Point.newPointBuilder("machine_table")
+        .setTimestamp(t0)
+        .addTag("city", "Singapore")
+        .addTag("ip", "10.0.0.1")
+        .addField("cpu", Value.withDouble(0.23))
+        .addField("mem", Value.withDouble(0.55))
         .build();
+```
 
-final CompletableFuture<Result<WriteOk, Err>> wf = client.write(data);
+## Write data example
+```java
+final CompletableFuture<Result<WriteOk, Err>> wf = client.write(new WriteRequest(pointList));
 // here the `future.get` is just for demonstration, a better async programming practice would be using the CompletableFuture API
-final Result<WriteOk, Err> wr = wf.get();
-
-        Assert.assertTrue(wr.isOk());
-        Assert.assertEquals(3, wr.getOk().getSuccess());
-        // `Result` class referenced the Rust language practice, provides rich functions (such as mapXXX, andThen) transforming the result value to improve programming efficiency. You can refer to the API docs for detail usage.
-        Assert.assertEquals(3, wr.mapOr(0, WriteOk::getSuccess).intValue());
-        Assert.assertEquals(0, wr.getOk().getFailed());
-        Assert.assertEquals(0, wr.mapOr(-1, WriteOk::getFailed).intValue());
+final Result<WriteOk, Err> writeResult = wf.get();
+Assert.assertTrue(writeResult.isOk());
+// `Result` class referenced the Rust language practice, provides rich functions (such as mapXXX, andThen) transforming the result value to improve programming efficiency. You can refer to the API docs for detail usage.
+Assert.assertEquals(3, writeResult.getOk().getSuccess());
+Assert.assertEquals(3, writeResult.mapOr(0, WriteOk::getSuccess).intValue());
+Assert.assertEquals(0, writeResult.mapOr(-1, WriteOk::getFailed).intValue());
 ```
 See [write](docs/write.md)
 
 ## Query data example
 ```java
-final QueryRequest queryRequest = QueryRequest.newBuilder()
-        .forMetrics("machine_metric") // table name is optional. If not provided, SQL parser will parse the `ql` to get the table name and do the routing automaticly
-        .ql("select timestamp, cpu, mem from machine_metric") //
+final SqlQueryRequest queryRequest = SqlQueryRequest.newBuilder()
+        .forTables("machine_table") // table name is optional. If not provided, SQL parser will parse the `sql` to get the table name and do the routing automaticly
+        .sql("select * from machine_table where ts = %d", t0) //
         .build();
-final CompletableFuture<Result<QueryOk, Err>> qf = client.query(queryRequest);
+final CompletableFuture<Result<SqlQueryOk, Err>> qf = client.sqlQuery(queryRequest);
 // here the `future.get` is just for demonstration, a better async programming practice would be using the CompletableFuture API
-final Result<QueryOk, Err> qr = qf.get();
+final Result<SqlQueryOk, Err> queryResult = qf.get();
+
+Assert.assertTrue(queryResult.isOk());
 
-        Assert.assertTrue(qr.isOk());
+final SqlQueryOk queryOk = queryResult.getOk();
+Assert.assertEquals(1, queryOk.getRowCount());
 
-final QueryOk queryOk = qr.getOk();
+// get rows as list
+final List<Row> rows = queryOk.getRowList();
 
-final List<Record> records = queryOk.mapToRecord().collect(Collectors.toList())
+// get rows as stream
+final Stream<Row> rowStream = queryOk.stream();
+rowStream.forEach(row -> System.out.println(row.toString()));
 ```
 See [read](docs/read.md)
 
 ## stream write/read Example
 CeresDB support streaming writing and reading，suitable for large-scale data reading and writing。
 ```java
-final Calendar time = Calendar.getInstance();
-final StreamWriteBuf<Rows, WriteOk> writeBuf = client.streamWrite("machine_metric");
-        for (int i = 0; i < 1000; i++) {
-        time.add(Calendar.MILLISECOND, 1);
-        Collection<Rows> rows = new ArrayList<>();
-        final long t0 = System.currentTimeMillis();
-        final long t1 = t0 + 1000;
-        final long t2 = t1 + 1000;
-        final Rows data = Series.newBuilder("machine_metric").tag("city", "Singapore").tag("ip", "127.0.0.1")
-        .toRowsBuilder()
-        .field(t0, "cpu", FieldValue.withDouble(0.23)) 
-        .field(t0, "mem", FieldValue.withDouble(0.55)) 
-        .field(t1, "cpu", FieldValue.withDouble(0.25))
-        .field(t1, "mem", FieldValue.withDouble(0.56))
-        .field(t2, "cpu", FieldValue.withDouble(0.21))
-        .field(t2, "mem", FieldValue.withDouble(0.52))
+final StreamWriteBuf<Point, WriteOk> writeBuf = client.streamWrite("machine_table");
+for (int i = 0; i < 1000; i++) {
+    final Point point = Point.newPointBuilder("machine_table")
+        .setTimestamp(timestamp)
+        .addTag("city", "Beijing")
+        .addTag("ip", "10.0.0.3")
+        .addField("cpu", Value.withDouble(0.42))
+        .addField("mem", Value.withDouble(0.67))
         .build();
-        rows.add(data);
-        writeBuf.writeAndFlush(data);
-        }
-final CompletableFuture<WriteOk> writeOk = writeBuf.completed();
-        Assert.assertEquals(1000, writeOk.join().getSuccess());
+        writeBuf.writeAndFlush(Arrays.asList(point));
+        timestamp = timestamp+1;
+}
 
-final QueryRequest req = QueryRequest.newBuilder().ql("select * from %s", "machine_metric").build();
-final Iterator<Record> it = client.blockingStreamQuery(req, 3, TimeUnit.SECONDS);
+final CompletableFuture<WriteOk> writeOk = writeBuf.completed();
 ```
 See [streaming](docs/streaming.md)